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

22 April 2026 at 15:00

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 […]

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

21 April 2026 at 15:00

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 […]

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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.

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.

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

15 April 2026 at 19:34

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.

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.

Building AI defenses at scale: Before the threats emerge

7 April 2026 at 20:02

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.

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.

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

18 March 2026 at 16:57

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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