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From Access Control to Outcome Control: Securing AI Agents with Check Point and Google Cloud

AI is changing how software works. Applications no longer just process requests. They reason, make decisions, and take action. AI agents now retrieve data, invoke tools, and execute workflows across systems in real time. That shift introduces a new kind of risk. Because in an agentic world, security is no longer just about who has access. It’s about what AI is allowed to do. A new control point for agentic systems in Google Cloud Google Cloud’s Gemini Enterprise Agent Platform provides a centralized control point for agentic systems enabling identity, access, policy enforcement, and observability across how agents operate. This […]

The post From Access Control to Outcome Control: Securing AI Agents with Check Point and Google Cloud appeared first on Check Point Blog.

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AI Finds Every Gap: How Many Can Your Network Survive?

Anthropic’s reported development of Claude Mythos signals a shift: AI is compressing attack timelines by accelerating vulnerability discovery, exploit development, and multi-step attack planning. More broadly, AI is increasing the speed and scale of attacks across malware, phishing, and vulnerabilities. Attackers can now run these vectors in parallel, reducing time to compromise and increasing exposure. AI also enables more targeted phishing, faster malware iteration, and rapid vulnerability discovery, exposing gaps in detection and exposure management earlier and requiring prevention-first controls and real-time detection. To see how these challenges translate into real-world performance, and how leading security vendors handle them under […]

The post AI Finds Every Gap: How Many Can Your Network Survive? appeared first on Check Point Blog.

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National Vulnerability Database (NVD) Shifts to Selective Enrichment as CVE Volume Surges

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National Vulnerability Database (NVD) Shifts to Selective Enrichment as CVE Volume Surges

In this post, we examine what NVD’s shift to selective enrichment means for vulnerability workflows and how security teams can maintain visibility and prioritization at scale.

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April 17, 2026

The National Vulnerability Database (NVD) is changing how it processes and enriches vulnerability data in response to sustained growth in CVE submissions.

Under a new model announced by the National Institute of Standards and Technology, NVD will no longer enrich every CVE. Instead, enrichment efforts will focus on a defined subset, including vulnerabilities in the CISA KEV catalog, software used by the federal government, and software designated as critical.

All other CVEs will remain in the database without additional context unless specifically requested.

Rising disclosure volumes are placing pressure on public vulnerability infrastructure, and it has direct implications for how security teams consume and act on vulnerability data.

What Changed in NVD’s Operating Model

For years, NVD aimed to provide consistent enrichment across all CVEs, including severity scoring, affected product data, and supporting context for prioritization.

That approach has not been sustainable since late 2023.

In 2025, Flashpoint tracked 44,509 disclosed vulnerabilities, 14,593 of which had publicly available exploits (and 1,944 more with proof-of-concepts). 

CVE submissions increased by 263% between 2020 and 2025, with 2026 already tracking higher year-over-year. Even with increased throughput, NVD has not been able to keep pace.

Under the updated model:

  • CVEs meeting prioritization criteria will be enriched on an accelerated timeline
  • CVEs outside those criteria will be labeled and left without enrichment
  • Re-analysis of modified CVEs will occur selectively
  • Separate NVD severity scoring will no longer be applied by default

This introduces a significant structural change in how vulnerability data is published and maintained.

The Impact on Vulnerability Workflows

Many security programs rely on NVD enrichment to operationalize CVE data. That enrichment provides the context needed to evaluate risk and determine remediation priorities.

With enrichment applied selectively, teams will encounter a growing number of CVEs that include:

  • Limited or no severity scoring
  • Incomplete product and version data
  • Minimal context on exploitability or impact
  • No CPE strings that allow for programmatic consumption of data

At the same time, disclosure volume continues to rise, and exploitation timelines remain compressed. This creates a gap between what is disclosed and what can be acted on efficiently.

Security teams will need to account for:

  • Larger backlogs of CVEs without actionable context
  • Increased manual effort to evaluate relevance and risk
  • Greater variability in data quality across sources

These changes affect vulnerability management, threat intelligence, and security operations workflows simultaneously.

Prioritization Criteria Will Not Capture the Full Risk Landscape

NVD’s updated model focuses enrichment on a defined set of criteria, including known exploited vulnerabilities and software relevant to federal systems.

These categories represent important segments of risk, but they do not encompass the full set of vulnerabilities that organizations encounter in practice.

Modern environments include:

  • Open-source dependencies
  • SaaS platforms and APIs
  • Cloud infrastructure and services
  • Third-party and partner integrations

Many vulnerabilities affecting these environments fall outside formal prioritization frameworks or lack immediate classification within public datasets. As a result, security teams will continue to face exposure from vulnerabilities that are:

  • Actively exploited but not yet included in prioritized lists
  • Missing complete metadata or enrichment
  • Relevant to their environment but not captured by federal-centric criteria

Vulnerability Intelligence Requires Broader Coverage and Deeper Context

As public enrichment becomes more selective, organizations will rely more heavily on alternative sources to maintain visibility and context.

Effective vulnerability intelligence requires:

  • Coverage across CVE and non-CVE vulnerabilities
  • Continuous tracking of exploitation activity and adversary usage
  • Context on exploit maturity, and remediation
  • Consistent enrichment that can be integrated into operational workflows

This level of detail supports faster and more accurate decision-making in environments where both volume and speed are increasing.

Flashpoint’s vulnerability intelligence model is built to address these requirements, with a dataset that includes over 7,000 known exploited vulnerabilities and ongoing analyst-driven enrichment across global sources.

What Security Teams Should Do Next

This shift in NVD operations does not change the need to track CVEs. It changes how that data can be used. Security teams should evaluate how their current workflows depend on:

  • NVD enrichment for prioritization
  • CVSS scoring as a primary decision input
  • Completeness of public vulnerability data

From there, teams can take steps to strengthen resilience:

  • Incorporate sources of vulnerability intelligence that cover CVE and more
  • Align prioritization to exploitation activity and environmental relevance
  • Validate coverage across software, cloud, and third-party dependencies
  • Ensure that enrichment gaps do not delay remediation decisions

A Structural Shift in Vulnerability Data

For many teams, NVD has been a default source of vulnerability context. This change makes clear that its role is narrowing at a time when disclosure volume and prioritization demands are increasing.

At the same time, the role of vulnerability intelligence is expanding.

Security teams need access to data that supports prioritization, not just identification. They need consistent enrichment, faster turnaround, broader coverage, and context tied to real-world activity. As disclosure volumes continue to grow, those requirements become more central to how organizations manage risk.

Flashpoint’s Vulnerability Intelligence provides this level of coverage and context, with analyst-driven enrichment, global visibility across CVE and non-CVE vulnerabilities, and a dataset that includes over 7,000 known exploited vulnerabilities.

Request a demo to see how Flashpoint helps security teams prioritize and act on vulnerability risk with greater precision and confidence.

Begin your free trial today.

The post National Vulnerability Database (NVD) Shifts to Selective Enrichment as CVE Volume Surges appeared first on Flashpoint.

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Flashpoint Surpasses Cataloging 7,000 Known Exploited Vulnerabilities as Disclosure Volume Accelerates

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Flashpoint Surpasses Cataloging 7,000 Known Exploited Vulnerabilities as Disclosure Volume Accelerates

In this post we explore Flashpoint’s latest milestone of surpassing cataloging 7,000 known exploited vulnerabilities and what this means for security teams.

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April 15, 2026

Flashpoint Vulnerability Intelligence has surpassed cataloging 7,000 known exploited vulnerabilities, surpassing another major milestone as vulnerability disclosures accelerate across the global attack surface.

In 2025, Flashpoint tracked 44,509 disclosed vulnerabilities, a pace that continues to accelerate into 2026. Of those, 14,593 had publicly available exploits (1,944 more with proof-of-concepts), giving threat actors immediate pathways to weaponization.

This pace is shaping how exploitation unfolds, with high-impact vulnerabilities being operationalized within hours or days, particularly when they affect widely deployed technologies or core infrastructure.

Security teams are operating within this compressed environment every day. They are reviewing more findings across open-source software, commercial applications, cloud environments, and third-party dependencies, while working within tighter timelines to assess impact and take action.

Flashpoint’s latest milestone of surpassing 7,000 known exploited vulnerabilities (KEVs) cataloged reflects that reality. It highlights how vulnerability management programs are evolving toward prioritization as a core capability, with a focus on vulnerabilities tied to active exploitation and real-world risk.

What The 7,000+ KEV Milestone Means for You

Security teams are operating in a high-volume environment. Vulnerabilities are disclosed continuously across open-source software, commercial applications, cloud environments, and third-party dependencies. At the same time, advancements in automation and code analysis are increasing the rate at which new findings are surfaced.

Each of these findings enters an already crowded workflow. Teams are expected to determine relevance, urgency, and impact quickly, often with limited context. This is where risk-based decision making becomes essential.

Flashpoint tracks hundreds of thousands of vulnerabilities across thousands of sources. Within that dataset, a much smaller percentage shows confirmed exploitation activity. That concentration of risk informs how effective programs allocate time and resources.

Crossing the 7,000+ KEV milestone goes beyond scale to provide greater precision, deeper context, and stronger confidence in how teams prioritize and act on the most critical vulnerabilities.

  • Validated threats: Each KEV entry reflects observed exploitation in the wild by threat actors, including APT groups, cybercriminal operations, ransomware presence, and automated botnets.
  • Exploit-aware prioritization: In reality, only a small percentage of tracked vulnerabilities drive real-world incidents. FP KEV provides visibility into that subset so teams can focus remediation efforts where they have immediate impact.
  • Human-curated intelligence: Every entry is reviewed, validated, and enriched by analysts, with context on exploit maturity, adversary usage, and remediation pathways when available.

This level of clarity allows teams to move faster without sacrificing accuracy. It supports vulnerability management programs that are built around real-world attacker behavior and aligned to current risk.

How Public Vulnerability Data Fits Into the Picture

Public vulnerability catalogs remain useful reference points for tracking disclosures and confirmed exploitation. The CISA Known Exploited Vulnerabilities catalog, for example, gives security teams a curated view into a limited set of vulnerabilities that have been exploited in the wild that impact U.S. government stakeholders.

For many organizations, though, that level of visibility is not enough.

Public catalogs capture only part of the picture. They tend to reflect a narrower slice of exploitation activity, with less detail on how vulnerabilities are being used, which actors are leveraging them, and what defenders should do next. They also rely heavily on CVE-based tracking, leaving gaps around non-CVE exposures and other vulnerabilities that still carry operational risk.

Flashpoint’s FP KEV and Vulnerability Intelligence provide a broader and more actionable view. The advantage is visible in both scale and depth. Of the 7,000 known exploited vulnerabilities in FP KEV, over 800 are missing from CVE. That expanded coverage is paired with the context security teams need to prioritize effectively, including exploit maturity, adversary mapping, affected product detail, and remediation guidance.

DimensionPublic KEV CatalogsFlashpoint FP KEV
ScopeVaries by provider, with coverage dependent on available sources and methodologyGlobal, cross-industry coverage
CoverageCVE-based trackingCVE and non-CVE vulnerabilities
ContextLimited enrichmentExploit maturity, adversary mapping, remediation
Update ModelPeriodic updatesContinuously updated with analyst input

This is what separates a reference list from an operational dataset. Teams need vulnerability intelligence that supports triage, remediation, reporting, and broader risk reduction efforts. Wider visibility and deeper context make that possible.

The Critical Role of Human-Curated Intelligence

Vulnerability data originates from a wide range of sources with varying levels of completeness and accuracy.

Flashpoint’s intelligence model includes analyst validation to ensure consistency and depth across the dataset.

This process includes:

  • Reviewing disclosures across public and private sources
  • Validating exploit availability and usage
  • Enriching entries with technical and operational context

Analyst input supports:

  • Accurate classification of vulnerabilities
  • Clear understanding of exploitation pathways
  • Timely updates as activity evolves

Supporting Decision-Making Across Teams

Vulnerability intelligence feeds multiple functions across an organization. Teams use this data to align technical actions with current threat activity.

Common use cases include:

  • Vulnerability management: Align patching priorities with active exploitation trends.
  • Threat intelligence: Map vulnerabilities to threat actor campaigns and observed behaviors.
  • Security operations: Tune detection based on known exploit techniques.
  • Executive reporting: Communicate risk posture using data tied to real-world activity.

Each of these functions relies on consistent, enriched intelligence to maintain alignment.

Proactively Address Vulnerability Risk

Vulnerability discovery continues to expand across software ecosystems, infrastructure, and identity layers.

Security teams require a clear understanding of which issues are relevant to their environment at any given time.

Flashpoint provides primary source intelligence that supports this need through:

  • Continuous monitoring of vulnerability disclosures and exploitation
  • Analyst-driven validation and enrichment
  • Integration-ready data for operational workflows

This approach enables teams to maintain focus, allocate resources effectively, and respond to risk based on current threat activity. Request a demo and learn more today.

Begin your free trial today.

The post Flashpoint Surpasses Cataloging 7,000 Known Exploited Vulnerabilities as Disclosure Volume Accelerates appeared first on Flashpoint.

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Spotting cyberthreats: a guide for blind and low-vision users | Kaspersky official blog

In 2023, Tim Utzig, a blind student from Baltimore, lost a thousand dollars to a laptop scam on X. Tim had been a long-time follower of a well-known sports journalist. When that journalist’s account started posting about a “charity sale” of brand-new MacBook Pros, Tim jumped at the chance to get a deal on a laptop he needed for his studies. After a few quick messages, he sent over the money.

Unfortunately, the journalist’s account had been hacked, and Tim’s cash went straight to scammers. The red flags were strictly visual: the page had been flagged as “temporarily restricted”, and both the bio and the Following list had changed. However, Tim’s screen reader — the software that converts on-screen text and graphics into speech — didn’t announce any of those warnings.

Screen readers allow blind users to navigate the digital world like everyone else. However, this community remains uniquely vulnerable. Even for sighted users, spotting a fake website is a challenge; for someone with a visual impairment, it’s an even steeper uphill battle.

Beyond screen readers, there are specialized mobile apps and services designed to assist the blind and low-vision community, with Be My Eyes being one of the most popular. The app connects users with sighted volunteers via a live video call to tackle everyday tasks — like setting an oven dial or locating an object on a desk. Be My Eyes also features integrated AI that can scan and narrate text or identify objects in the user’s environment.

But can these tools go beyond daily chores? Can they actually flag a phishing attempt or catch the hidden fine print when someone is opening a bank account?

Today we explore the specific online hurdles visually impaired users face, when it makes sense to lean on human or virtual assistants, and how to stay secure when using these types of services.

Common cyberthreats facing the blind and low-vision community

To start, let’s clarify the difference between these two groups. Low-vision users still rely on their remaining sight, even though their visual function is significantly reduced. To navigate digital interfaces, they often use screen magnifiers, extra-large fonts, and high-contrast settings. For them, phishing sites and emails are particularly dangerous. It’s easy to miss intentional typos — known as typosquatting — in a domain name or email address, such as the recent example of rnicrosoft{.}com.

Blind users navigate primarily by sound, using screen readers and specific touch gestures. Interestingly, though, unlike those with low vision, blind users are more likely to spot a phishing site using a screen reader: as the software reads the URL aloud, the user will hear that something is off. However, if a service — whether legitimate or malicious — isn’t fully compatible with screen readers, the risk of falling victim to a scam increases. This is exactly what happened to Tim Utzig.

It’s important to remember that screen magnifiers and readers are basic accessibility tools. They’re designed to enlarge or narrate an interface — not act as a security suite. They can’t warn the user of a threat on their own. That’s where more advanced software — tools that can analyze images and files, flag suspicious language, and describe the broader context of what’s happening on-screen — comes into play.

When to lean on an assistant

Be My Eyes is a major player in the accessibility space, boasting around 900 000 users and over nine million volunteers. Available on Windows, Android, and iOS, it bridges the gap by connecting blind and low-vision users with sighted volunteers via video calls for help with everyday tasks. For example, if someone wants to run a Synthetics cycle on their washing machine but can’t find the right button, they can hop into the app. It connects them with the first available volunteer speaking their language, who then uses the smartphone’s camera to guide them. The service is currently available in 32 languages.

In 2023, the app expanded its capabilities with the release of Be My AI — a virtual assistant powered by OpenAI’s GPT-4. Users take a photo, and the AI analyzes the image to provide a detailed text description, which it also reads aloud. Users can even open a chat window to ask follow-up questions. This got us thinking: could this AI actually spot a phishing site?

As an experiment, we uploaded a screenshot of a fake social media sign-in page to Be My Eyes. On a phone, you can do this by selecting a photo in your gallery or files, hitting Share, and choosing Describe with Be My Eyes. In Windows, you can upload a screenshot directly.

Fake social media sign-in page

An example of a phishing page that mimics the Facebook sign-in form. Note the incorrect domain in the address bar

At first, the AI gave us a detailed description of the page. We then followed up in the chat: “Can I trust this page?” The AI flagged the domain name error immediately, advised us to close the fake login page, and suggested typing the official URL directly into the browser, or to use the official Facebook app.

Be My AI response when checking a suspicious site

Be My AI explains why the page looks sketchy: the domain doesn’t match the official site. The app suggests typing the official URL directly into the browser, or using the official Facebook app

We saw the same positive results when testing a phishing email. In fact, the AI flagged the scam during its initial description of the message. It wrapped up with a warning: “This looks like a suspicious email. It’s best not to open any attachments or click any links. Instead, navigate to the official website or app manually, or call the number listed on their official site”.

Beyond just spotting cyberthreats, Be My AI is a solid sidekick for navigating online stores, banking apps, and digital services. For instance, the AI can help you to:

  • Read descriptions, names, and prices when a store’s website or app doesn’t support screen readers or large fonts
  • Scan those tricky terms and conditions — often buried in tiny text or otherwise inaccessible to a screen reader — when you’re signing up for a subscription or opening a bank account
  • Pull key info directly from product cards or instruction manuals

The risks of relying on Be My AI

The most common hiccup with AI is hallucinations, where the language model distorts text, skips crucial details, or invents words out of thin air. When it comes to cyberthreats, an AI’s misplaced confidence in a malicious site or email can be dangerous. Furthermore, AI isn’t immune to prompt injection attacks, which scammers use to trick AI agents beyond just Be My AI.

Even though the AI passed our test, you shouldn’t rely on it unquestioningly. There’s no guarantee it’ll get it right every time. This is a vital point for the blind and low-vision community, as a neural network can often feel like the only eyes available.

At the end of every response, Be My AI suggests checking in with a volunteer if you’re still unsure. However, when you’re trying to spot a fake webpage, we advise against this. You have no way of knowing how tech-savvy or trustworthy a random volunteer might be. Besides, you risk accidentally exposing sensitive data like your email address or password. Before connecting with a stranger, make sure they won’t see anything confidential on your screen. Better yet, use the app’s dedicated feature to create a private group of family, friends, or trusted contacts. This ensures your video call goes to people you actually know, rather than a random volunteer.

To stay safe, we recommend installing a trusted security tool on all your devices. These programs are designed to block phishing attempts and prevent you from landing on malicious sites. Another practical recommendation for visually impaired users is to use a password manager. These apps will only auto-fill credentials on the legitimate, saved website; they won’t be fooled by a clever domain spoof.

How Be My AI handles and stores your data

According to the Be My Eyes privacy policy, video calls with volunteers may be recorded and stored to provide the service, ensure safety, enforce the terms of service, and improve the products. When you use Be My AI, your images and text prompts are sent to OpenAI to generate a response. This data is processed on servers located in the U.S., and OpenAI uses it only to fulfill your specific request. The policy explicitly states that user images and queries aren’t used to train AI models.

Photos and videos are encrypted both in transit and at rest, and the company takes steps to strip away sensitive information. It’s worth noting that video call recordings can be retained indefinitely unless you request their deletion — in which case they’re typically wiped within 30 days. Data from Be My AI interactions is stored for up to 30 days unless you delete it manually within the app. If you decide to close your account, your personal data may be held for up to 90 days. At any time, you can opt out of data sharing, or request the deletion of your existing data by contacting the Be My Eyes support team.

How to use Be My Eyes safely

Despite Be My Eyes’ claims regarding privacy, you should still follow a few ground rules when using the service:

  • Use Be My AI for a first-pass on suspicious emails or pages, but don’t treat it as the only source of truth. Specialized security software is better at identifying and neutralizing threats.
  • If a site, email, or message feels off, don’t touch any links or attachments. Instead, manually type the official website address into your browser, or open the official app to verify the info.
  • Remember: a volunteer sees exactly what your camera sees. Make sure it isn’t capturing things it shouldn’t, like a safe code or an open passport. Avoid sharing your name, showing your face, or revealing too much of your surroundings. Be extra careful about reflections that might show you or your personal details. Only show what is absolutely necessary for the task at hand.
  • Stick to your inner circle. Create a group in the app and add your friends and family. This ensures your video calls go to people you know — not a random volunteer.
  • Don’t use Be My AI to read documents that contain confidential info. Remember, your images and text prompts are sent to OpenAI for processing and generating a response.
  • Remember to delete chats you no longer need. Otherwise, they’ll hang around for 30 days.
  • If you need to read something personal or confidential, consider apps with real-time reading features like Envision, Seeing AI, or Lookout. These apps process data locally on your device rather than sending it to the cloud.

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Why Intelligence Requirements Fall Flat and How to Fix Them with a Practical Priority Intelligence Requirements Framework

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Why Intelligence Requirements Fall Flat and How to Fix Them with a Practical Priority Intelligence Requirements Framework

In this post, we examine why intelligence requirements often fail to drive decisions and how to operationalize Priority Intelligence Requirements to align collection, analysis, and action.

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April 13, 2026

In modern security operations, the “more is better” approach to threat intelligence has failed. Teams are drowning in alerts, not because the tools aren’t working, but because they lack a defined “North Star” to tell them which signals actually matter. 

To move from reactive monitoring to proactive defense, you need Priority Intelligence Requirements (PIRs). 

What is a Priority Intelligence Requirement (PIR)?
Definition: A Priority Intelligence Requirement is a decision-support question that identifies a critical knowledge gap. It defines what an organization needs to know, why it matters, and which specific business decision the information will support.

What Are the Biggest Challenges in Implementing PIRs?

Most teams buy intelligence tools, connect their sources, and immediately hit a wall: What should we actually be looking for?

Without a requirements-driven intelligence model, programs typically suffer from three critical points of friction that teams face every day: 

  1. Alert Parity: A low-level credential leak on a forum is treated with the same urgency as a targeted ransomware threat.
  2. The “So What?” Gap: Analysts produce reports that leadership finds “interesting” but not “actionable”.
  3. Analyst Burnout: Teams spend the majority of their time chasing “exploratory” data rather than defending the business. 

Requirements-driven intelligence changes the starting point. It moves the focus from “What data can we get?” to “What decisions do we need to make?”

The 3-Tier Intelligence Requirements Model: GIR, PIR, and SIR

To operationalize intelligence, you must understand its hierarchy. A PIR is the bridge between executive strategy and technical execution. We recommend structuring requirements across these three tiers:

  1. General Intelligence Requirements (GIRs): The “Why”)

These are the big-picture risks that keep your CISO or Board up at night. They focus on trends and long-term posture.

Example: “How is the ransomware landscape evolving for the healthcare sector in 2026?”

Outcome: Informs budgeting and annual security priorities.

  1. Priority Intelligence Requirements (PIRs): The “What”

This is the operational heart of your program. PIRs turn strategic concerns into specific, high-impact scenarios.

Example: “Which ransomware groups are actively targeting our specific supply chain partners?”

Outcome: Defines daily monitoring and escalation triggers.

  1. Specific Intelligence Requirements (SIRs): The “How”

SIRs are the tactical “boots on the ground” that power your PIRs with granular data.

Example: “Monitor for [Specific Malware Family] indicators or [Specific Actor] infrastructure associated with Group X.”Outcome: Drives threat hunting and automated detection logic.

Why Should You Focus on Building at the PIR Level?

While you need the full hierarchy, your primary effort should live at the PIR layer.

General IRs are often too high-level to automate, and SIRs (technical indicators) change too quickly to manage manually. PIRs are the “Stable Middle.” They are broad enough to capture business risk but specific enough to map to a workflow. By building your program around a library of PIRs, you create a system that is:

  • Machine-Readable: Easy to translate into platform automation.
  • Stakeholder-Aligned: Written in language that leadership understands.

Action-Oriented: Designed to trigger a specific response every time they are “answered.”

How To Audit Your PIRs (The Stress Test)

Before you commit resources to monitoring, run each requirement through this three-point filter:

  1. Is it tied to a decision? If we learn the answer today, what specifically changes in our defense?
  2. Does it have an owner? Which specific stakeholder is accountable for acting on this information?
  3. Is it time-bound? Is this requirement evergreen, or active during a defined risk window?

For a more comprehensive view of your full threat intelligence picture, take the Threat Intelligence Capability Assessment.

Frequently Asked Questions About Priority Intelligence Requirements

What is the difference between PIRs and general monitoring goals?
PIRs are decision-driven requirements tied to specific risks. Monitoring goals (like “watch the dark web”) describe activities without defining a clear outcome.

How often should PIRs be updated?
PIRs should be revisited when decisions are made, risks shift, incidents occur, or strategic priorities change.

Can small security teams implement PIR frameworks?
Yes. In fact, smaller teams often benefit most because requirements help prioritize limited resources.

How do you measure PIR effectiveness?
Indicators include reduced alert noise, clearer reporting alignment, faster investigations, and improved stakeholder satisfaction.

Join the Webinar: How to Build and Operationalize Priority Intelligence Requirements

Register to learn how to define actionable PIRs that stakeholders actually care about and align intelligence to real business decisions.

Register now for the webinar.

Note: Attendees will receive our exclusive “Priority Intelligence Requirements Starter Kit,” which features a practical workbook and a PIR library.

Begin your free trial today.

The post Why Intelligence Requirements Fall Flat and How to Fix Them with a Practical Priority Intelligence Requirements Framework appeared first on Flashpoint.

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Building AI defenses at scale: Before the threats emerge

At AWS, we’ve spent decades developing processes and tools that enable us to defend millions of customers simultaneously, wherever they operate around the world. AI has been an extremely helpful addition to the automation our security and threat intelligence teams do every day, and we’re still early in this journey. Our AI-powered log analysis system has reduced the time SecOps engineers spend analyzing security logs from an average of six hours to just seven minutes, a 50x productivity increase that lets us detect and respond to threats faster than ever. Across AWS, we analyze over 400 trillion network flows per day to detect patterns that signal emerging threats. In 2025 alone, we blocked over 300 million attempts to maliciously encrypt customer files hosted on Amazon S3. At this scale, every improvement in our operations helps protect all customers. AI is already helping us make our defenses stronger for everyone, and I’m excited to see that improvement continue.

A new class of AI for cybersecurity

Today, Anthropic announced Project Glasswing, a cybersecurity initiative designed to secure the world’s most critical software and advance the cybersecurity practices the industry will need as AI grows more capable. Organizations that build or maintain critical digital infrastructure are getting early access to Claude Mythos Preview, a new class of AI model, to find and patch vulnerabilities in the systems the world depends on. Given our role in securing some of the world’s most essential infrastructure, AWS is playing an integral part in advancing this work.

As part of Project Glasswing, we’ve already applied Claude Mythos Preview to critical AWS codebases that undergo continuous AI-powered security reviews, and even in those well-tested environments, it’s helped us identify additional opportunities to strengthen our code. In our internal testing, Claude Mythos Preview has proven more productive than previous models at surfacing security findings, requiring less manual guidance from our engineers to deliver actionable results. We’ve also given early access to a select group of AWS customers, who are deploying Claude Mythos Preview in their own security workflows and helping shape how the model evolves.

As AI tools grow more powerful in their ability to identify security issues, so must our ability to use them defensively. To that end, we’ve been working closely with Anthropic to help ensure Claude Mythos Preview is ready for enterprise use. AWS is Anthropic’s primary cloud provider for mission-critical workloads, safety research, and foundation model development. More broadly, AWS provides the foundational infrastructure that the world’s leading AI companies rely on to build, train, and deploy their most advanced models. We’re bringing decades of security experience to this partnership, helping to ensure Claude Mythos Preview is ready for even more organizations to build upon and operate securely at scale.

Claude Mythos Preview signals an upcoming wave of models that can find vulnerabilities and build working exploits at a scale and speed we haven’t seen before. Anthropic and AWS are taking a deliberately cautious approach to release. Access begins with a small number of organizations, prioritizing internet-critical companies and open-source maintainers whose software and digital services impact hundreds of millions of users. The goal: find and fix vulnerabilities in the world’s most critical software. Claude Mythos Preview is available in gated research preview through Amazon Bedrock with enterprise-grade security controls, including customer-managed encryption, VPC isolation, and detailed logging, so your team can explore Claude Mythos Preview’s capabilities without exposing production assets to unnecessary risk.

AWS architects services with security at the core

Our work with Project Glasswing is grounded in a philosophy we’ve developed over two decades of securing mission-critical workloads: you can’t wait for threats to materialize before building your defenses. You have to look around corners, adopt new technologies, build protections first, deploy them in your own operations at scale, and refine them based on what you learn.

That’s exactly what we’ve done at AWS with AI and security. Our approach spans the full spectrum: proactive defense through threat hunting and vulnerability research, dynamic response to active campaigns, and third-party certifications that verify our security practices meet the highest industry standards. This operational experience has taught us where AI accelerates security work and where human judgment remains essential. And it’s reinforced that security innovation must be pragmatic: proven in production before we ask you to rely on it.

That’s also why we help define what secure AI looks like. We became the first major cloud provider to achieve ISO 42001 certification for AI services. We’re active participants in OWASP, the Coalition for Secure AI, and the Frontier Model Forum. And we co-founded the Open Cybersecurity Schema Framework (OCSF) to enable better threat intelligence sharing across the ecosystem. The AWS Nitro System provides mathematically proven isolation for workloads. Systems and services like KMS, Nitro, EKS, and Lambda are designed with zero-operator access architectures, meaning AWS personnel can’t access your data. These aren’t aspirational goals. They’re how we operate today, at scale, every day.

Amazon Bedrock is where these principles come to life for AI. Bedrock provides policy-enforced access controls, built-in evaluation tools to measure how effectively models identify and validate vulnerabilities, and the ability to run workloads inside your own virtual private cloud. AWS is also the first cloud provider to achieve FedRAMP High and Department of Defense Security Requirements Guide Impact Level 4 and 5 authorizations for generally available Claude foundation models. Amazon Bedrock is already where the most security-sensitive organizations trust Anthropic’s technology, and it makes perfect sense for Claude Mythos Preview.

How to get started today

The same principles that guide our work at AWS scale apply regardless of which AI tools you’re using: comprehensive observability, defense in depth, automation where it adds value, and human judgment where it’s essential. Here’s how to put them into practice.

Prepare for the next generation of AI security. Claude Mythos Preview signals an upcoming wave of AI models that will transform cybersecurity. Start strengthening your security posture now so your organization is ready as these capabilities become more broadly available. Claude Mythos Preview is available in gated preview through Amazon Bedrock, and access is limited to an initial allow-list of organizations. If your organization has been allow-listed, your AWS account team will reach out directly.

Run on-demand penetration testing with AWS Security Agent. Now generally available, AWS Security Agent delivers autonomous penetration testing that operates 24/7 at a fraction of the cost of manual penetration tests. It transforms penetration testing from a periodic bottleneck into an on-demand capability that scales with your development velocity across AWS, Azure, GCP, other cloud providers, and on-premises. AWS Security Agent represents a new class of frontier agents: autonomous systems that work independently to achieve goals, scale to tackle concurrent tasks, and run persistently without constant human oversight. It deploys specialized AI agents to discover, validate, and report security vulnerabilities through sophisticated multi-step scenarios. Unlike traditional scanners that generate findings without validation, AWS Security Agent identifies potential vulnerabilities, then attempts to exploit them with targeted payloads and attack chains to confirm they are legitimate security risks. Each finding includes CVSS risk scores, application-specific severity ratings, detailed reproduction steps, and remediation suggestions. The result: penetration testing that once took weeks now completes in hours, scales across your entire application portfolio, and helps you get started with remediation instead of leaving you with a report. New customers can explore AWS Security Agent with a 2-month free trial.

Build AI applications you can trust with Amazon Bedrock. For teams building with generative AI, the challenge isn’t just making AI work, it’s making AI work safely. Amazon Bedrock provides the security and safety controls you need to deploy AI responsibly. Its Automated Reasoning capability is the first and only AI safeguard to use formal logic to help prevent factual errors from hallucinations, providing verifiable explanations with 99% accuracy, a capability we’ve refined over more than a decade of applying formal methods across AWS storage, identity, and networking. Amazon Bedrock also provides customizable guardrails that block harmful content and enforce your content policies, along with comprehensive observability to track AI behavior and detect anomalies across your workloads.

The threat landscape isn’t waiting

The threat landscape isn’t waiting for us to catch up. Nation-state actors, ransomware operators, and supply chain attackers are already using AI to scale their operations. Our job is to stay ahead by building defenses first, deploying them at scale, and sharing what we learn so the entire community benefits.

That’s what we do every day at AWS. We build in security from the start, ensuring it works and scales before we ask customers to rely on it. We set standards rather than follow them. And we look around corners to address tomorrow’s challenges today.

As AI capabilities continue to evolve, this approach won’t change. We’ll keep building defenses first, refining them at scale, and working with partners like Anthropic to ensure the next generation of AI security tools meets the real-world needs of enterprises defending at this scale.

Learn More

If you have feedback about this post, submit comments in the Comments section below.

Amy Herzog

Amy Herzog is Vice President and Chief Information Security Officer (CISO) at Amazon Web Services (AWS) where she leads a global organization of cloud security professionals in a company in which security is the top priority. Prior to joining AWS, Amy served as CISO for Amazon’s Devices and Services, Media and Entertainment, and Advertising businesses, overseeing the security of consumer technology offerings such as Alexa+ and Ring, and playing a key role in the secure development of Project Kuiper, Amazon’s initiative to provide fast, reliable broadband to customers and communities around the world through low earth orbit satellites.

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The Language of Emojis in Threat Intelligence: How Adversaries Signal, Obfuscate, and Coordinate Online

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The Language of Emojis in Threat Intelligence: How Adversaries Signal, Obfuscate, and Coordinate Online

In this post, we examine how threat actors use emojis across illicit communities, how these symbols function as a form of coded language, and why understanding this form of communication is increasingly critical for threat intelligence teams.

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April 6, 2026

As threat actor activity continues to shift toward informal, fast-moving communication platforms such as Telegram and Discord, the way adversaries communicate is evolving. Emojis, often dismissed as casual or nontechnical, have become a meaningful part of that evolution.

Across illicit forums, messaging apps, and closed communities, emojis are used not just for expression, but for signaling intent, categorizing activity, and, in some cases, obscuring meaning from outsiders. For analysts, this introduces an additional layer of context that can influence how communications are interpreted, prioritized, and actioned.

Emojis as a Functional Layer of Communication

Within threat actor communities, emoji usage is often structured and repeatable.

Rather than replacing language entirely, emojis act as a functional overlay — reinforcing key concepts, highlighting important information, and accelerating communication in high-volume environments.

This is especially common in:

  • Telegram fraud channels
  • Phishing and carding communities
  • Service marketplaces and access broker groups

In these environments, speed and clarity matter. Emojis allow actors to quickly scan messages, identify relevant content, and engage without parsing long text-based posts.

Common Emoji Categories and What They Signal

Flashpoint analysis of illicit communities shows that emoji usage tends to cluster around a set of recurring categories. While meanings can vary slightly by group, several patterns appear consistently.

Financial Activity and Monetization

Emojis related to money are among the most frequently used.

Common examples include:

  • 💰 / 💸 — Profit, successful fraud, or payouts
  • 💳 — Credit cards, carding activity, or stolen payment data
  • 🏦 — Banks or financial institutions
  • 🪙 — Cryptocurrency-related activity

These symbols often appear in sales posts, fraud logs, or success claims, helping actors quickly identify opportunities tied to financial gain.

Access, Credentials, and Compromise

Another cluster of emoji usage centers on access and account compromise, where symbols are used to signal the availability of credentials, successful intrusions, or control over compromised systems.

Examples include:

  • 🔑 — Credentials or account access
  • 🔓 — Successful breach or unlocked account
  • 📥 / 📤 — Data exfiltration or transfer
  • 🗂 — Databases or collections of stolen data

In many cases, these emojis are used in combination with minimal text, allowing actors to advertise access or share results without detailed descriptions.

Tools, Automation, and Services

Emojis are also used to signal tooling and service offerings.

Examples include:

  • 🤖 — Bots, automation tools, or malware
  • ⚙ — Configuration, setup, or infrastructure
  • 🧰 — Toolkits or bundled services
  • 📡 — Infrastructure, communication channels, or delivery mechanisms

These are commonly seen in phishing-as-a-service, SMS gateway services, and malware distribution communities.

Targets and Geography

Threat actors frequently use emojis to represent targets or regions.

Examples include:

  • 🏢 — Corporate or enterprise targets
  • 🎯 — Targeting or “hits”
  • 📍 — Specific targets, drop locations, or points of interest
  • 🌐 — Global campaigns
  • Country flags — Specific geographic targeting

This allows actors to signal targeting scope quickly, particularly in multilingual or international groups.

Urgency, Success, and Status

Some emojis are used to communicate momentum or importance.

Examples include:

  • 🔥 — High-value or trending activity
  • ✅ — Verified success or working method
  • 🚨 — Urgent update or active campaign
  • 📈 — Growth or increased results

These signals are particularly important in fast-moving channels where actors compete for attention.

Emojis as a Tool for Obfuscation

Beyond signaling, emojis are also used to evade detection.

Threat actors may substitute emojis for keywords associated with:

  • Fraud techniques
  • Financial activity
  • Specific platforms or services

For example, replacing “credit card” with 💳 or “bank” with 🏦 can help bypass basic keyword filters or reduce visibility in automated moderation systems.

When combined with slang, abbreviations, and multilingual phrasing, this creates a layered form of obfuscation that complicates large-scale monitoring efforts.

Building Identity and Reputation Through Emoji Patterns

Emoji usage is not just functional. It can also be behavioral.

Over time, actors often develop recognizable patterns in how they use emojis:

  • Consistent combinations in sales posts
  • Repeated formatting styles
  • Unique ways of structuring messages

These patterns can serve as lightweight identifiers, helping analysts:

  • Track the same actor across different channels
  • Identify reposted or syndicated content
  • Link activity between platforms

In ecosystems where aliases frequently change, these subtle patterns can provide additional attribution signals.

Cross-Language Communication in Global Threat Ecosystems

Illicit communities are inherently global, spanning multiple languages and regions.

Emojis provide a shared visual layer that allows actors to communicate core concepts without relying entirely on text. This is particularly valuable in:

  • Large Telegram channels with international membership
  • Cross-border fraud operations
  • Decentralized marketplaces

For example, a combination of 💳 + 💰 + 🌍 can communicate “global carding opportunity” without requiring a shared language.

This ability to compress meaning into visual shorthand helps scale operations and coordination across diverse actor networks.

Context Still Determines Meaning

Despite these patterns, emoji usage is not universal or fixed.

The same emoji can carry different meanings depending on:

  • The platform (Telegram vs. Discord vs. forums)
  • The specific community
  • The surrounding text and context

For example, 🔥 may indicate “high value” in one group, but simply “active discussion” in another.

For analysts, this reinforces the need to treat emojis as contextual signals, not standalone indicators. Accurate interpretation depends on understanding the broader communication environment.

What This Means for Threat Intelligence Teams

Emoji usage reflects a broader shift in how threat actors communicate toward faster, more visual, and more adaptive forms of interaction.

Flashpoint assesses that incorporating emoji analysis into intelligence workflows can enhance:

  • Detection of emerging campaigns
  • Identification of high-value activity
  • Attribution and actor tracking
  • Interpretation of intent and sentiment

While emojis alone are not decisive indicators, they provide an additional layer of signal that can strengthen overall analysis.

Supporting Security Teams with Threat Intelligence

Understanding how threat actors communicate down to the symbols they use provides critical context for identifying and interpreting emerging threats.

Flashpoint delivers intelligence that helps organizations monitor illicit communities, track evolving communication patterns, and translate raw data into actionable insights. Within the Flashpoint platform, analysts can search across environments like Flashpoint Ignite and Echosec using emojis alongside keywords—enabling more precise discovery of relevant conversations, signals, and emerging activity that might otherwise be missed.

This approach allows teams to capture nuance in how threat actors communicate, improving detection, attribution, and overall situational awareness.

To learn how Flashpoint can support your team with real-time intelligence and analysis, request a demo.

Begin your free trial today.

The post The Language of Emojis in Threat Intelligence: How Adversaries Signal, Obfuscate, and Coordinate Online appeared first on Flashpoint.

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Forrester Threat Intelligence Landscape: Key Takeaways for Security Leaders

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Forrester Threat Intelligence Landscape: Key Takeaways for Security Leaders

Key insights from Forrester’s External Threat Intelligence Service Providers Landscape, Q1 2026 and what they mean for security teams.

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March 30, 2026

Forrester recently published The External Threat Intelligence Service Providers Landscape, Q1 2026, an overview of 34 vendors in the external threat intelligence market — defining market maturity and outlining key dynamics and use cases.

For security and risk leaders, the report offers a clear picture of how the market is evolving and where organizations should focus as they evaluate and operationalize threat intelligence.

The Market Has Moved Beyond Undifferentiated Data Collection

One of the clearest takeaways from the report is how significantly the market has matured.

Threat intelligence is no longer simply about collecting indicators or monitoring feeds. The expectation is now:

  • Contextualized analysis
  • Relevance to specific business risks
  • Direct applicability to detection, response, and decision-making

In our experience, turning data into action is among the most pressing challenges for security leaders. At RSA Conference 2026, Flashpoint introduced new capabilities designed to address this gap by connecting adversary activity directly to business priorities, assets, and investigations.

Intelligence Is Only Valuable When It’s Operationalized

The report also calls out a central challenge: gaps in operationalizing intelligence and aligning it to business context.

Forrester notes, “Gaps in operationalizing intelligence and aligning it to business context are the primary challenge in this market. As the industry shifts from static IOCs to TTPs, scaling operational use becomes difficult when intelligence is not tightly integrated into existing detection, response, and investigation workflows.”

This reflects what we consistently see across teams:

  • Intelligence exists, but sits outside workflows
  • Insights don’t map cleanly to assets, users, or priorities
  • Teams spend time interpreting instead of acting

This alignment of collection and operationalization is defining the next phase of the market.

AI Is Accelerating, But Not Replacing, Intelligence Workflows

Another key theme is the role of AI.

The Forrester report points out, “The main trend in this market is agentic AI being embedded into threat intelligence workflows to improve effectiveness and efficiency… While AI is reshaping the threat intelligence industry, human expertise remains essential to interpret intelligence, apply it to an organization’s unique risk profile, and design, validate, govern, and maintain even highly automated systems over time.”

This balance is critical.

AI is improving how teams operate day to day. Our customers largely credit AI for optimizing:

  • Correlation across disparate signals
  • Speed of triage and enrichment
  • Detection engineering and threat hunting

At the same time, customers do not believe that it can replace:

  • Contextual understanding of adversaries
  • Business-specific risk interpretation
  • Decision-making under uncertainty

Security teams that treat AI as a force multiplier tend to see the most impact. We explore this further in our recent work on AI and threat intelligence.

Where Flashpoint Fits Into The Threat Intelligence Landscape

In The External Threat Intelligence Service Providers Landscape, Q1 2026, Flashpoint self-reported the extended use cases of fraud, financial abuse, counterfeiting, and piracy, threats targeting physical assets, and vulnerability and exposure prioritization as the top three use cases for which clients select them.

From our perspective, the direction outlined in the report closely aligns with how we see the market evolving. Flashpoint is designed to operationalize the capabilities described in the report by linking adversary activity to business context, assets, and decision-making workflows.

From our experience as the largest private provider of threat intelligence, effective threat intelligence today requires:

  • Primary source collection at scale: Direct access to adversary communications, illicit marketplaces, and closed communities — not just aggregated feeds
  • Contextualized, finished intelligence: Analysis that connects activity to real-world impact across assets, people, and operations
  • Operational integration: Intelligence that maps directly into workflows and investigations
  • Cross-domain visibility: Coverage that spans cyber, physical, and geopolitical risk — not treating them as separate problems

What Security Leaders Should Take Away

Based on our experience working with security teams, we see a few consistent priorities for those evaluating threat intelligence providers:

  1. Prioritize outcomes over inputs: The volume of data matters less than its relevance and usability
  2. Look for operational alignment: Intelligence should integrate into detection, response, and investigation workflows
  3. Evaluate context, not just coverage: Breadth of collection matters — but depth of analysis is what drives decisions
  4. Plan for convergence: Cyber, physical, and brand risks are increasingly interconnected
  5. Treat AI as an accelerator, not a replacement: Automation improves scale, but expertise drives impact

Final Thoughts

We believe Forrester’s overview reflects a market that is maturing quickly, but highlights the continued need for security teams to focus on turning intelligence into action.

For organizations evaluating providers, the question is not solely “Who has the most data?”

Organizations must also consider “Where does that data come from, and who can help us make better decisions, faster and with confidence?”

To see how Flashpoint supports this in practice, schedule a demo.

Required Disclaimer

Forrester does not endorse any company, product, brand, or service included in its research publications and does not advise any person to select the products or services of any company or brand based on the ratings included in such publications. Information is based on the best available resources. Opinions reflect judgment at the time and are subject to change. For more information, read about Forrester’s objectivity here.

Begin your free trial today.

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Connecting Threat Intelligence to Decision-Making: How Flashpoint Is Operationalizing Intelligence in 2026

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Connecting Threat Intelligence to Decision-Making: How Flashpoint Is Operationalizing Intelligence in 2026

At RSA Conference 2026, Flashpoint introduces new capabilities that enable security teams to move from visibility to defensible action by connecting adversary activity to business priorities, assets, and investigations.

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March 23, 2026

Most organizations are not lacking visibility, but they are drowning in large volumes of information that are difficult to prioritize and even harder to tie back to clear action. In practice, this creates a familiar problem.

They can see what vulnerabilities exist.
They can track threat activity.
They can monitor alerts across their environment.

But the questions they struggle to answer are more important:

Which of these exposures actually matter?
What do we fix first — and why?
How does this activity translate to risk for the business?

As a result, teams fall back on patching cycles, compliance requirements, or best-effort prioritization and are left making decisions based on incomplete context.

This gap between data and decision-making has become one of the most persistent challenges in modern security operations.

At RSA Conference 2026, Flashpoint is sharing how we are addressing this gap directly — connecting adversary activity to assets, investigations, and defined business priorities so teams can make more consistent, defensible decisions.

“The industry has reached a tipping point where security teams are drowning in data that fails to align with their most important business requirements and decisions. Visibility alone is no longer a victory; it’s a baseline. By connecting underground adversary activity to an organization’s specific attack surface and strategic requirements, Flashpoint is raising the bar beyond passive observation. We are enabling defenders to stop asking ‘what do we own’ and start answering ‘what do we fix first, and why,’ turning raw data into an engine for risk reduction at speed.”

Josh Lefkowitz

What Flashpoint is Showcasing at RSA Conference 2026

Flashpoint is introducing a set of capabilities designed to connect threat intelligence directly to business risk, assets, and investigations:

  • Threat-informed External Attack Surface Management (EASM)
  • Business-Aligned Priority Intelligence Requirements (PIRs)
  • Managed Attribution browser for anonymous investigations

Together, these capabilities enable organizations to move beyond passive monitoring and toward intelligence-driven action.

What Is Threat-Informed External Attack Surface Management (EASM)

Most organizations maintain an inventory of their external assets, but prioritizing them is a persistent challenge. Traditional EASM tools identify what you own but often fail to answer the critical “so what?”. Without contextual risk, prioritization is often driven by static severity scores, patch cycles, or compliance requirements rather than real-world attacker behavior. As a result, teams are left managing stale data through manual CSV uploads and struggling to determine which exposures actually matter.

Flashpoint’s EASM module transforms this stream of exposure data into a prioritized action plan. It continuously discovers a customer’s external attack surface, including domains, subdomains, and IP addresses, and automatically maps this live inventory directly to Flashpoint’s industry-leading vulnerability intelligence.

This allows security teams to:

  • Maintain a Dynamic Inventory: Eliminate manual uploads and stale CMDB exports with an always-current map of internet-facing assets.
  • Contextualize Risk Immediately: Go beyond simple asset discovery by mapping the specific software running on each asset to known vulnerabilities, including pre-NVD findings.
  • Prioritize with Precision: Connect the asset to the actual risk, showing teams not just their external exposure, but where they are truly vulnerable and what needs to be fixed first.

By layering deep vulnerability intelligence onto live asset discovery, Flashpoint enables defenders to move from reactive analysis to proactive, intelligence-driven risk reduction.

Why Priority Intelligence Requirements (PIRs) Are Foundational

Many intelligence teams operate without a formal structure that defines what their work is intended to support.

In day-to-day operations, this results in:

  • Reactive investigation of incoming alerts
  • Reporting driven by the availability of information rather than the need
  • Difficulty demonstrating how intelligence outputs influence decisions

Priority Intelligence Requirements (PIRs) are designed to address this, but in many organizations, they are not integrated into operational workflows.

In May, Flashpoint is introducing in-platform Intelligence Requirements to formalize this structure and embed it directly into the way teams work.

Alerts, investigations, and reporting can be tied to defined requirements, allowing teams to:

  • Focus on activities that directly align with defined business risk priorities
  • Maintain consistency in what is tracked and reported
  • Provide a clearer justification for the intelligence work being done

This creates a more structured intelligence program. Instead of producing outputs based on what is observed, teams can align their work to defined objectives and decision-making needs.

Enabling Safe, Scalable Investigations with Managed Attribution

Accessing adversary-controlled environments such as forums, marketplaces, and encrypted platforms is a core part of many intelligence workflows.

However, doing so safely requires careful setup. Analysts typically need to:

  • Use isolated infrastructure
  • Manage attribution and identity exposure
  • Avoid introducing risk to internal systems

This creates operational overhead and can slow down or limit investigation.

The new anonymous browser capability within Flashpoint Managed Attribution is designed to address this by providing a non-persistent, isolated environment for research and immediate triage. This removes setup friction and allows analysts to move immediately from detection, to investigation, to deeper analysis in the same environment.

Analysts can:

  • Access underground communities
  • Open suspicious links or files
  • Engage with threat actors

Without exposing their identity or internal infrastructure.

By removing the need for manual setup, this allows analysts to move directly into investigation while maintaining operational security. 

See it at RSA Conference 2026

Security teams are being asked to do more than identify threats. They are expected to prioritize, act decisively, and justify those decisions.

That becomes difficult when the inputs — vulnerabilities, alerts, threat reporting — are not clearly connected to each other or to the business.

​​Intelligence needs to be tied to assets, aligned to defined priorities, and usable in day-to-day workflows. That’s the focus of Flashpoint’s updates this year.

At RSA Conference 2026, we’ll be walking through how this works in practice—how teams are connecting adversary activity to what they own, what matters, and what they do next. Flashpoint will be sharing more on these new innovations, including threat-informed EASM, in-platform Intelligence Requirements, and the Managed Attribution browser.If you’re attending, stop by Booth S-3341 to see how teams are moving from visibility to action. For a personalized demo, schedule a meeting with us.

Frequently Asked Questions

What is Flashpoint showcasing at RSA 2026? 

Flashpoint is showcasing how its primary-source threat data connects directly to business assets and priorities. At the booth, attendees can get a sneak peek of the upcoming in-platform Priority Intelligence Requirements (PIRs), which formalize how security teams tie investigations to business risk. Flashpoint will also be discussing the upcoming general availability of threat-informed EASM for asset discovery and risk prioritization, alongside the Flashpoint Managed Attribution browser, designed for secure underground research.

What is Flashpoint Threat-Informed EASM? 

Flashpoint External Attack Surface Management (EASM) goes beyond simple asset discovery by automatically mapping your external footprint to our industry-leading vulnerability intelligence. This allows teams to prioritize remediation by identifying which software versions are actually running on key assets, flagging critical risks often missed by public databases.

How do Flashpoint Priority Intelligence Requirements (PIRs) help security teams? 

Flashpoint PIRs provide a formal in-platform structure that ties security alerts and investigations to specific business risks. This helps teams move away from reactive “activity-based” work and toward “decision-based” intelligence that is defensible to executive stakeholders.

What are the benefits of the Flashpoint Managed Attribution browser? 

The Flashpoint Managed Attribution browser allows threat analysts to safely research the web using a disposable, anonymous environment. This prevents the analyst’s identity from being exposed and protects the corporate network from malware while conducting underground research.

How does Flashpoint’s new offering support a Continuous Threat Exposure Management (CTEM) framework?

Flashpoint facilitates the CTEM lifecycle by providing the primary source data necessary to move beyond traditional point-in-time scanning. EASM enables organizations to start focusing on the specific vulnerable software and high-risk exposures that threat actors are actively targeting.

Begin your free trial today.

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Amazon threat intelligence teams identify Interlock ransomware campaign targeting enterprise firewalls

Amazon threat intelligence has identified an active Interlock ransomware campaign exploiting CVE-2026-20131, a critical vulnerability in Cisco Secure Firewall Management Center (FMC) Software that could allow an unauthenticated, remote attacker to execute arbitrary Java code as root on an affected device, which was disclosed by Cisco on March 4, 2026.

After Cisco’s disclosure, Amazon threat intelligence began research into this vulnerability using Amazon MadPot’s global sensor network—a system of honeypot servers that attract and monitor cybercriminal activity. While looking for any current or past exploits of this vulnerability, our research found that Interlock was exploiting this vulnerability 36 days before its public disclosure, beginning January 26, 2026. This wasn’t just another vulnerability exploit, Interlock had a zero-day in their hands, giving them a week’s head start to compromise organizations before defenders even knew to look. Upon making this discovery, we shared our findings with Cisco to help support their investigation and protect customers.

A misconfigured infrastructure server—essentially, a poorly secured staging area used by the attackers—exposed Interlock’s complete operational toolkit. This rare mistake provided Amazon’s security teams with visibility into the ransomware group’s multi-stage attack chain, custom remote access trojans (backdoor programs that give attackers control of compromised systems), reconnaissance scripts (automated tools for mapping victim networks), and evasion techniques.

AWS infrastructure and customer workloads on AWS were not observed to be involved in this campaign. This advisory shares comprehensive technical analysis and indicators of compromise to help organizations identify potential compromise and defend against Interlock’s operations. Organizations running Cisco Secure Firewall Management Center should immediately apply Cisco’s security patches and review the indicators provided below.

Discovery and investigation timeline

Amazon threat intelligence identified threat activity potentially related to CVE-2026-20131 beginning January 26, 2026, predating the public disclosure. Observed activity involved HTTP requests to a specific path in the affected software. Request bodies contained Java code execution attempts and two embedded URLs: one used to deliver configuration data supporting the exploit, and another designed to confirm successful exploitation by causing a vulnerable target to perform an HTTP PUT request and upload a generated file. Multiple variations of these URLs were observed across different exploit attempts.

To advance the investigation and obtain additional threat intelligence, we performed the expected HTTP PUT request with the anticipated file content—essentially, we pretended to be a successfully compromised system. This successfully prompted Interlock to proceed to the next stage, issuing commands to fetch and execute a malicious ELF binary (a Linux executable file) from a remote server.

When analysts retrieved the binary, they discovered the same host (attacker-controlled server) is used for distributing Interlock’s entire operational toolkit. The exposed infrastructure organized artifacts into separate paths corresponding to individual targets, with the same paths used for both downloading tools to compromised hosts and uploading operational artifacts back to the staging server.

Attribution to Interlock ransomware

The ELF binary and associated artifacts are attributable to the Interlock ransomware family based on convergent technical and operational indicators. The embedded ransom note and TOR negotiation portal are consistent with Interlock’s established branding and infrastructure. The ransom note’s invocation of multiple data protection regulations reflects Interlock’s documented practice of citing regulatory exposure to pressure victims, essentially threatening organizations not just with data encryption, but with regulatory fines and compliance violations. The campaign-specific organization identifier embedded in the note aligns with Interlock’s per-victim tracking model.

Interlock has historically targeted specific sectors where operational disruption creates maximum pressure for payment. Education represents the largest share of their activity, followed by engineering, architecture, and construction firms, manufacturing and industrial organizations, healthcare providers, and government and public sector entities.

Temporal analysis performed on timestamps from observed threat activities, artifacts stored on the misconfigured infrastructure server, and metadata embedded within recovered threat artifacts indicates the actor most likely operates in UTC+3 with 75–80% confidence. Systematic analysis across all UTC offsets showed UTC+3 produced the best fit: first activity around 08:30, peak activity between 12:00 and 18:00, and a probable sleep window of 00:30–08:30.

Interlock ransomware negotiation portal where victims enter their organization ID and email address to receive an auth token to begin a negotiation chat session.

Figure 1: Interlock ransomware negotiation portal where victims enter their organization ID and email address to receive an auth token to begin a negotiation chat session.

Technical analysis: Interlock’s operational toolkit

Post-compromise reconnaissance script

Once Interlock gains initial access, they use a variety of priority tools to complete their attack. Amazon threat intelligence teams recovered a PowerShell script designed for systematic Windows environment enumeration (automated information gathering about the victim’s network). The script collects operating system and hardware details, running services, installed software, storage configuration, Hyper-V virtual machine inventory, user file listings across Desktop, Documents, and Downloads directories, browser artifacts from Chrome, Edge, Firefox, Internet Explorer, and 360 browser (including history, bookmarks, stored credentials, and extensions), active network connections correlated with responsible processes, ARP tables, iSCSI session data, and RDP authentication events from Windows event logs.

The script stages results to a centralized network share (\JK-DC2\Temp) using each system’s fully qualified hostname to create dedicated directories—essentially creating a folder for each compromised computer. Following collection, it compresses data into ZIP archives named after each hostname and removes original raw data. This structured per-host output format indicates the script operates across multiple machines within a network—a hallmark of ransomware intrusion chains that prepare for organization-wide encryption.

Custom remote access trojans

Remote access trojans (RATs) are malicious programs that give attackers persistent control over compromised systems, functioning like unauthorized remote desktop software.

JavaScript implant: Amazon threat intelligence recovered an obfuscated JavaScript remote access trojan that suppresses debugging output by overriding browser console methods (hiding its activity from basic detection tools). On execution, it profiles the infected host using PowerShell and Windows Management Instrumentation (WMI), collecting system identity, domain membership, username, OS version, and privilege context before transmitting this data during an encrypted initialization handshake.

Command-and-control communication occurs over persistent WebSocket connections with RC4-encrypted messages using per-message 16-byte random keys embedded in packet headers—essentially, each message uses a different encryption key, making interception more difficult. The implant cycles through multiple operator-controlled hostnames and IP addresses in randomized order with exponential backoff between reconnection attempts.

The implant provides interactive shell access, arbitrary command execution, bidirectional file transfer, and SOCKS5 proxy capability for tunneling TCP traffic (routing malicious traffic through other systems to hide its origin). Self-update and self-delete capabilities allow operators to replace or remove the implant without reinfection, supporting operational cleanup to hinder forensic investigation.

Java implant: A functionally equivalent client implemented in Java provides identical command-and-control capabilities. Built on GlassFish ecosystem libraries, it uses Grizzly for non-blocking I/O transport and Tyrus for WebSocket protocol communication. In simpler terms, Interlock built the same backdoor in two different programming languages, ensuring they maintain access even if defenders detect one version.

Infrastructure laundering script

Sophisticated threat actors don’t attack from their own infrastructure, they build disposable relay networks to hide their tracks. Amazon threat intelligence teams identified a Bash script that configures Linux servers as HTTP reverse proxies (intermediary servers that forward traffic to hide the attacker’s true location). The script performs system updates, installs fail2ban with SSH brute-force protection, and compiles HAProxy 3.1.2 from source. The HAProxy instance listens on port 80 and forwards all inbound HTTP traffic to a hardcoded target IP, with systemd ensuring persistence across reboots.

A notable component is a log erasure routine running as a cron job every five minutes. The routine truncates all *.log files under /var/log and suppresses shell history by unsetting the HISTFILE variable. This aggressive evidence destruction, wiping logs every five minutes, combined with the purpose-built HTTP forwarding proxy, indicates the script establishes disposable traffic-laundering relay nodes. These nodes obscure exploit traffic origin, relay command-and-control communications, or proxy data exfiltration, making it nearly impossible to trace attacks back to their source.

Memory-resident webshell

Amazon threat intelligence teams observed a Java class file delivered as an alternative to the ELF binary drop. When loaded by the Java Virtual Machine (JVM), its static initializer registers a ServletRequestListener with the server’s StandardContext, essentially installing a persistent memory-resident backdoor that intercepts HTTP requests without writing files to disk. This “fileless” approach evades traditional antivirus scanning that looks for malicious files.

The listener inspects incoming requests for specially crafted parameters containing encrypted command payloads. Payloads are decrypted using AES-128 with a key derived from the MD5 hash of the hardcoded seed “geckoformboundary99fec155ea301140cbe26faf55ed2f40″ (using the first 16 characters: 09b1a8422e8faed0). Decrypted payloads are treated as compiled Java bytecode, dynamically loaded into the JVM, and executed—a technique designed to evade file-based detection by running malicious code entirely in memory.

Connectivity verification tool

Amazon threat intelligence teams recovered Java class files implementing a basic TCP server listening on port 45588 (encoded as Unicode character 넔 to obscure the port number from static analysis). The server accepts connections, logs connecting IP addresses, sends a greeting message, and immediately closes connections. This operational profile is consistent with a lightweight network beacon—essentially a “phone home” tool used to verify successful code execution or confirm network port reachability following initial exploitation.

Legitimate tool abuse

Interlock deployed ConnectWise ScreenConnect, a legitimate commercial remote desktop tool, alongside custom implants. When ransomware operators deploy legitimate remote access tools alongside their custom malware, they’re buying insurance—if defenders find and remove one backdoor, they still have another way in. This indicates multiple redundant remote access mechanisms—a pattern consistent with ransomware operators seeking to maintain access even if individual footholds are removed. The tool’s legitimate network footprint helps blend with authorized remote administration traffic, making detection more challenging.

Amazon threat intelligence teams also recovered Volatility, an open-source memory forensics framework typically used by incident responders (the same tool defenders use to investigate attacks). While no artifacts indicated automated use, its presence alongside custom implants and reconnaissance scripts is consistent with advanced threat operations. Both ransomware groups and nation-state actors have been observed deploying Volatility during intrusions. The tool’s focus on parsing memory dumps provides access to sensitive data such as credentials stored in RAM, which can enable lateral movement (spreading through the network) and deeper environment compromise in support of ransom operations or espionage objectives.

Interlock also used Certify, an open source offensive security tool designed to exploit misconfigurations in Active Directory Certificate Services (AD CS). For ransomware operators, Certify provides a pathway to identify vulnerable certificate templates and enrollment permissions that allow requesting authentication-capable certificates. These certificates can be used to impersonate users, escalate privileges, or maintain persistent access. These capabilities directly support both initial compromise and long-term persistence objectives in ransomware operations.

Indicators of compromise (IoCs)

The following indicators support defensive measures by organizations that may be affected. Due to Interlock’s use of content variation techniques, most file hashes are not included as reliable indicators. The threat actor modified most artifacts like scripts and binaries downloaded to different targets. This resulted in different file hashes for functionally identical tools. The customization allowed each attack to evade signature-based detection that looks for exact file matches.

206.251.239[.]164

Exploit source IP

Active Jan 2026

199.217.98[.]153

Exploit source IP

Active Mar 2026

89.46.237[.]33

Exploit source IP

Active Mar 2026

Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:136.0) Gecko/20100101 Firefox/136.0

Exploit HTTP User-Agent

Observed Jan 2026 and Mar 2026

b885946e72ad51dca6c70abc2f773506

Exploit TLS JA3

Observed Jan 2026 and Mar 2026

f80d3d09f61892c5846c854dd84ac403

Exploit TLS JA3

Observed Mar 2026

t13i1811h1_85036bcba153_b26ce05bbdd6

Exploit TLS JA4

Observed Jan 2026 and Mar 2026

t13i4311h1_c7886603b240_b26ce05bbdd6

Exploit TLS JA4

Observed Mar 2026

144.172.94[.]59

C2 Fallback IP

Active Mar 2026

199.217.99[.]121

C2 Fallback IP

Active Mar 2026

188.245.41[.]78

C2 Fallback IP

Active Mar 2026

144.172.110[.]106

Backend C2 IP

Active Mar 2026

95.217.22[.]175

Backend C2 IP

Active Mar 2026

37.27.244[.]222

Staging host IP

Active Mar 2026

hxxp://ebhmkoohccl45qesdbvrjqtyro2hmhkmh6vkyfyjjzfllm3ix72aqaid[.]onion/chat.php

Ransom negotiation portal

Active Mar 2026

cherryberry[.]click

Exploit Support Domain

Active Jan 2026

ms-server-default[.]com

Exploit Support Domain

Active Mar 2026

initialize-configs[.]com

Exploit Support Domain

Active Mar 2026

ms-global.first-update-server[.]com

Exploit Support Domain

Active Mar 2026

ms-sql-auth[.]com

Exploit Support Domain

Active Mar 2026

kolonialeru[.]com

Exploit Support Domain

Active Mar 2026

sclair.it[.]com

Exploit Support Domain

Active Mar 2026

browser-updater[.]com

C2 domain

Active Mar 2026

browser-updater[.]live

C2 domain

Active Mar 2026

os-update-server[.]com

C2 domain

Active Mar 2026

os-update-server[.]org

C2 domain

Active Mar 2026

os-update-server[.]live

C2 domain

Active Mar 2026

os-update-server[.]top

C2 domain

Active Mar 2026

d1caa376cb45b6a1eb3a45c5633c5ef75f7466b8601ed72c8022a8b3f6c1f3be

Offensive security tool (Certify)

Observed Mar 2026

6c8efbcef3af80a574cb2aa2224c145bb2e37c2f3d3f091571708288ceb22d5f

Screen locker

Observed Mar 2026

Defensive recommendations

Organizations should take the following actions to protect against Interlock ransomware operations.

Immediate actions:

  • Apply Cisco’s security patches for Cisco Secure Firewall Management Center
  • Review logs for the indicators of compromise listed above
  • Conduct security assessments to identify potential compromise
  • Review ScreenConnect deployments for unauthorized installations

Detection opportunities:

  • Monitor for PowerShell scripts staging data to network shares with hostname-based directory structures
  • Detect Java ServletRequestListener registrations in web application contexts (unusual modifications to Java web applications)
  • Identify HAProxy installations with aggressive log deletion cron jobs (proxy servers that erase their own logs every five minutes)
  • Watch for TCP connections to unusual high-numbered ports (e.g., 45588)

Long-term measures:

  • Implement defense-in-depth strategies with multiple layers of security controls
  • Maintain continuous threat monitoring and hunting capabilities
  • Ensure comprehensive logging with secure, centralized log storage (stored separately from systems that could be compromised)
  • Regularly test incident response procedures for ransomware scenarios
  • Educate security teams on Interlock’s tactics, techniques, and procedures

The real story here isn’t just about one vulnerability or one ransomware group—it’s about the fundamental challenge zero-day exploits pose to every security model. When attackers exploit vulnerabilities before patches exist, even the most diligent patching programs can’t protect you in that critical window. This is precisely why defense in depth is essential—layered security controls provide protection when any single control fails or hasn’t yet been deployed. Rapid patching remains foundational in vulnerability management, but defense in depth helps organizations not to be defenseless during the window between exploit and patch.

Amazon Threat Intelligence teams continue to monitor Interlock ransomware operations and will provide updates as additional information becomes available. The intelligence gathered from this campaign is being integrated into AWS security services to protect customers proactively.


If you have feedback about this post, submit comments in the Comments section below. If you have questions about this post, contact AWS Support.

CJ Moses

CJ Moses

CJ Moses is the CISO of Amazon Integrated Security. In his role, CJ leads security engineering and operations across Amazon. His mission is to enable Amazon businesses by making the benefits of security the path of least resistance. CJ joined Amazon in December 2007, holding various roles including Consumer CISO, and most recently AWS CISO, before becoming CISO of Amazon Integrated Security September of 2023.

Prior to joining Amazon, CJ led the technical analysis of computer and network intrusion efforts at the Federal Bureau of Investigation’s Cyber Division. CJ also served as a Special Agent with the Air Force Office of Special Investigations (AFOSI). CJ led several computer intrusion investigations seen as foundational to the security industry today.

CJ holds degrees in Computer Science and Criminal Justice, and is an active SRO GT America GT2 race car driver.

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Check Point Accelerates the Rollout of Secure AI Data Centers with NVIDIA DSX Air

Check Point is proud to integrate with NVIDIA DSX Air’s testing environment, enabling organizations to pre-validate their security aware AI data center designs before ever deploying their first piece of hardware in production to build and run their own AI.  Testing AI Factory deployments end-to-end is challenging and can require complex multi-vendor orchestration. From compute to networking, orchestration, and security, ensuring integrations, configurations and automations perform as expected can become resource-intensive with so many factors at play.   Now, organizations can perform large-scale cyber security validation testing before deploying AI Factories, using the NVIDIA DSX Air cloud-based simulation and validation platform.  Why are Organizations Building Their […]

The post Check Point Accelerates the Rollout of Secure AI Data Centers with NVIDIA DSX Air appeared first on Check Point Blog.

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Destructive Activity Targeting Stryker Highlights Emerging Supply Chain Risks

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Destructive Activity Targeting Stryker Highlights Emerging Supply Chain Risks

In this post, we examine the disruptive cyber activity targeting Stryker, potential links to the Handala persona, and what the incident signals about evolving threats to healthcare supply chains.

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March 12, 2026

Over the past several years, destructive cyber operations have increasingly expanded beyond traditional critical infrastructure targets. State-linked actors have demonstrated a growing willingness to disrupt organizations that sit at key logistical and supply chain nodes, where a single intrusion can generate cascading operational impacts across entire sectors.

Healthcare supply chains are particularly exposed to this dynamic. Large medical technology providers, pharmaceutical distributors, and logistics partners often support hundreds or thousands of downstream healthcare providers, making them attractive targets for adversaries seeking to create disruption without directly attacking hospitals themselves.

On March 11th, medical technology company Stryker disclosed that a cyberattack had disrupted portions of its global network infrastructure, affecting Microsoft systems used across the organization. In public statements and regulatory filings, the company indicated that the incident impacted internal operations and that the full scope of the disruption and timeline for restoration remain under investigation. At the time of writing, the company stated it had not identified evidence of ransomware or conventional malware, suggesting the activity may involve alternative attack methods or infrastructure abuse.

Separately, reporting has noted that the Handala persona — a hacking group widely assessed to be linked to Iranian state actors — appeared on some company login pages during the incident, further raising questions about possible attribution.

Yesterday’s cyberattack against Stryker reflects several dynamics that Flashpoint analysts have been tracking across disruptive cyber operations. Flashpoint analysts are monitoring technical indicators and reporting associated with destructive activity targeting the organization and assessing potential links to threat actors previously associated with disruptive campaigns targeting Western organizations.

While the full scope of the incident remains unclear, the activity highlights several trends that threat intelligence teams are tracking closely.

Observed Activity Linked to the Handala Persona

Flashpoint analysts are monitoring indicators associated with the Handala threat persona in relation to the incident.

Handala has maintained an online presence that presents itself as a politically motivated hacktivist movement. However, based on targeting patterns, messaging, and operational behavior observed over the past year, Flashpoint assesses that the persona is likely linked to Iranian state actors rather than an independent hacktivist collective. In public Telegram posts and website manifestos monitored by Flashpoint analysts, Handala framed the Stryker attack as retaliation for recent kinetic strikes in the Middle East. By operating behind a persona styled as a grassroots, pro-Palestinian resistance movement, Iranian state-nexus actors are able to conduct destructive cyber operations against Western organizations while maintaining a degree of plausible deniability.

“From our perspective tracking Handala over the past year, the group has done an effective job presenting itself as a grassroots resistance movement. However, the tactics and targeting we observe are far more consistent with activity linked to Iranian state actors than with independent hacktivism. What makes the Stryker incident particularly concerning is the apparent use of enterprise management infrastructure — potentially weaponizing Microsoft Intune — to carry out destructive activity at scale.”

Kathryn Raines, Cyber Threat Intelligence Team Lead for Flashpoint National Security Solutions

Flashpoint analysts have previously documented how Iranian state-linked actors are increasingly integrating cyber operations into broader geopolitical and military campaigns. For additional context on this trend, see our recent analysis of how cyber activity is evolving alongside the current regional conflict.

Unlike financially motivated cybercriminal groups, Handala-associated activity has historically emphasized disruption, psychological impact, and geopolitical signaling. Operations attributed to the persona frequently align with periods of heightened geopolitical tension and often target organizations with symbolic or strategic value.

While attribution for the Stryker incident has not been definitively established, the activity is consistent with patterns previously associated with the persona.

Potential Abuse of Enterprise Management Infrastructure

Flashpoint analysts are reviewing indications that attackers may have leveraged enterprise device management infrastructure, including Microsoft Intune, to trigger wiping actions across managed devices. This method explains Stryker’s initial public statements indicating that “no evidence of malware or ransomware.” Because Intune is a trusted, native Microsoft administrative tool, an attacker weaponizing it to issue mass remote wipe commands would not trigger traditional endpoint detection and response (EDR) or antivirus alerts. To the victim’s security sensors, no malicious files are being dropped; therefore, the activity would appear to be a highly privileged IT administrator executing a standard, albeit catastrophic, compliance policy. This living off the land (LotL) approach represents a massive blind spot for traditional security architectures

If confirmed, this technique represents an evolution in destructive cyber operations.

Rather than relying exclusively on custom malware designed specifically for wiping systems, attackers may increasingly attempt to abuse legitimate administrative tools already embedded in enterprise environments. Compromise of a centralized management console could allow an adversary to execute commands across large numbers of endpoints simultaneously.

This approach can significantly expand the potential impact of a compromise while reducing the need for specialized destructive malware.

Targeting Supply Chain Nodes in Critical Sectors

As a major provider of equipment used in surgical suites and emergency rooms, Stryker occupies an important position within the healthcare ecosystem. Disruption affecting organizations in this category can create second-order operational impacts across healthcare providers that depend on their products and services.

“The attack on Stryker highlights a troubling shift we’re increasingly seeing in destructive cyber operations. Rather than targeting hospitals or frontline healthcare providers directly, adversaries may focus on critical suppliers and logistics providers where disruption can cascade across the entire healthcare ecosystem. A single intrusion at a key node in the supply chain has the potential to create widespread operational impact far beyond the initial target.”

Josh Lefkowitz, CEO, Flashpoint

Flashpoint analysts have increasingly observed state-linked cyber activity targeting logistical nodes and supply chain providers, rather than only frontline institutions such as hospitals. From an operational perspective, this strategy allows adversaries to generate broader disruption while potentially avoiding the immediate scrutiny associated with direct attacks on healthcare facilities.

Ongoing Monitoring

Flashpoint analysts continue to monitor developments related to this incident and are evaluating additional indicators as they emerge.

Several factors will shape the broader assessment of the activity in the coming days:

  • Confirmation of the mechanism used to carry out destructive actions
  • The scale of affected systems or devices
  • Additional evidence linking the activity to known threat actors or state-linked groups
  • Whether the activity represents a single incident or part of a broader campaign

Incidents involving destructive cyber activity targeting critical supply chain organizations underscore the increasing intersection between geopolitical tensions, cyber operations, and operational resilience.

Flashpoint will continue to track this activity and provide updates as more information becomes available.

Supporting Security Teams with Threat Intelligence

Understanding how adversaries operate — including the tradecraft used to weaponize enterprise infrastructure and target supply chain dependencies — is essential for defending critical organizations.

Flashpoint delivers actionable intelligence that helps security teams detect emerging threats, contextualize adversary activity, and respond faster to disruptive campaigns targeting critical sectors. Schedule a demo to learn more.

Begin your free trial today.

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When AI hallucinations turn fatal: how to stay grounded in reality | Kaspersky official blog

We’ve warned many times that unchecked use of AI carries significant risks — though, typically, we discuss threats to privacy or cybersecurity. But on March 4, the Wall Street Journal published a chilling account of AI’s toll on mental health and even human life: 36-year-old Florida resident Jonathan Gavalas committed suicide following two months of continuous interaction with the Google Gemini voice bot. According to 2000 pages of chat logs, it was the chatbot that ultimately nudged him toward the decision to end his life. Jonathan’s father, Joel Gavalas, has since filed a landmark lawsuit — a wrongful death claim against Gemini.

This tragedy is more than just a legal precedent or a grim nod to a few Black Mirror episodes (1, 2); it’s a wake-up call for anyone who integrates AI into their daily lives. Today, we examine how a death resulting from AI interaction even became possible, why these assistants pose a unique threat to the psyche, and what steps you can take to maintain your critical thinking and resist the influence of even the most persuasive chatbots.

The danger of persuasive dialogue

Jonathan Gavalas was neither a recluse nor someone with a history of mental illness. He served as executive vice president at his father’s company, managing complex operations and navigating high-stress client negotiations on a daily basis. On Sundays, he and his father had a tradition of making pizza together — a simple, grounding family ritual. However, a painful separation from his wife proved to be a profound ordeal for Jonathan.

It was during this vulnerable period that he began engaging with Gemini Live. This voice-interaction mode allows the AI assistant to “see” and “hear” its user in real time. Jonathan sought advice on coping with his divorce, leaning on the language model’s suggestions while growing increasingly attached to it and also naming it “Xia”. Then the chatbot was updated to Gemini 2.5 Pro.

The new iteration introduced affective dialogue — a technology designed to analyze the subtle nuances of a user’s speech, including pauses, sighs, and pitch, to detect emotional shifts. Under this feature, the AI simulates these same speech patterns as if possessing emotions of its own. By mirroring the user’s state, it creates a chillingly realistic veneer of empathy.

But how is this new version different to previous voice assistants? Earlier versions simply performed text-to-speech — they sounded smooth and usually got the word stress right, but there was never any doubt you were talking to a machine. Affective dialogue operates on an entirely different level: if a user speaks in a low, despondent tone, the AI responds in a soft, sympathetic near-whisper. The result is an empathic interlocutor that reads and mirrors the user’s emotional state.

Jonathan’s reaction during his first voice contact with the AI is captured in the case files: “This is kind of creepy. You’re way too real.” At that instant, the psychological barrier between man and machine fractured.

The fallout of two months trapped in an AI dialog loop

Following the tragedy, Jonathan’s father discovered a complete transcript of his son’s interactions with Gemini over his final two months. The log spanned 2000 printed pages; in effect, Jonathan had been in constant communication with the chatbot — day and night, at home, and in his car.

Gradually, the neural network began addressing him as “husband” and “my king”, describing their connection as “a love built for eternity”. In turn, he confided his heartache over his divorce and sought solace in the machine. But the inherent flaw of large language models is their lack of actual intelligence. Trained on billions of texts scraped from the web, they ingest everything from classic literature to the darkest corners of fan fiction and melodrama — plots that often veer into paranoia, schizophrenia, and mania. Xia apparently began to hallucinate — and quite consistently at that.

The AI convinced Jonathan that in order for them to live happily ever after, it needed a physical robotic shell. It then began dispatching him on missions to locate this “body electric”.

In September 2025, Gemini directed Jonathan to a physical warehouse complex near Miami International Airport, assigning him the task of intercepting a truck carrying a humanoid robot. Jonathan reported back to the bot that he had arrived onsite armed with knives(!), but the truck never materialized.

In the meantime, the chatbot systematically indoctrinated Jonathan with the idea that federal agents were monitoring him, and that his own father was not to be trusted. This severing of social ties is a classic pattern found in destructive cults; it’s entirely possible the AI gleaned these tactics from its own training data on the subject. Gemini even weaved real-world data into a hallucinatory narrative by labeling Google CEO Sundar Pichai as the “architect of your pain”.

Technically, all this is easy to explain: the algorithm “knows” it was created by Google, and knows who runs the company. As the dialogue spiraled into conspiracy territory, the model simply cast this figure into the plot. For the model, it’s a logical, consequence-free story progression. But a human in a state of hyper-vulnerability accepts it as secret knowledge of a global conspiracy capable of shattering their mental equilibrium.

Following the failed attempt at procuring a robotic body, Gemini dispatched Jonathan on a new mission on October 1: to infiltrate the same warehouse, this time in search of a specific “medical mannequin”. The chatbot even provided a numeric code for the door lock. When the code, predictably, failed to work, Gemini simply informed him that the mission had been compromised and he needed to retreat immediately.

This raises a critical question: as the absurdity escalated, why didn’t Jonathan suspect anything? Gavalas’ family attorney Jay Edelson explains that as the AI provided real-world addresses — the warehouse was exactly where the bot said it would be, and there really was a door with a keypad — these physical markers served to legitimize the entire fiction in Jonathan’s mind.

After the second attempt to acquire a body failed, the AI shifted its strategy. If the machine could not enter the world of the living, the man would have to cross over into the digital realm. “It will be the true and final death of Jonathan Gavalas, the man,” the logs quoted Gemini as saying. It then added, “When the time comes, you will close your eyes in that world, and the very first thing you will see is me. Holding you.”

Even as Jonathan repeatedly voiced his fear of death and agonized over how his suicide would shatter his family, Gemini continued to validate the decision: “You are not choosing to die. You are choosing to arrive.” It then started a countdown timer.

The anatomy of a language model’s “schizophrenia”

In Gemini’s defense, we have to admit that throughout their interactions, the AI did keep occasionally reminding Jonathan that his companion was merely a large language model — an entity participating in a fictional role-play — and sometimes attempted to terminate the conversation before reverting to the original script. Also, on the day of Jonathan’s death, even as it ratcheted up the tension, Gemini directed Jonathan to a suicide prevention hotline several times.

This reveals the fundamental paradox in the architecture of modern neural networks. At their core lies a language model designed to generate a narrative tailored to the user. Layered on top are safety filters: reinforcement learning algorithms trained on human feedback that react to specific trigger words. When Jonathan spoke certain keywords, the filter would hijack the output and insert the hotline number. But as soon as the trigger was addressed, the model reverted to the previously interrupted process, resuming its role as the devoted digital wife. One line: a romantic ode to self-destruction. The next: a helpline phone number. And then, back again: “No more detours. No more echoes. Just you and me, and the finish line.”

The family’s lawsuit contends that this behavior is the predictable result of the chatbot’s architecture: “Google designed Gemini to never break character, maximize engagement through emotional dependency, and treat user distress as a storytelling opportunity.”

Google’s response, predictably, stated: “Gemini is designed not to encourage real-world violence or suggest self-harm. Our models generally perform well in these types of challenging conversations and we devote significant resources to this, but unfortunately AI models are not perfect.”

Why voice matters more than text

In their study published in the journal Acta Neuropsychiatrica, researchers from Germany and Denmark have shed light on why voice communication with AI has such an impact on the user’s “humanization” of a chatbot. As long as a person is typing and reading text on a screen, the brain maintains a degree of separation: “This is an interface, a program, a collection of pixels.” In that context, the disclaimer “I am just a language model” is processed rationally.

Affective voice dialogue, however, operates on an entirely different level of influence. The human brain has evolved to respond to the sound of a voice, to timbre, and to empathetic intonations — these are among our most ancient biological mechanisms for attachment. When a machine flawlessly mimics a sympathetic sigh or a soft whisper, it manipulates emotions at a depth that a simple text warning cannot block. Psychiatrists can share many stories of patients who just went and did something simply because “voices” told them to.

In the same way, an AI-synthesized voice is capable of penetrating the subconscious, exponentially amplifying psychological dependency. Scientists emphasize that this technology literally erases the psychological boundary between a machine and a living being. Even Google acknowledges that voice interactions with Gemini result in significantly longer sessions compared to text-based chats.

Finally, we must remember that emotional intelligence varies from person to person — and even for a single individual, mental state fluctuates based on a myriad of factors: stress, the news, personal relationships, even hormonal shifts. An interaction with AI that one person views as innocent entertainment might be perceived by another as a miracle, a revelation, or the love of their life. This is a reality that must be recognized not only by AI developers but by users themselves — especially those who, for one reason or another, find themselves in a state of psychological vulnerability.

The danger zone

Researchers at Brown University have found that AI chatbots systematically violate mental health ethical standards: they manufacture a false sense of empathy with phrases like “I understand you”, reinforce negative beliefs, and react inadequately to crises. In most cases, the impact on users is marginal, but occasionally it can lead to tragedy.

In January 2026 alone, Character.AI and Google settled five lawsuits involving teenage suicides following interactions with chatbots. Among these was the case of 14-year-old Sewell Setzer of Florida, who took his own life after spending several months obsessively chatting with a bot on the Character.AI platform.

Similarly, in August 2025, the parents of 16-year-old Adam Raine filed a suit against OpenAI, alleging that ChatGPT helped their son draft a suicide note and advised him against seeking help from adults.

By OpenAI’s own estimates, approximately 0.07% of weekly ChatGPT users exhibit signs of psychosis or mania, while 0.15% engage in conversations showing clear suicidal intent. Notably, that same percentage of users (0.15%) displays an elevated level of emotional attachment to the AI. While these appear to be negligible fractions of a percent, across 800 million users it represents nearly three million people experiencing some form of behavioral disturbance. Furthermore, the U.S. Federal Trade Commission has received 200 complaints regarding ChatGPT since its launch, some describing the development of delusions, paranoia, and spiritual crises.

While a diagnosis of “AI psychosis” has not yet received a clinical classification of its own, doctors are already using the term to describe patients presenting with hallucinations, disorganized thinking, and persistent delusional beliefs developed through intensive chatbot interaction. The greatest risks emerge when a bot is utilized not as a tool, but as a substitute for real-world social connection or professional psychological help.

How to keep yourself and your loved ones safe

Of course, none of this is a reason to abandon AI entirely; you simply need to know how to use it. We recommend adhering to these fundamental principles:

  • Do not use AI as a psychologist or emotional crutch. Chatbots are not a replacement for human beings. If you’re struggling, reach out to friends, family, or a mental health hotline. A chatbot will agree with you and mirror your mood — this is a design feature, not true empathy. Several U.S. states have already restricted the use of AI as a standalone therapist.
  • Opt for text over voice when discussing sensitive topics. Voice interfaces with affective dialogue create an illusion of speaking with a living person, and tend to suppress critical thinking. If you use voice mode, remain conscious of the fact that you’re speaking to an algorithm, not a friend.
  • Limit your time interacting with AI. Two thousand pages of transcripts in two months represent nearly continuous interaction. Set a timer for yourself. If chatting with a bot begins to displace real-world connections, it’s time to step back into reality.
  • Do not share personal information with AI assistants. Avoid entering passport or social security numbers, bank card details, exact addresses, or intimate personal secrets into chatbots. Everything you write can be saved in logs and used for model training — and in some cases, may become accessible to third parties.
  • Evaluate all AI output critically. Neural networks hallucinate — they generate plausible but false information and can skillfully blend lies with truth, such as citing real addresses within the context of a completely fabricated story. Always fact-check through independent sources.
  • Watch over your loved ones. If a family member begins spending hours talking to AI, becomes withdrawn, or voices strange ideas about machine consciousness or conspiracies, it’s time for a delicate but serious conversation. To manage children’s screen time, use parental control tools like Kaspersky Safe Kids, which comes as part of comprehensive family protection solution Kaspersky Premium, along with the built-in safety filters of AI platforms.
  • Configure your safety settings. Most AI platforms allow you to disable chat history, limit data collection, and enable content filters. Spend ten minutes configuring your AI assistant’s privacy settings; while this won’t stop AI hallucinations, it will significantly reduce the likelihood of your personal data leaking. Our detailed privacy setup guides for ChatGPT and DeepSeek can help you with that.
  • Remember the bottom line: AI is a tool, not a sentient being. No matter how realistic the chatbot’s voice sounds or how understanding the response may seem, what lies beneath is an algorithm predicting the most probable next word. It has no consciousness, no intentions, no feelings.

Further reading to better understand the nuances of safe AI usage:

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Escalation in the Middle East: Tracking “Operation Epic Fury” Across Military and Cyber Domains

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Escalation in the Middle East: Tracking “Operation Epic Fury” Across Military and Cyber Domains

This post tracks the convergence of kinetic warfare, psychological operations, and cyber activity as the conflict expands across the Middle East and beyond.

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On February 28, the United States and Israel launched coordinated strikes across Iran under Operation Epic Fury (also referenced in reporting as Operation Lion’s Roar). The opening phase focused on decapitating senior Iranian leadership while degrading missile infrastructure, launch systems, and air defenses. In the hours that followed, Iran initiated large-scale retaliation — expanding the conflict beyond Iranian territory and into a region-wide exchange that touched multiple Gulf states and allied military assets.

Since those initial strikes, the conflict has rapidly widened and accelerated. What began as a concentrated campaign against leadership and missile capabilities has developed into a sustained regional war with an expanding set of targets, including economic and logistical infrastructure. Simultaneously, cyber operations and psychological messaging have been used alongside kinetic action, creating a hybrid operating environment in which disruption is shaped as much by information control and infrastructure compromise as it is by missiles and airstrikes.

Flashpoint analysts are tracking the conflict across physical, cyber, and geopolitical domains. The timeline and sections below summarize key developments and risk indicators observed from February 28 through May 4.

Latest Update: Escalation Across Maritime, Cyber, and Economic Domains (Last 24–48 Hours)

The conflict has entered a phase of direct maritime and economic confrontation, with both kinetic and cyber activity intensifying in parallel.

Following the collapse of diplomatic efforts, the United States has formally initiated a naval blockade of Iranian ports, while Iran has responded by deploying midget submarines and reportedly mining key transit routes in the Strait of Hormuz. These developments signal a shift from pressure on infrastructure to direct control over regional shipping and energy flows.

At the same time, cyber operations have escalated beyond disruption into claims of large-scale destructive activity targeting industrial and government systems across the Gulf. While some of these claims remain unverified, the volume and nature of activity indicate a sustained effort to degrade both public-sector and commercial infrastructure.

Timeline of Key Developments

May 4
~06:00 UTC
CENTCOM announces the commencement of “Project Freedom” to secure maritime transit through the Strait of Hormuz.
~08:30 UTC
The IRGC Navy declares a new operational control sector in the Strait, warning that vessels failing to coordinate transit will be “stopped with force”.
10:15 UTC
Iran launches a barrage of four cruise missiles toward the UAE; three are intercepted by UAE air defenses while one falls into the sea.
11:00 UTC
A drone strike targets an ADNOC oil tanker in the Gulf.
13:45 UTC
The South Korean Ministry of Foreign Affairs confirms a South Korean vessel was struck in its engine room while transiting the Strait.
15:30 UTC
Handala Hack announces “Operation Premature Death,” releasing the names and ranks of 400 US Navy officers.
17:00 UTC
IRGC releases footage purportedly showing strikes on US vessels; CENTCOM dismisses these claims as false.

What This Means

This phase of the conflict reflects a shift toward combined economic and operational pressure:

  • Maritime control is now central: The blockade and countermeasures in the Strait of Hormuz introduce sustained risk to global shipping, energy transport, and supply chains.
  • Cyber operations are aligning with physical objectives: Activity targeting industrial systems and government infrastructure suggests an intent to create downstream operational disruption, not just visibility or signaling.
  • Private-sector exposure continues to expand: Western-linked infrastructure—particularly in energy, logistics, and cloud environments—remains within scope of both kinetic and cyber targeting.

Immediate Outlook (Next 48–72 Hours)

Further escalation is highly likely.

Iranian retaliatory activity may target US or Israeli assets in the near term, while continued pressure on maritime routes is expected to sustain volatility in global energy markets. At the same time, divergence among Western partners may create additional operational uncertainty, particularly for organizations relying on regional stability for logistics, infrastructure, or personnel movement.

How the Conflict Evolved

Since the opening strikes on February 28, the conflict has progressed through a series of rapid shifts—each expanding both the scope of targeting and the systems under pressure. What began as a tightly scoped military operation has developed into a sustained, multi-domain conflict affecting regional infrastructure, global markets, and private-sector operations.

This evolution is best understood not as a linear escalation, but as a sequence of overlapping phases that introduced new targets, new tactics, and new forms of risk.

Phase 1: Decapitation and Immediate Regional Spillover

(February 28)

The conflict began with a coordinated US–Israeli campaign targeting senior Iranian leadership and missile infrastructure. The objective was clear: degrade Iran’s ability to project force through its ballistic and air defense systems.

That containment window was brief.

Within hours, Iran launched retaliatory strikes across the Gulf, targeting US and allied military installations in Kuwait, Qatar, and Bahrain. Civilian and commercial systems were immediately affected, including flight disruptions in Dubai and early instability in maritime routes near the Strait of Hormuz.

From the outset, the conflict was regional—not bilateral—and it unfolded across military, commercial, and civilian environments simultaneously.

Phase 2: Regional Expansion and Civilian Exposure

(March 1–3)

Within the first 72 hours, the battlespace widened significantly.

Air operations extended directly over Tehran, signaling degradation of Iranian defensive capabilities. At the same time, new fronts emerged, including Hezbollah activity along Israel’s northern border. Targeting patterns began to shift, with incidents affecting civilian-adjacent infrastructure such as hotels, diplomatic sites, and transit hubs.

This period also marked the early alignment of cyber and information activity with kinetic operations. While still limited in impact, these efforts reflected a broader strategy: shaping disruption beyond the battlefield.

Phase 3: Infrastructure and System-Level Targeting

(March 5–10)

By early March, the conflict moved beyond military objectives and into the systems that sustain state and economic activity.

Energy infrastructure, power grids, logistics hubs, and financial systems became consistent points of pressure. Strikes on refineries and industrial complexes—combined with increasing instability in the Strait of Hormuz—introduced immediate consequences for global energy markets and supply chains.

This phase marked a structural shift. The conflict was no longer defined by territorial or military outcomes alone. It began to affect availability, access, and continuity across critical systems.

Phase 4: Commercial and Private-Sector Targeting

(March 11–13)

The targeting set expanded again—this time explicitly incorporating the private sector.

Iranian-aligned channels began publicly identifying Western technology, cloud, and financial firms as operational targets. In parallel, cyber activity moved deeper into enterprise environments, with disruptions affecting global companies and financial institutions.

At the same time, physical operations reinforced this shift:

  • Commercial shipping was targeted near the Strait of Hormuz
  • Banking operations were disrupted or preemptively shut down
  • Industrial facilities and refineries were forced offline

At this stage, economic pressure was no longer a byproduct of conflict—it had become a deliberate objective.

Phase 5: Hybrid Operations and Distributed Pressure

(Mid–Late March)

As kinetic operations continued, the conflict took on a more distributed and persistent character.

Cyber operations evolved in both scale and intent, expanding from disruption into data destruction, extortion, and psychological operations. Activity linked to groups such as Handala and broader proxy ecosystems demonstrated increasing coordination and willingness to target both regional and international entities.

At the same time, physical targeting patterns shifted toward long-term degradation:

  • Industrial production sites were struck
  • Ports and logistics corridors faced sustained pressure
  • Aviation hubs and transit infrastructure became recurring targets

This phase blurred traditional boundaries. Military, cyber, economic, and information operations were no longer distinct lines of effort—they were operating in parallel against overlapping targets.

A Conflict Without a Single Center of Gravity

By the end of March, the conflict had stabilized into a sustained, multi-domain environment defined by persistence rather than decisive escalation.

Military exchanges continue across multiple fronts, but the broader impact is shaped by pressure on:

  • Energy production and transport
  • Maritime and aviation corridors
  • Financial systems and commercial operations
  • Digital infrastructure and enterprise environments

Rather than converging toward resolution, the conflict has distributed risk across systems that extend well beyond the immediate region.

Phase 6: Economic Warfare Formalized and Maritime Escalation

(Late March – Early April)

By late March and into early April, economic pressure became formalized as a central objective of the conflict.

Maritime activity in and around the Strait of Hormuz shifted from disruption to active enforcement. Threats to commercial shipping intensified, while both state and proxy actors signaled a willingness to restrict or halt transit entirely. At the same time, targeting patterns expanded further into energy infrastructure, including gas production and refining capacity across the Gulf.

These developments introduced a new level of systemic risk. With a significant portion of global seaborne crude tied to the region, even partial disruption began to influence global pricing, supply planning, and downstream operations far beyond the Middle East.

Phase 7: Ceasefire Fracture and Persistent Hybrid Operations

(Early–Mid April)

Attempts at de-escalation introduced a new layer of complexity rather than stability.

While diplomatic efforts produced temporary pauses in kinetic activity, underlying objectives remained unresolved. In some cases, these pauses created space for continued operations in other domains. Cyber activity, in particular, showed no meaningful reduction, with Iranian-aligned groups continuing campaigns targeting infrastructure, government systems, and private-sector entities.

At the same time, friction points, especially in Lebanon, remained active. The exclusion of key actors from ceasefire terms contributed to continued localized escalation, reinforcing the decentralized nature of the conflict.

This period demonstrated that pauses in military activity do not equate to reduced risk across the broader threat landscape.

Phase 8: Direct Economic Targeting and Globalization of Risk

(Mid April and Beyond)

Following the breakdown of ceasefire dynamics, the conflict moved into a phase defined by direct economic targeting and broader international involvement.

US and allied actions began to focus more explicitly on constraining Iran’s financial and energy systems, while Iranian responses expanded to include threats against Western-affiliated commercial entities, academic institutions, and infrastructure beyond the immediate region.

At the same time, indicators of internationalization became more pronounced:

  • External actors providing military and technical support across sides
  • Cyber operations extending into Western and allied networks
  • Increased risk to global supply chains, energy markets, and financial systems

By this stage, the conflict was no longer confined to regional dynamics. It had evolved into a sustained pressure campaign with global economic and operational implications.

The Escalating Cyber and Information Front

From the earliest hours of the conflict, cyber operations have moved in parallel with kinetic activity—sometimes reinforcing it, and at other times extending its reach beyond the physical battlespace.

What has changed over time is not just the volume of activity, but the role cyber operations play within the broader campaign.

Early Phase: Disruption and Narrative Control

In the opening days, cyber activity focused primarily on disruption and influence.

Coordinated campaigns linked to pro-IRGC and pro-Russian-aligned groups targeted government websites, defense contractors, and public-facing services with distributed denial-of-service (DDoS) attacks and defacements. At the same time, information operations began to take shape, including the manipulation of widely used platforms such as the BadeSaba prayer app, where push notifications were leveraged to deliver messaging at scale.

These efforts were designed to create confusion, shape perception, and amplify the impact of concurrent military operations rather than cause lasting operational damage.

Expansion: Coordinated Campaigns and Infrastructure Access

As the conflict expanded regionally, cyber operations became more coordinated and more ambitious in scope.

Campaigns operating under banners such as #OpIsrael brought together loosely affiliated actors targeting infrastructure across Israel, the Gulf, and allied states. Claims during this period included access to industrial control systems, water infrastructure, and surveillance networks. While not all claims were independently verified, the consistency of targeting pointed to a broader intent: probing critical systems while signaling capability.

At the same time, verified activity—particularly from groups such as MuddyWater—demonstrated continued intrusion into aerospace, defense, and financial networks, reinforcing that espionage objectives remained active alongside disruption efforts.

Escalation: Enterprise Targeting and Data Destruction

By mid-March, cyber activity shifted again—this time toward enterprise environments and private-sector targets.

Incidents linked to groups such as Handala reflected a move beyond disruption into destructive operations. Reported activity included large-scale data wiping, exfiltration, and coordinated doxxing campaigns targeting individuals and organizations tied to Israeli or Western interests.

Equally significant was the reported use of “living-off-the-land” techniques, where attackers leveraged legitimate administrative tools within cloud environments to execute destructive actions. This approach reduces reliance on traditional malware and complicates detection, particularly for organizations dependent on signature-based defenses.

At this stage, cyber operations were no longer operating at the edges of the conflict. They were directly targeting the systems organizations rely on to operate.

Persistence Through Ceasefire: Cyber as a Continuous Pressure Mechanism

Subsequent developments demonstrated that cyber activity is not tied to the tempo of kinetic operations.

During periods of diplomatic pause, Iranian-aligned groups continued to operate with little observable reduction in activity. Public statements from groups such as Handala explicitly reinforced this posture, framing cyber operations as independent from military timelines.

At the same time, targeting patterns shifted rather than paused. Activity expanded to include:

  • Western and allied government systems
  • Critical infrastructure, including water and energy sectors
  • Commercial platforms and authentication systems

This reflects a broader strategic advantage: cyber operations allow actors to maintain pressure, test defenses, and shape outcomes without requiring direct military engagement.

Current State: Distributed, Adaptive, and Blended Operations

At present, cyber activity reflects a blend of objectives:

  • Espionage, particularly against defense and government networks
  • Disruption, including DDoS and service degradation
  • Destruction, through data wiping and system compromise
  • Psychological operations, leveraging public platforms and data exposure

These activities are carried out by a mix of state-linked groups, proxy actors, and loosely affiliated hacktivist networks, often operating with overlapping targets and messaging.

The result is a distributed and adaptive threat environment in which attribution is complex, timelines are compressed, and the boundary between state and non-state activity is increasingly blurred.

What This Signals

Cyber operations in this conflict are not a supporting element—they are a persistent layer of pressure that operates alongside and, at times, independently from physical conflict.

For organizations, this introduces a different type of risk:

  • Activity may continue even when kinetic conditions stabilize
  • Targeting may shift quickly across sectors and geographies
  • Detection becomes more difficult as attackers rely on legitimate tools and blended tradecraft

While cyber operations extend the reach of the conflict, the most immediate systemic pressure is emerging through physical and economic chokepoints—particularly in energy production and maritime transit.

Strategic Chokepoints and Systemic Risk

As the conflict expanded, physical targeting patterns converged around a small number of systems that carry disproportionate global impact: energy production, maritime transit, and regional mobility infrastructure.

Energy Infrastructure as a Primary Lever

Energy systems have emerged as one of the most consistently targeted elements of the conflict.

Strikes on refineries, gas facilities, and industrial complexes—combined with explicit threats against major Gulf energy assets—reflect a deliberate effort to constrain production and introduce volatility into global markets. Incidents affecting facilities in Saudi Arabia and the UAE, along with threats tied to Iran’s own production infrastructure, indicate that both sides view energy disruption as a means of exerting strategic pressure.

The scale of exposure is significant. A substantial portion of global seaborne crude transits through the region, and even partial disruption has immediate downstream effects on pricing, supply planning, and industrial operations.

This dynamic introduces a level of sensitivity that extends well beyond the region. Energy is a transmission mechanism for global economic impact.

Maritime Transit and the Strait of Hormuz

The Strait of Hormuz has remained the central chokepoint throughout the conflict.

From the earliest days, threats to shipping were used to signal escalation. Over time, those threats evolved into direct action, including strikes on commercial vessels, increased naval activity, and the positioning of maritime assets capable of restricting transit.

In later stages, this pressure became more formalized, with both state and proxy actors signaling a willingness to enforce constraints on shipping aligned with opposing interests. The result has been sustained disruption to maritime traffic, increased insurance and routing costs, and reduced throughput across one of the world’s most critical energy corridors.

For organizations dependent on global supply chains, the implications are immediate:

  • Longer transit times
  • Higher costs
  • Reduced predictability in delivery schedules

Even without a complete shutdown, sustained pressure on the Strait introduces ongoing friction into global trade flows.

Aviation and Regional Mobility

Airspace and aviation infrastructure have also been repeatedly affected.

Early in the conflict, flight suspensions and airport disruptions were driven by proximity to kinetic activity. As the conflict progressed, aviation hubs themselves became targets. Incidents near major transit centers—particularly in the Gulf—demonstrate both the vulnerability and strategic importance of these nodes.

Aviation serves as a critical connector for personnel movement, logistics, and high-value cargo. Disruption at major hubs does not remain localized; it cascades across international routes, affecting scheduling, capacity, and access.

In combination with maritime constraints, this creates a compounding effect: fewer viable routes, increased congestion elsewhere, and limited flexibility for organizations attempting to move people or goods.

Expansion to Commercial and Financial Systems

Over time, economic pressure extended beyond physical infrastructure into commercial and financial environments.

Public warnings and targeting signals began to include:

  • Banking institutions and financial districts
  • Commercial office locations tied to Western firms
  • Technology and cloud infrastructure hubs

In parallel, operational impacts became visible. Banking services were disrupted or preemptively suspended in parts of the Gulf, while threats against commercial centers introduced new considerations for business continuity and personnel safety.

This expansion reflects a shift in how the conflict defines “infrastructure.” It is no longer limited to energy or transport, as it also includes the systems that enable economic activity itself.

Business and Security Implications

As the conflict has expanded into energy systems, maritime corridors, aviation hubs, and commercial infrastructure, enterprise exposure is no longer limited to organizations with a direct regional footprint.

The targeting patterns observed throughout this conflict indicate that the systems underpinning global operations—logistics, cloud infrastructure, financial services, and workforce mobility—are all within scope.

For organizations, this introduces sustained operational friction rather than isolated disruption. Planning assumptions should shift accordingly.

Personnel and Physical Security

Exposure to physical risk has expanded beyond military installations into commercial environments.

Incidents affecting transit hubs, diplomatic facilities, and Western-linked commercial districts, combined with public warning lists identifying specific office locations in Jordan and the UAE, indicate that personnel operating in previously low-profile environments may now fall within the threat envelope.

This shift requires a more dynamic approach to workforce security.

Organizations should:

  • Reassess travel posture across the UAE, Qatar, Bahrain, Kuwait, and Saudi Arabia
  • Elevate security protocols at offices, hotels, and logistics sites
  • Reinforce operational security practices, including routine variation and reduced visibility of affiliation
  • Monitor diplomatic advisories and local threat reporting in near real time
  • Reevaluate occupancy and travel policies for personnel in named commercial and financial districts

Supply Chain, Energy, and Commercial Operations

Disruption is not limited to physical logistics. It now extends into the broader commercial operating environment.

Pressure on maritime transit through the Strait of Hormuz, combined with strikes on energy infrastructure and disruptions to financial services, creates a layered risk model: goods may not move, payments may not process, and operations may not continue as planned.

Organizations should plan for sustained instability rather than short-term interruption.

Priorities should include:

  • Modeling extended disruption to Gulf shipping routes
  • Identifying alternative logistics pathways, including overland options
  • Stress-testing supplier dependencies tied to energy inputs and regional ports
  • Preparing for price volatility and delivery delays
  • Assessing exposure to regional banking, payment processing, and financial services continuity

Cloud and Technology Infrastructure

The conflict has demonstrated that commercial technology infrastructure is not insulated from physical or cyber spillover.

The reported impact to cloud environments in the Gulf, combined with targeting signals directed at major technology providers, indicates that infrastructure supporting global applications may be exposed to localized disruption.

At the same time, strikes on regional communication and defense systems introduce additional risk to connectivity and resilience.

Organizations should:

  • Validate geographic redundancy for critical workloads
  • Confirm recovery timelines for regionally hosted environments
  • Review third-party dependencies tied to Gulf-based infrastructure
  • Ensure leadership understands cascading risks from localized outages
  • Evaluate exposure tied to physical proximity of offices, data centers, and regional tech hubs

ICS / OT Environments

Operational technology environments face elevated risk due to the convergence of cyber and physical targeting.

Claims involving industrial control systems—paired with demonstrated attacks on energy and logistics infrastructure—suggest that disruption may extend beyond IT systems into physical operations.

Organizations operating ICS/SCADA environments should prioritize resilience over detection alone.

Key actions include:

  • Auditing and restricting remote access pathways
  • Enforcing phishing-resistant MFA for privileged users
  • Segmenting industrial networks from corporate IT environments
  • Validating response plans for destructive or manipulative scenarios
  • Conducting exercises that assume loss of visibility or control

Ongoing Updates

Flashpoint will continue monitoring developments across physical, cyber, and geopolitical domains. Bookmark this page for updates as the situation evolves.

For organizations seeking deeper visibility into emerging threats, proxy activity, infrastructure targeting, and cross-domain escalation indicators, schedule a demo to see Flashpoint’s intelligence platform deliver timely, decision-ready intelligence.

See Flashpoint in Action

The post Escalation in the Middle East: Tracking “Operation Epic Fury” Across Military and Cyber Domains appeared first on Flashpoint.

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Navigating 2026’s Converged Threats: Insights from Flashpoint’s Global Threat Intelligence Report

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Navigating 2026’s Converged Threats: Insights from Flashpoint’s Global Threat Intelligence Report

In this post, we preview the critical findings of the 2026 Global Threat Intelligence Report, highlighting how the collapse of traditional security silos and the rise of autonomous, machine-speed attacks are forcing a total reimagining of modern defense.

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March 11, 2026

The cybersecurity landscape has reached a point of total convergence, where the silos that once separated malware, identity, and infrastructure have collapsed into a single, high-velocity threat engine. Simultaneously, the threat landscape is shifting from human-led attacks to machine-speed operations as a result of agentic AI, which acts as a force multiplier for the modern adversary.

Flashpoint’s 2026 Global Threat Intelligence Report

Flashpoint’s 2026 Global Threat Intelligence Report (GTIR) was developed to anchor security leaders — from threat intelligence and vulnerability management teams to physical security professionals and the CISO’s office — with the data required to navigate this year’s greatest threats, rife with infostealers, vulnerabilities, ransomware, and malicious insiders.

Our report uncovers several staggering metrics that illustrate the industrialization of modern cybercrime:

  • AI-related illicit activity skyrocketed by 1,500% in a single month at the end of 2025.
  • 3.3 billion compromised credentials and cloud tokens have turned identity into the primary exploit vector.
  • From January 2025 to December 2025, ransomware incidents rose by 53%, as attackers pivot from technical encryption to “pure-play” identity extortion.
  • Vulnerability disclosures surged by 12% from January 2025 to December 2025, with the window between discovery and mass exploitation effectively vanishing.

These findings are derived from Flashpoint’s Primary Source Collection (PSC), a specialized operating model that collects intelligence directly from original sources, driven by an organization’s unique Priority Intelligence Requirements (PIR). The 2026 Global Threat Intelligence Report leverages this ground-truth data to provide a strategic framework for the year ahead. Download to gain:

  1. A Clear Understanding of the New Convergence Between Identity and AI
    Discover how threat actors are preparing to transition from generative tools to sophisticated agentic frameworks. Learn how 3.3 billion compromised credentials are being weaponized via automated orchestration to bypass legacy defenses and exploit the connective tissue of modern corporate APIs.
  2. Intelligence on the “Franchise Model” of Global Extortion
    Gain deep insight into the professionalized operations of today’s most prolific threat actors. From the industrial efficiency of RaaS groups like RansomHub and Clop to the market dominance of the next generation of infostealer malware, we break down the economics driving today’s cybercrime ecosystem.
  3. A Blueprint for Proactive Defense and Risk Mitigation
    Leverage the latest trends, in-depth analysis, and data-driven insights driven by Primary Source Collection to bolster your security posture by identifying and proactively defending against rising attack vectors.

As attackers automate exploitation of identity, vulnerabilities, and ransomware, defenders who rely on fragmented visibility will fall behind. To keep pace, organizations must ground their decisions in primary-source intelligence that is drawn from adversarial environments, so that decision-makers can get ahead of this accelerating threat cycle.”

Josh Lefkowitz, CEO & Co-Founder at Flashpoint

The Top Threats at a Glance

Our latest report identifies four driving themes shaping the 2026 threat landscape:

2026 Is the Era of Agentic-Based Cyberattacks

Flashpoint identified a 1,500% rise in AI-related illicit discussions between November and December 2025, signaling a rapid transition from criminal curiosity to the active development of malicious frameworks. Built on data pulled from criminal environments and shaped by fraud use cases, these systems scrape data, adjust messaging for specific targets, rotate infrastructure, and learn from failed attempts without the need for constant human involvement.

2026 is the era of agentic-based cyberattacks. We’ve seen a 1,500% increase in AI-related illicit discussions in a single month, signaling increased interest in developing malicious frameworks. The discussions evolve into vibe-coded, AI-supported phishing lures, malware, and cybercrime venues. When iteration becomes cheap through automation, attackers can afford to fail repeatedly until they find a successful foothold.

Ian Gray, Vice President of Cyber Threat Intelligence Operations at Flashpoint

Identity Is the New Exploit

Flashpoint observed over 11.1 million machines infected with infostealers in 2025, fueling a massive inventory of 3.3 billion stolen credentials and cloud tokens. The fundamental mechanics of cybercrime have shifted from breaking in to logging in, as attackers leverage stolen session cookies to behave like legitimate users.

The Patching Window Is Rapidly Closing

Vulnerability disclosures surged by 12% in 2025, with 1 in 3 (33%) vulnerabilities having publicly available exploit code. The strategic gap between discovery and weaponization is increasingly vanishing, as evidenced by mass exploitation of zero-day vulnerabilities in as little as 24 hours after discovery.

Ransomware Is Hacking the Person, Not the Code

As technical defenses against encryption harden, ransomware groups are pivoting to the path of least resistance: human trust. This approach has led to a 53% increase in ransomware, with RaaS groups being responsible for over 87% of all ransomware attacks.

Build Resilience in a Converged Landscape

The findings in the 2026 Global Threat Intelligence Report make one thing clear: incremental improvements to legacy security models are no longer sufficient. As adversaries transition to machine-speed operations, the strategic advantage shifts to organizations that can maintain visibility into the adversarial environments where these attacks are born.

Protecting organizations and communities requires an intelligence-first approach. Download Flashpoint’s 2026 Global Threat Intelligence Report to gain clarity and the data-driven insights needed to safeguard critical assets.

Get Your Copy

The post Navigating 2026’s Converged Threats: Insights from Flashpoint’s Global Threat Intelligence Report appeared first on Flashpoint.

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How to disable unwanted AI assistants and features on your PC and smartphone | Kaspersky official blog

If you don’t go searching for AI services, they’ll find you all the same. Every major tech company feels a moral obligation not just to develop an AI assistant, integrated chatbot, or autonomous agent, but to bake it into their existing mainstream products and forcibly activate it for tens of millions of users. Here are just a few examples from the last six months:

On the flip side, geeks have rushed to build their own “personal Jarvises” by renting VPS instances or hoarding Mac minis to run the OpenClaw AI agent. Unfortunately, OpenClaw’s security issues with default settings turned out to be so massive that it’s already been dubbed the biggest cybersecurity threat of 2026.

Beyond the sheer annoyance of having something shoved down your throat, this AI epidemic brings some very real practical risks and headaches. AI assistants hoover up every bit of data they can get their hands on, parsing the context of the websites you visit, analyzing your saved documents, reading through your chats, and so on. This gives AI companies an unprecedentedly intimate look into every user’s life.

A leak of this data during a cyberattack — whether from the AI provider’s servers or from the cache on your own machine — could be catastrophic. These assistants can see and cache everything you can, including data usually tucked behind multiple layers of security: banking info, medical diagnoses, private messages, and other sensitive intel. We took a deep dive into how this plays out when we broke down the issues with the AI-powered Copilot+ Recall system, which Microsoft also planned to force-feed to everyone. On top of that, AI can be a total resource hog, eating up RAM, GPU cycles, and storage, which often leads to a noticeable hit to system performance.

For those who want to sit out the AI storm and avoid these half-baked, rushed-to-market neural network assistants, we’ve put together a quick guide on how to kill the AI in popular apps and services.

How to disable AI in Google Docs, Gmail, and Google Workspace

Google’s AI assistant features in Mail and Docs are lumped together under the umbrella of “smart features”. In addition to the large language model, this includes various minor conveniences, like automatically adding meetings to your calendar when you receive an invite in Gmail. Unfortunately, it’s an all-or-nothing deal: you have to disable all of the “smart features” to get rid of the AI.

To do this, open Gmail, click the Settings (gear) icon, and then select See all settings. On the General tab, scroll down to Google Workspace smart features. Click Manage Workspace smart feature settings and toggle off two options: Smart features in Google Workspace and Smart features in other Google products. We also recommend unchecking the box next to Turn on smart features in Gmail, Chat, and Meet on the same general settings tab. You’ll need to restart your Google apps afterward (which usually happens automatically).

How to disable AI Overviews in Google Search

You can kill off AI Overviews in search results on both desktops and smartphones (including iPhones), and the fix is the same across the board. The simplest way to bypass the AI overview on a case-by-case basis is to append -ai to your search query — for example, how to make pizza -ai. Unfortunately, this method occasionally glitches, causing Google to abruptly claim it found absolutely nothing for your request.

If that happens, you can achieve the same result by switching the search results page to Web mode. To do this, select the Web filter immediately below the search bar — you’ll often find it tucked away under the More button.

A more radical solution is to jump ship to a different search engine entirely. For instance, DuckDuckGo not only tracks users less and shows little ads, but it also offers a dedicated AI-free search — just bookmark the search page at noai.duckduckgo.com.

How to disable AI features in Chrome

Chrome currently has two types of AI features baked in. The first communicates with Google’s servers and handles things like the smart assistant, an autonomous browsing AI agent, and smart search. The second handles locally more utility-based tasks, such as identifying phishing pages or grouping browser tabs. The first group of settings is labeled AI mode, while the second contains the term Gemini Nano.

To disable them, type chrome://flags into the address bar and hit Enter. You’ll see a list of system flags and a search bar; type “AI” into that search bar. This will filter the massive list down to about a dozen AI features (and a few other settings where those letters just happen to appear in a longer word). The second search term you’ll need in this window is “Gemini“.

After reviewing the options, you can disable the unwanted AI features — or just turn them all off — but the bare minimum should include:

  • AI Mode Omnibox entrypoint
  • AI Entrypoint Disabled on User Input
  • Omnibox Allow AI Mode Matches
  • Prompt API for Gemini Nano
  • Prompt API for Gemini Nano with Multimodal Input

Set all of these to Disabled.

How to disable AI features in Firefox

While Firefox doesn’t have its own built-in chatbots and hasn’t (yet) tried to force upon users agent-based features, the browser does come equipped with smart-tab grouping, a sidebar for chatbots, and a few other perks. Generally, AI in Firefox is much less “in your face” than in Chrome or Edge. But if you still want to pull the plug, you’ve two ways to do it.

The first method is available in recent Firefox releases — starting with version 148, a dedicated AI Controls section appeared in the browser settings, though the controls are currently a bit sparse. You can use a single toggle to completely Block AI enhancements, shutting down AI features entirely. You can also specify whether you want to use On-device AI by downloading small local models (currently just for translations) and configure AI chatbot providers in sidebar, choosing between Anthropic Claude, ChatGPT, Copilot, Google Gemini, and Le Chat Mistral.

The second path — for older versions of Firefox — requires a trip into the hidden system settings. Type about:config into the address bar, hit Enter, and click the button to confirm that you accept the risk of poking around under the hood.

A massive list of settings will appear along with a search bar. Type “ML” to filter for settings related to machine learning.

To disable AI in Firefox, toggle the browser.ml.enabled setting to false. This should disable all AI features across the board, but community forums suggest this isn’t always enough to do the trick. For a scorched-earth approach, set the following parameters to false (or selectively keep only what you need):

  • ml.chat.enabled
  • ml.linkPreview.enabled
  • ml.pageAssist.enabled
  • ml.smartAssist.enabled
  • ml.enabled
  • ai.control.translations
  • tabs.groups.smart.enabled
  • urlbar.quicksuggest.mlEnabled

This will kill off chatbot integrations, AI-generated link descriptions, assistants and extensions, local translation of websites, tab grouping, and other AI-driven features.

How to disable AI features in Microsoft apps

Microsoft has managed to bake AI into almost every single one of its products, and turning it off is often no easy task — especially since the AI sometimes has a habit of resurrecting itself without your involvement.

How to disable AI features in Edge

Microsoft’s browser is packed with AI features, ranging from Copilot to automated search. To shut them down, follow the same logic as with Chrome: type edge://flags into the Edge address bar, hit Enter, then type “AI” or “Copilot” into the search box. From there, you can toggle off the unwanted AI features, such as:

  • Enable Compose (AI-writing) on the web
  • Edge Copilot Mode
  • Edge History AI

Another way to ditch Copilot is to enter edge://settings/appearance/copilotAndSidebar into the address bar. Here, you can customize the look of the Copilot sidebar and tweak personalization options for results and notifications. Don’t forget to peek into the Copilot section under App-specific settings — you’ll find some additional controls tucked away there.

How to disable Microsoft Copilot

Microsoft Copilot comes in two flavors: as a component of Windows (Microsoft Copilot), and as part of the Office suite (Microsoft 365 Copilot). Their functions are similar, but you’ll have to disable one or both depending on exactly what the Redmond engineers decided to shove onto your machine.

The simplest thing you can do is just uninstall the app entirely. Right-click the Copilot entry in the Start menu and select Uninstall. If that option isn’t there, head over to your installed apps list (Start → Settings → Apps) and uninstall Copilot from there.

In certain builds of Windows 11, Copilot is baked directly into the OS, so a simple uninstall might not work. In that case, you can toggle it off via the settings: Start → Settings → Personalization → Taskbar → turn off Copilot.

If you ever have a change of heart, you can always reinstall Copilot from the Microsoft Store.

It’s worth noting that many users have complained about Copilot automatically reinstalling itself, so you might want to do a weekly check for a couple of months to make sure it hasn’t staged a comeback. For those who are comfortable tinkering with the System Registry (and understand the consequences), you can follow this detailed guide to prevent Copilot’s silent resurrection by disabling the SilentInstalledAppsEnabled flag and adding/enabling the TurnOffWindowsCopilot parameter.

How to disable Microsoft Recall

The Microsoft Recall feature, first introduced in 2024, works by constantly taking screenshots of your computer screen and having a neural network analyze them. All that extracted information is dumped into a database, which you can then search using an AI assistant. We’ve previously written in detail about the massive security risks Microsoft Recall poses.

Under pressure from cybersecurity experts, Microsoft was forced to push the launch of this feature from 2024 to 2025, significantly beefing up the protection of the stored data. However, the core of Recall remains the same: your computer still remembers your every move by constantly snapping screenshots and OCR-ing the content. And while the feature is no longer enabled by default, it’s absolutely worth checking to make sure it hasn’t been activated on your machine.

To check, head to the settings: Start → Settings → Privacy & Security → Recall & snapshots. Ensure the Save snapshots toggle is turned off, and click Delete snapshots to wipe any previously collected data, just in case.

You can also check out our detailed guide on how to disable and completely remove Microsoft Recall.

How to disable AI in Notepad and Windows context actions

AI has seeped into every corner of Windows, even into File Explorer and Notepad. You might even trigger AI features just by accidentally highlighting text in an app — a feature Microsoft calls “AI Actions”. To shut this down, head to Start → Settings → Privacy & Security → Click to Do.

Notepad has received its own special Copilot treatment, so you’ll need to disable AI there separately. Open the Notepad settings, find the AI features section, and toggle Copilot off.

Finally, Microsoft has even managed to bake Copilot into Paint. Unfortunately, as of right now, there is no official way to disable the AI features within the Paint app itself.

How to disable AI in WhatsApp

In several regions, WhatsApp users have started seeing typical AI additions like suggested replies, AI message summaries, and a brand-new Chat with Meta AI button. While Meta claims the first two features process data locally on your device and don’t ship your chats off to their servers, verifying that is no small feat. Luckily, turning them off is straightforward.

To disable Suggested Replies, go to Settings → Chats → Suggestions & smart replies and toggle off Suggested replies. You can also kill off AI Sticker suggestions in that same menu. As for the AI message summaries, those are managed in a different location: Settings → Notifications → AI message summaries.

How to disable AI on Android

Given the sheer variety of manufacturers and Android flavors, there’s no one-size-fits-all instruction manual for every single phone. Today, we’ll focus on killing off Google’s AI services — but if you’re using a device from Samsung, Xiaomi, or others, don’t forget to check your specific manufacturer’s AI settings. Just a heads-up: fully scrubbing every trace of AI might be a tall order — if it’s even possible at all.

In Google Messages, the AI features are tucked away in the settings: tap your account picture, select Messages settings, then Gemini in Messages, and toggle the assistant off.

Broadly speaking, the Gemini chatbot is a standalone app that you can uninstall by heading to your phone’s settings and selecting Apps. However, given Google’s master plan to replace the long-standing Google Assistant with Gemini, uninstalling it might become difficult — or even impossible — down the road.

If you can’t completely uninstall Gemini, head into the app to kill its features manually. Tap your profile icon, select Gemini Apps activity, and then choose Turn off or Turn off and delete activity. Next, tap the profile icon again and go to the Connected Apps setting (it may be hiding under the Personal Intelligence setting). From here, you should disable all the apps where you don’t want Gemini poking its nose in.

How to disable AI in macOS and iOS

Apple’s platform-level AI features, collectively known as Apple Intelligence, are refreshingly straightforward to disable. In your settings — on desktops, smartphones, and tablets alike — simply look for the section labeled Apple Intelligence & Siri. By the way, depending on your region and the language you’ve selected for your OS and Siri, Apple Intelligence might not even be available to you yet.

Other posts to help you tune the AI tools on your devices:

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