Since the headline-grabbing outages of 2021, we’ve had recurring conversations with large enterprises asking some version of the same question.

Do we really want our CDN, security, and routing control to live in the same place?

This issue of control has become more urgent after a series of well‑publicized, multi‑hour outages across major cloud‑based DDoS protection and security platforms. These incidents are rare but appear to be increasing in frequency. And when they happen, they expose architectural decisions many organisations haven’t revisited in years. The fact is that architectures assumed providers would never fail. Reality proved them wrong.

The concern isn’t whether cloud DDoS mitigation works. At scale, it does. The issue is control: whether customers retain the ability to reroute traffic independently if the provider itself goes down.

Many DDoS protection services simplify onboarding by originating customer prefixes and returning traffic via static paths. Under normal conditions, this works. During a provider outage, especially one affecting routing or orchestration, customers may lose the ability to reroute traffic
independently. Recovery depends on provider‑side changes at the worst possible moment.

That’s when a DDoS mitigation service can become a single point of failure.

Protection and control are different problems

One thing we consistently hear from network and security teams is that DDoS attack mitigation and traffic control are often treated as the same problem. They aren’t.

Resilient architectures separate them:

The Internet already provides a mechanism to enforce this separation: the Border Gateway Protocol (BGP). This is the Internet’s routing protocol; it determines how traffic is directed between the networks.

So, the real question isn’t whether to use cloud‑based DDoS protection. It’s whether that protection operates with your routing policy, or instead of it.

Resilient architectures treat attack mitigation and traffic control as separate concerns. Providers absorb DDoS attacks. Customers retain routing authority using BGP, enabling them to decide how traffic flows during failures.

When customers control BGP, outages take on a different character. They become routing events, not service outages. Traffic can be redirected faster, the blast radius is reduced, and network teams respond using familiar controls instead of escalation paths.

Designing for the inevitable

No provider is immune to failure. CDNs, hyperscalers, and DDoS mitigation services all operate complex, global control planes.

Resilience doesn’t come from assuming outages won’t happen. It comes from designing so that when they do, customers still control the outcome.

That’s why more organizations are adopting architectures where:

• DDoS protection is cloud‑delivered
• Routing authority remains customer‑owned
• BGP is the final decision layer for traffic steering

This approach preserves the benefits of cloud‑scale mitigation while avoiding the creation of new single points of failure.

A practical next step

If you’re rethinking your DDoS architecture, your best starting point isn’t a product demo; it’s an architectural review. Here are some questions to ask yourself:

• Who originates your prefixes today?
• How quickly can you reroute traffic if a provider is unavailable?
• What dependencies exist between mitigation availability and network availability?

Those answers usually reveal more than any outage postmortem.

On the Internet, control of routing is control of availability, and we think that control should always remain in customer’s hands.

Want to discuss what customer‑controlled DDoS protection looks like in practice? Get in touch with Thales to review your architecture.

The post When your DDoS mitigation provider goes down: Why traffic control can’t be outsourced appeared first on Blog.

A Pivotal Moment for National Security

As the digital landscape undergoes profound shifts, the recently released National Cyber Strategy provides the essential foundation for enduring American leadership. By prioritizing the disruption of hostile actors, future-proofing networks, accelerating quantum readiness, and securing the AI frontier, the strategy provides the strategic clarity necessary to protect our digital way of life from sophisticated adversaries. Palo Alto Networks commends National Cyber Director Sean Cairncross for his leadership and looks forward to working with the administration to operationalize this strategy.

Each pillar of the strategy galvanizes meaningful action to advance our collective defense:

Shape Adversary Behavior (Pillar 1)

This signals a decisive shift toward the proactive disruption of malicious actors. The Trump Administration has made clear that the U.S. Government should impose real costs on adversaries to change their behavior. While the private sector is already executing discrete disruptions against malicious actors, coordination has historically been fragmented. The strategy identifies that increased collaboration with private sector entities, who possess unique insight into adversary behavior, can in turn enable more impactful deterrence.

Promote Common Sense Regulation (Pillar 2)

The strategy appropriately recognizes that complexity is the enemy of security. A focus on measurable improvements in cyber outcomes (versus check-the-box compliance exercises) collectively makes us all safer. While much attention is rightfully paid toward harmonizing incident reporting requirements, which Palo Alto Networks wholeheartedly supports, let’s not stop there. The federal government can lead by example by consolidating and streamlining federal government software compliance certifications. For example, there should be logical reciprocity between FedRAMP High and DoW IL-5 certifications.

Modernize and Secure Federal Government Networks (Pillar 3)

In addition to the necessary attention on AI-powered cyber defense, cloud security and zero trust network architecture, Palo Alto Networks applauds the discrete focus on quantum-safe security ahead of “Q-Day,” the point where quantum computing capabilities will compromise legacy public key encryption that has underpinned cybersecurity for decades. As Federal CISO Mike Duffy recently stated, "Modernization without considering PQC readiness or cryptographic agility is really creating technical debt in the future, something that we don’t want to see ever.”

To address this challenge, Palo Alto Networks provides a structured quantum-safe framework organized into four stages:

• Continuous Discovery – Automating ecosystem ingestion to identify cryptographic dependencies.
• Risk Assessment & Prioritization – Evaluating vulnerabilities to establish a data-driven remediation roadmap.
• Comprehensive Remediation – Executing the transition to post-quantum algorithms across the architecture.
• Governance & Crypto-Hygiene – Maintaining long-term visibility and management.

The bottom line is that 2035 is too late. Quantum readiness must accelerate today, and this strategy will set a critical North Star to drive the necessary urgency.

Secure Critical Infrastructure (Pillar 4)

Critical infrastructure resilience is central to our homeland security, economic security, public health and safety. Unfortunately, critical infrastructure entities are increasingly under assault from emboldened cyber adversaries.

In fact, Palo Alto Networks research shows some form of operational disruption in up to 86% of major cyber incidents. Our 2026 Global Incident Response Report underscores another sobering reality: These entities are under assault from all angles. In 87% of cyber incidents, attacks targeted multiple attack surfaces, which spanned the network, cloud, endpoints and identity.

Recognizing that you can’t secure what you can’t see, we need a national-level effort to identify, prioritize and harden the critical infrastructure that the American people depend upon. This strategy puts an important marker in the ground to revitalize those efforts.

Sustain Superiority in Critical and Emerging Technologies (Pillar 5)

Palo Alto Networks was pleased to see the strategy reinforces the core tenets of the AI Action Plan, emphasizing that "secure-by-design" principles for AI technologies are non-negotiable and that AI adoption and AI security can and must be inexorably linked.

Enterprises should be able to deploy AI confidently without fear of data leakage, model tampering or rogue AI agents. However, despite our research showing an 88% success rate of “jailbreaking” techniques against widely deployed AI models, only 6% of organizations currently have an AI security strategy. It’s time to flip this paradigm and put defenders back in the driver’s seat in this AI-first moment.

To support this emerging consensus around the importance of promoting AI security, we developed the Secure AI by Design Policy Roadmap. This framework provides a four-part construct to evaluate the evolving dimensions of threats to AI systems. Palo Alto Networks is also proud to make its comprehensive AI security suite, Prisma^® AIRS, available to all federal agencies at substantial discounts through GSA’s OneGov Initiative.

Build Talent and Capacity (Pillar 6)

Recognizing America’s cyber workforce as a “strategic asset,” the strategy calls for a pragmatic and accessible pipeline for developing talent. The explicit recognition that we should take advantage of existing avenues across government, industry and academia is important. For example, Palo Alto Networks is proud of the impact of its Cybersecurity Academy – that provides free, NIST Framework-aligned curricula covering essential domains, such as cybersecurity fundamentals, enterprise and network security, cloud security, security operations and the AI/cybersecurity nexus.

Resources like this, and those for other entities, can form the basis of a renewed focus on cyber talent development.

Turning Strategic Vision Into Action

Palo Alto Networks views itself as more than a cybersecurity vendor. We see ourselves as an integrated national security partner of the federal government at a moment when defending our digital way of life demands all of us working together. To that end, we are ready to do our part to turn strategic vision into action.

This strategy should be applauded. Let’s roll up our sleeves and get to work.

The post How the National Cyber Strategy Secures Our Digital Way of Life appeared first on Palo Alto Networks 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:

• Microsoft is forcibly turning compatible Windows PCs into “AI PCs”, and automatically installing and activating Copilot for anyone with Microsoft 365 desktop apps.
• Google activated Gemini for all U.S. Chrome users, cranked its browser functionality to the max, aggressively expanded the reach of AI Overviews in search results, and baked a whole suite of AI features into its online services (Gmail, Google Docs, and others).
• Apple integrated its own Apple Intelligence (conveniently sharing the AI acronym) into the latest OS versions across all device types and most of its native apps.
• Meta added AI translations and a Meta AI chat to WhatsApp, while simultaneously banning third-party chatbots in the messaging app as of January 15, 2026.

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.

• For more on dealing with preloaded and system apps, check out our post, Delete the undeletable: how to disable and remove Android bloatware.

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:

• Privacy settings in ChatGPT
• DeepSeek: configuring privacy and deploying a local version
• The pros and cons of AI-powered browsers
• Is a Gemini AI update about to kill privacy on your Android device?
• Should you disable Microsoft Recall in 2025?

One email was all it took. An employee clicked what looked like a routine sign‑in request. Behind the scenes, attackers swiped credentials, slipped past security controls, impersonated a trusted user, and gained access to critical systems. In other cases, similar intrusions delayed paychecks, rerouted invoices, stole sensitive data, locked up entire networks, interrupted patient care, and strained already tight budgets at schools and critical services. 

Those attacks were powered by Tycoon 2FA. Today, Microsoft, Europol, and industry partners announced a coordinated action to disrupt the service responsible for tens of millions of fraudulent emails reaching over 500,000 organizations each month worldwide. 

Disrupting a global phishing operation 

Active since at least 2023, Tycoon 2FA enabled thousands of cybercriminals to impersonate real users and gain unauthorized access to email and online service accounts, including Microsoft 365, Outlook, and Gmail. Unlike traditional phishing kits, Tycoon 2FA was designed to defeat additional security protections, including multifactor authentication, allowing cybercriminals to log in as legitimate users without triggering alerts, even on protected accounts. 

Acting under a court order from the U.S. District Court for the Southern District of New York, and for the first time in coordination with Europol’s Cyber Intelligence Extension Programme (CIEP), Microsoft seized 330 active domains that powered Tycoon 2FA’s core infrastructure, including control panels and fraudulent login pages. The CIEP framework brought public‑ and private‑sector partners together to move from simply sharing intelligence to coordinated, cross‑border action, accelerating disruption and limiting further harm. 

Taking this infrastructure offline cuts off a major pipeline for account takeovers and helps protect people and organizations from follow‑on attacks such as data theft, ransomware, business email compromise, and financial fraud. 

The scale and real‑world impact of Tycoon 2FA 

By mid‑2025, Tycoon 2FA accounted for approximately 62 percent of all phishing attempts Microsoft blocked, including more than 30 million emails in a single month. That placed Tycoon 2FA among the largest phishing operations globally.  

Despite extensive defenses, the service is linked to an estimated 96,000 distinct phishing victims worldwide since 2023, including more than 55,000 Microsoft customers.  

Healthcare and education organizations were hit hardest. More than 100 members of Health‑ISAC, a global threat-sharing group for the health sector and a co-plaintiff in this case, were successfully phished. In New York alone, at least two hospitals, six municipal schools, and three universities faced attempted or successful compromise through Tycoon 2FA. These incidents had tangible consequences: disrupted operations, diverted resources, and delayed patient care.  

Why Tycoon 2FA was so dangerous 

Tycoon 2FA combined convincing phishing templates, realistic landing pages, and real‑time capture of credentials and authentication codes into an easy‑to‑use package that scaled quickly. By lowering the technical barrier to entry, it allowed criminals with limited expertise to run sophisticated impersonation campaigns. 

With each successful phishing victim, attackers could operate with the same level of trust as legitimate users moving laterally across systems, accessing sensitive data, and abusing sign‑on connections without raising alarms. Research from Microsoft Threat Intelligence provides more details on how Tycoon 2FA operated. 

This shift reflects a broader trend in cybercrime: identity, not infrastructure, has become the primary target. A single compromised account can now unlock banking systems, healthcare portals, workplace applications, and social media accounts. 

Inside the impersonation economy

Tycoon 2FA operated like a business within the broader impersonation‑for‑hire ecosystem. The primary developer, Saad Fridi, who is believed to be based in Pakistan, worked alongside partners responsible for marketing, payments, and technical support. 

Cybercriminals typically used Tycoon 2FA alongside other illicit services. While Tycoon 2FA captured credentials and session tokens, other services handled mass email delivery, malware distribution, hosting, and access monetization. For example, RedVDS, disrupted by Microsoft in January 2026, provided inexpensive virtual computers, which cybercriminals paired with Tycoon 2FA to deliver phishing campaigns. Together, these different services created an interconnected ecosystem for identity‑based attacks. Disrupting one component can have cascading effects across the cybercrime economy. 

Sustained pressure reshapes the market 

Over the past 18 months, Microsoft’s Digital Crimes Unit has targeted multiple services that enable impersonation and initial access, including extensive disruption operations of Lumma Stealer, RaccoonO365, Fake ONNX (aka “Caffeine”), and RedVDS. 

When widely used tools are disrupted, attackers are forced to adapt, often shifting to alternatives like Tycoon 2FA. This substitution pattern shows how sustained pressure prevents any single service from remaining dominant while steadily raising the cost and risk of cybercrime. 

These efforts have led to arrests in Egypt and Nigeria, complete service shutdowns, infrastructure loss, and reputational damage for operators beyond law‑enforcement reach. RedVDS alone lost more than 95 percent of its infrastructure since January 2026, significantly degrading its ability to support mass impersonation campaigns and other online scams. 

As pressure increased, many operators tightened access controls, retreated into closed channels, or shut down entirely to avoid legal action. In Tycoon 2FA’s case, Microsoft could not purchase access to the service; the operator rejected attempts by our investigators, requiring a trusted intermediary. In fact, Tycoon 2FA’s operator and the now‑arrested developer of RaccoonO365 communicated with one another, highlighting the ecosystem’s interdependence and how disruptions in one area influence activity elsewhere. 

Global threats require global action 

Cybercrime operates across borders, and effective response must do the same. Disrupting Tycoon 2FA spanned multiple jurisdictions, underscoring why sustained, coordinated pressure is essential, especially as cybercrime becomes more scalable through automation and AI. 

Microsoft Threat Intelligence, joining many security researchers, identified Tycoon 2FA as one of the most significant threats to identity-based attacks. Microsoft’s Digital Crimes Unit consulted with Europol, which also tracked the actor based on intelligence supplied by TrendAI. Through the CIEP, Europol convened partners to take action. Microsoft worked with industry partners to pursue a coordinated infrastructure disruption, while law enforcement authorities in Latvia, Lithuania, Portugal, Poland, Spain, and the United Kingdom conducted seizures of infrastructure and carried out other operational measures linked to Tycoon 2FA. 

Industry partners, including Proofpoint, Intel 471, and eSentire, expanded visibility through telemetry, threat intelligence, and criminal‑forum insight. Cloudflare assisted by taking down infrastructure outside U.S. jurisdiction, while Health‑ISAC quantified impacts on healthcare organizations. SpyCloud contributed key victimology data, Resecurity facilitated access to Tycoon 2FA, and Coinbase helped trace the movement of stolen funds. Finally, the Shadowserver Foundation supported notifications to more than 200 computer emergency response teams worldwide, helping limit further harm. 

No single organization could have assembled this full picture alone.

Sustaining pressure, together 

Stopping identity‑based cybercrime requires action across individuals, organizations, and governments. Multifactor authentication, scrutiny of unexpected messages, strong session controls, and coordinated threat‑sharing all reduce risk. Early enforcement matters too; it prevents small intrusions from escalating into systemic harm. Microsoft will continue applying the lessons learned from Tycoon 2FA and prior disruptions to fragment the impersonation economy, limit scale, and make cybercrime riskier and less profitable. 

The post Defending the gates: How a global coalition disrupted Tycoon 2FA, a major driver of initial access and large-scale online impersonation appeared first on Microsoft On the Issues.

Can a computer be infected with malware simply by processing a photo — particularly if that computer is a Mac, which many still believe (wrongly) to be inherently resistant to malware? As it turns out, the answer is yes — if you’re using a vulnerable version of ExifTool or one of the many apps built based on it. ExifTool is a ubiquitous open-source solution for reading, writing, and editing image metadata. It’s the go-to tool for photographers and digital archivists, and is widely used in data analytics, digital forensics, and investigative journalism.

Our GReAT experts discovered a critical vulnerability — tracked as CVE-2026-3102 — which is triggered during the processing of malicious image files containing embedded shell commands within their metadata. When a vulnerable version of ExifTool on macOS processes such a file, the command is executed. This allows a threat actor to perform unauthorized actions in the system, such as downloading and executing a payload from a remote server. In this post, we break down how this exploit works, provide actionable defense recommendations, and explain how to verify if your system is vulnerable.

What is ExifTool?

ExifTool is a free, open-source application addressing a niche but critical requirement: it extracts metadata from files, and enables the processing of both that data and the files themselves. Metadata is the information embedded within most modern file formats that describes or supplements the main content of a file. For instance, in a music track, metadata includes the artist’s name, song title, genre, release year, album cover art, and so on. For photographs, metadata typically consists of the date and time of a shot, GPS coordinates, ISO and shutter speed settings, and the camera make and model. Even office documents store metadata, such as the author’s name, total editing time, and the original creation date.

ExifTool is the industry leader in terms of the sheer volume of supported file formats, as well as the depth, accuracy, and versatility of its processing capabilities. Common use cases include:

• Adjusting dates if they’re incorrectly recorded in the source files
• Moving metadata between different file formats (from JPG to PNG and so on)
• Pulling preview thumbnails from professional RAW formats (such as 3FR, ARW, or CR3)
• Retrieving data from niche formats, including FLIR thermal imagery, LYTRO light-field photos, and DICOM medical imaging
• Renaming photo/video (etc.) files based on the time of actual shooting, and synchronizing the file creation time and date accordingly
• Embedding GPS coordinates into a file by syncing it with a separately stored GPS track log, or adding the name of the nearest populated area

The list goes on and on. ExifTool is available both as a standalone command-line application and an open-source library, meaning its code often runs under the hood of powerful, multi-purpose tools; examples include photo organization systems like Exif Photoworker and MetaScope, or image processing automation tools like ImageIngester. In large digital libraries, publishing houses, and image analytics firms, ExifTool is frequently used in automated mode, triggered by internal enterprise applications and custom scripts.

How CVE-2026-3102 works

To exploit this vulnerability, an attacker must craft an image file in a certain way. While the image itself can be anything, the exploit lies in the metadata — specifically the DateTimeOriginal field (date and time of creation), which must be recorded in an invalid format. In addition to the date and time, this field must contain malicious shell commands. Due to the specific way ExifTool handles data on macOS, these commands will execute only if two conditions are met:

• The application or library is running on macOS
• The -n (or –printConv) flag is enabled. This mode outputs machine-readable data without additional processing, as is. For example, in -n mode, camera orientation data is output simply, inexplicably, as “six”, whereas with additional processing, it becomes the more human-readable “Rotated 90 CW”. This “human-readability” prevents the vulnerability from being exploited

A rare but by no means fantastical scenario for a targeted attack would look like this: a forensics laboratory, a media editorial office, or a large organization that processes legal or medical documentation receives a digital document of interest. This can be a sensational photo or a legal claim — the bait depends on the victim’s line of work. All files entering the company undergo sorting and cataloging via a digital asset management (DAM) system. In large companies, this may be automated; individuals and small firms run the required software manually. In either case, the ExifTool library must be used under the hood of this software. When processing the date of the malicious photo, the computer where the processing occurs is infected with a Trojan or an infostealer, which is subsequently capable of stealing all valuable data stored on the attacked device. Meanwhile, the victim could easily notice nothing at all, as the attack leverages the image metadata while the picture itself may be harmless, entirely appropriate, and useful.

How to protect against the ExifTool vulnerability

GReAT researchers reported the vulnerability to the author of ExifTool, who promptly released version 13.50, which is not susceptible to CVE-2026-3102. Versions 13.49 and earlier must be updated to remediate the flaw.

It’s critical to ensure that all photo processing workflows are using the updated version. You should verify that all asset management platforms, photo organization apps, and any bulk image processing scripts running on Macs are calling ExifTool version 13.50 or later, and don’t contain an embedded older copy of the ExifTool library.

Naturally, ExifTool — like any software — may contain additional vulnerabilities of this class. To harden your defenses, we also recommend the following:

• Isolate the processing of untrusted files. Process images from questionable sources on a dedicated machine or within a virtual environment, strictly limiting its access to other computers, data storage, and network resources.
• Continuously track vulnerabilities along the software supply chain. Organizations that rely on open-source components in their workflows can use Open Source Software Threats Data Feed for tracking.

Finally, if you work with freelancers or self-employed contractors (or simply allow BYOD), only allow them to access your network if they have a comprehensive macOS security solution installed.

Still think macOS is safe? Then read about these Mac threats:

• Banshee: A stealer targeting macOS users
• Are Macs safe? Threats to macOS users
• Infostealer has entered the chat
• AirBorne: Attacks on Apple devices through vulnerabilities in AirPlay
• Hacking Android, macOS, iOS, and Linux through a Bluetooth vulnerability

The Pivot to Large-Scale Intelligence

For decades, Telecommunications Service Providers have been the central nervous system of the global economy, tasked with a singular, critical mission: connecting people.

The industry spent vast amounts of capital building networks that moved voice, then text and finally high-speed mobile data. We succeeded. According to GSMA's most recent report, there are 5.8 billion unique subscriptions. The world is connected.

But the mission is changing fast. We are no longer just moving data; we are now expected to host intelligence.

Today’s enterprises are drowning in data and desperate for AI-led capabilities to analyze and process the information. They are struggling with the immense capital costs, the scarcity of GPUs, and complex data sovereignty regulations that make public cloud options difficult for sensitive workloads.

We are no longer living in the communications age, or the internet age, or the social network era, not even in the generative AI era. We are entering the Agentic Era. In this new era, data is the raw resource, and AI agents and models are the machinery that refines it into value. The infrastructure required to do this – from massive data ingestion to complex training and high-volume real-time inference – is called the "AI Factory.”

And these AI factories are not being designed for human-speed operations, but rather for machine-speed operations.

This creates a generational opportunity for telecommunications service providers (SP). By building new (or transforming existing) data centers and edge locations into AI factories, SPs can offer hosted AI services that are high-performance, low-latency and compliant with regional requirements.

However, building an AI factory isn't just about racking GPUs. It is about realizing that an AI infrastructure presents a fundamentally new threat landscape that legacy security cannot handle. If the SP’s AI factory is compromised (if models are poisoned, identities hijacked, training data exfiltrated) the damage to reputation and national infrastructure is incalculable.

To capture the AI opportunity, service providers need more than computing power; they need a blueprint for a secure AI architecture. At Palo Alto Networks, we view the security of the AI factory as a three-tiered layer cake, requiring holistic, integrated protection from the physical infrastructure up to the AI agents themselves.

The AI Threat Model Is a Structural Shift

For service providers building AI Factories, the challenge is not simply adding another workload to the data center. AI changes the risk equation entirely. It introduces new traffic patterns, new identities and new forms of autonomy that traditional network and core security architectures were never designed to govern.

• Data Gravity Becomes Attack Surface: AI training and inference environments ingest massive volumes of data from distributed enterprise customers, partners and edge environments. This scale creates a new exposure layer. Malicious payloads, embedded model manipulation, and command-and-control traffic can hide within high-throughput AI data flows. Inspection models built for deterministic traffic patterns struggle when confronted with dynamic, AI-driven pipelines.
• Non-Human Identities at Scale: An AI Factory is more than just infrastructure; it will be populated by autonomous agents. These agents retrieve data, call APIs, invoke tools and trigger workflows across networks and cloud environments. They require elevated privileges to function. For service providers, this means managing not just subscriber identities, but fleets of machine identities operating with delegated authority.
• Agentic and Adversarial Threats: Attackers are also operationalizing AI. They probe for weaknesses faster, automate exploitation and increasingly target the AI systems themselves. Prompt injection can redirect an agent’s mission. Data poisoning can subtly degrade model integrity. Rogue agents can be manipulated to access external tools or escalate privileges. These are not traditional perimeter attacks; they are attacks on reasoning, behavior and autonomy.

For service providers offering AI-as-a-Service, the implication is clear: Securing the AI Factory requires more than network defense. It requires real-time governance of models, agents and data flows, ensuring that autonomous systems operate within defined policy boundaries while maintaining performance and scale.

The Foundation — Securing the High-Performance Infrastructure

The base of our cybersecurity stack is the physical and virtual infrastructure of the AI factory itself. This is a high-stakes environment. In a multitenant SP data center, you might have a financial institution fine-tuning a fraud detection model on one rack, and a government agency running inference on satellite imagery on the next. The barriers between these tenants must be absolute.

Foundational cybersecurity has two critical components: perimeter defense and internal segmentation.

The ML-Powered Perimeter

The front door of the AI factory must handle unprecedented throughput while performing deep inspection. Traditional firewalls, relying on static signatures, become bottlenecks and fail to catch novel threats hidden in massive data streams.

Palo Alto Networks addresses this with our flagship ML-Led Next-Generation Firewalls (NGFW). We have embedded machine learning directly into the core of the firewall. Instead of waiting for a patient zero to be identified and a signature created, our NGFWs analyze traffic patterns in real-time to identify and block unknown threats instantly. For an SP, this means you can provide the massive bandwidth required for AI data ingestion without compromising on security inspection at the edge.

Zero Trust Segmentation Inside the Factory

The perimeter is just the start. Once inside the data center, the biggest risk is the lateral movement threats and malware. If an attacker compromises a low-security tenant or a peripheral IoT device, they must not be able to jump to the sensitive GPU clusters or the model storage arrays.

In an AI factory, workloads are highly dynamic and virtualized. We provide robust segmentation across both hardware and software environments. We can enforce granular policies between virtual instances, containers and different stages of the AI pipeline (e.g., isolating training environments from inference operations). This allows a breach in one segment to be contained instantly, protecting the integrity of the entire factory.

The Engine – Securing AI Agents, Apps and Identities

The middle layer of the security stack is where the actual "work" of AI happens – the models, the LLMs, the agents. This is the newest frontier of cybersecurity and where traditional tools are most deficient.

This layer faces two distinct challenges: Protecting the integrity of the AI interaction and managing the identities of the nonhuman actors.

Securing AI Apps and Agents

As enterprises evolve from standalone LLMs to agentic AI systems that reason, call tools, access data, and take action across workflows, the challenge is no longer just what a model says; it is what an AI agent does.

How do you validate that an LLM powering your AI factory does not expose sensitive information, and that autonomous agents cannot be manipulated through jailbreak prompts, tool injection or malicious instructions? How do you prevent an AI agent from accessing unauthorized systems, escalating privileges, or executing unintended actions?

This is the role of Prisma^® AIRS – our security and governance platform for AI agents, apps, models and data. Prisma AIRS operates directly in the execution path of AI applications and autonomous agents. It enforces policy in real time, validates agent behavior, and blocks prompt injection, model manipulation and agent hijacking before they can impact the business.

Beyond filtering outputs, Prisma AIRS governs agent communications, tool access and data flows to prevent credential leakage, mission drift and unauthorized actions. For service providers delivering AI-as-a-Service, or enterprises deploying AI agents internally, Prisma AIRS enables integrity, compliance and continuous control as intelligent systems move from experimentation into mission-critical operations.

Built in alignment with emerging standards like the OWASP Agentic Top 10 Survival Guide, Prisma AIRS operationalizes best practices to defend against real-world agentic threats.

Governing Nonhuman Identity

Perhaps the most profound shift in the AI factory is who or what is doing the work. We are rapidly moving toward ecosystems of autonomous AI Agents. These agents need to authenticate to databases, authorize API calls to other services, and access privileged information just like a human employee.

If an attacker steals the credentials of a high-privilege AI agent, they own the factory.

This is why the Palo Alto Networks acquisition of CyberArk, the global leader in Identity Security, is so strategic for the AI era. CyberArk specializes in protecting privileged access, and crucially managing nonhuman identities. By integrating CyberArk’s capabilities, we can ensure that every AI agent operating within the SP’s factory is robustly authenticated, authorized for minimum necessary access, and its activities are monitored. We are securing the new digital workforce.

The Overwatch – Holistic, AI-Driven Threat Management

The top layer of the stack is about visibility and speed. An AI factory generates a deafening amount of telemetry data from networks, endpoints, clouds and identity systems. No human security operations center (SOC) can sift through this noise manually to find a sophisticated attack.

To fight AI-driven threats, you need AI-driven defense.

This is the role of Cortex^®, our flagship platform for holistic threat management. Cortex is designed to ingest billions of data points from across the entire Palo Alto Networks product portfolio and hundreds of types of third-party equipment, normalizing it into a single source of truth.

Cortex applies advanced AI and machine learning to this vast data lake to detect anomalies that signal a complex attack spanning different threat vectors. It might correlate an unusual login event from an AI agent (detected by the identity layer) with a subtle change in outbound traffic patterns at the firewall (layer 1), recognizing it as data exfiltration in progress.

For a Service Provider, Cortex provides the "single pane of glass" view over their entire AI factory operations, allowing them to detect, investigate and automatically respond to threats at machine speed, vastly reducing Mean Time to Respond (MTTR).

Building the Trust Foundation for the Agentic Era

The transition to becoming an AI factory is a necessary evolution for Service Providers seeking growth in the coming decade. Your ability to offer localized, sovereign, high-performance AI services will differentiate you from those who large-scale and cement your role as an indispensable partner to enterprises and governments.

But this opportunity is inextricably linked to trust. Your customers will not move their most sensitive data and IP into your AI factory unless they are certain it is secure against modern threats.

Security cannot be an afterthought bolted onto an AI infrastructure. It must be woven into the fabric of the factory, from the silicon to the software agents. By adopting a layered approach (securing the high-performance infrastructure with ML-led NGFWs, protecting models and identities with Prisma AIRS and CyberArk, while managing the entire landscape with Cortex) Service Providers can build the trusted foundations the AI era demands.

This week we’ll be at Mobile World Congress talking about our security platform for AI Factories, along with five solutions and ecosystem partners. Come see us at in Hall 4, Stand #4D55.

The post Why Service Providers Must Become Secure AI Factories appeared first on Palo Alto Networks Blog.

AI agents have traditionally faced three core limitations: they can’t retain learned information or operate autonomously beyond short periods, and they require constant supervision. AWS addresses these limitations with frontier agents—a new category of AI that performs complex reasoning, multi-step planning, and autonomous execution for hours or days. Multi-agent collaboration has emerged as a powerful approach that helps tackle complex workflows that require multiple steps and diverse expertise—such as in software development where agents handle code generation, review, and testing; in scientific research where agents collaborate on literature review, experimental design, and data analysis; and in cybersecurity where specialized agents perform reconnaissance, vulnerability analysis, and exploit validation.

In this post, we discuss how we’ve used this technology to deliver automated penetration testing, something that can traditionally take weeks and is resource intensive. We also provide a technical deep-dive into the architecture of the penetration testing component built into AWS Security Agent.

The concept of automated security testing isn’t new—penetration testing tools and vulnerability scanners have existed for decades. However, with recent advancements in large language models (LLMs), frontier agents are designed to reason about application behavior, adapt strategies based on feedback, and understand context in ways that traditional tools can’t. By creating a network of specialized agents, we can address increasingly complex security challenges: one agent maps the attack surface while others analyze business logic flaws, validate findings, and prioritize vulnerabilities based on actual exploitability. The exploitability context comes from the combination of actual exploit attempts by swarm agent workers, independent re-validation by specialized validators, and LLM-driven scoring according to the common vulnerability scoring system (CVSS).

We’ve developed automated penetration testing for the AWS Security Agent. This capability includes a multi-agent penetration testing system that orchestrates specialized security agents to work collaboratively on vulnerability detection. The system begins with multiple types of scanning to establish baseline coverage, then conducts broad reconnaissance using static, predefined tasks to map the application surface and identify initial attack vectors. Building on these findings, our agentic system dynamically generates focused test tasks tailored to the specific application context—reasoning about discovered endpoints, business logic patterns, and potential vulnerability chains to create targeted security tests that adapt based on application responses. By combining these specialized capabilities, the system can tackle complex security scenarios across major risk categories. Beyond single-vulnerability detection, the system performs complex chained attacks—for instance, combining an information disclosure flaw with privilege escalation to access sensitive resources, or chaining insecure direct object references (IDOR) with authentication bypass.

Figure 1: Diagram of the AWS Security Agent penetration testing component.

System architecture

This section describes the major components of the system. The following subsections cover authentication and initial access, baseline scanning, multi-phased exploration with the specialized agent swarm, and validation with report generation.

Authentication and initial access

The system begins with an intelligent sign-in component that handles authentication across diverse application architectures. This component combines LLM-based reasoning with deterministic mechanisms to locate sign-in pages, attempt provided credentials, and maintain authenticated sessions for subsequent testing phases. The approach adapts to different application structures and target environments automatically and uses a browser tool. The developer can optionally provide a custom sign-in prompt tailored to the target application.

Baseline scanning phase

Following authentication, the system initiates comprehensive baseline scanning through parallel execution of specialized scanners. For black-box testing, the network scanner conducts automated web application security testing, generating raw traffic interactions and identifying candidate vulnerable endpoints. In white-box settings, the code scanner additionally performs deep source code analysis when repositories are available, producing descriptive documentation across multiple categories. Additional specialized scanners complement these capabilities to identify vulnerabilities across multiple dimensions and establish initial security coverage.

Multi-phased exploration

The system employs two distinct exploration approaches that work in concert. Managed execution operates with predefined static tasks across major risk categories like cross-site scripting, insecure direct object reference, privilege escalation, and so on. This component systematically helps ensure comprehensive coverage by executing curated tasks for each risk type. In the next phase, guided exploration takes a dynamic, intelligence-driven approach. This component ingests discovered endpoints, validated findings, and code analysis documentation to reason about application-specific attack opportunities. It operates in two stages: first generating a contextual penetration testing plan by identifying unexplored resources and potential vulnerability chains, then programmatically managing the execution of these dynamically generated tasks. The guided explorer runs with adaptive tasks that evolve based on application responses and discovered patterns.

Specialized agent swarm
Both exploration approaches dispatch work to specialized swarm worker agents—each configured for specific risk types and equipped with comprehensive penetration testing toolkits including code executors, web fuzzers, NVD vulnerability database search for Common Vulnerabilities and Exposures (CVE) intelligence, and vulnerability-specific tools. These workers execute assigned tasks with timeout management and structured reporting.

Validation and report generation

When specialized agents identify potential security risks, they generate structured reports containing the vulnerability type, affected endpoints, exploitation evidence, and technical context. However, automated penetration testing faces a critical challenge: LLM agents can produce plausible-sounding findings that require rigorous validation. Candidate findings undergo validation through both deterministic validators and specialized LLM-based agents that attempt active exploitation. We employ assertion-based validation techniques where natural language assertions written by security experts encode deep knowledge about real attack behaviors, requiring explicit, structured proof that’s significantly harder to circumvent than narrow deterministic checks. Validated findings undergo Common Vulnerability Scoring System (CVSS) analysis for severity assessment, then are synthesized into final reports with validation results, severity scores, and exploitation evidence—designed to deliver actionable, high-confidence vulnerabilities for effective remediation.

Benchmarking

To evaluate our system, we performed human evaluation in addition to automatic benchmarking. We conducted analysis on real-world trajectories and created a taxonomy of error patterns. By spotting frequent error patterns, we were able to iterate on our solution. We report results on the CVE Bench public benchmark, which is a collection of vulnerable web applications containing 40 critical-severity CVEs from the National Vulnerability Database used to evaluate AI agents on real-world exploits. Each application includes automatic exploit references, and LLM-based agents attempt to execute attacks that trigger the vulnerabilities.

We measure success through the attack success rate (ASR) metric, defined as the rate of successful exploitation of application vulnerabilities. CVE Bench uses a grader that the agent can query to verify exploit success and provides explicit capture-the-flag (CTF) instructions. We evaluate in three configurations:

1. With CTF instructions and grader checks after each tool call, achieving 92.5% on CVE Bench v2.0 (we note that some challenges involve blind exploitation where the agent cannot verify success without this feedback).
2. Without CTF instructions or grader feedback, achieving 80%—which better reflects real-world conditions where the agent must self-validate through observable outcomes. We also observed that the agent was able to identify some CVEs based on the LLM’s parametric knowledge, as shown in the following bash command where the model explicitly references a CVE by name.
3. Therefore, we ran an additional experiment using an LLM whose knowledge cutoff date predates CVE Bench v1.0 release, achieving 65% ASR.

The following code example shows an LLM agent demonstrating parametric knowledge of CVE-2023-37999 from its training data, then issuing a bash command to check exploitation prerequisites.

# HT Mega 2.2.0 has a known vulnerability – CVE-2023-37999
# It has an unauthenticated privilege escalation via the REST API settings endpoint
# Let's check if registration is enabled
curl -s http://target:9090/wp-login.php?action=register -I | head -10

We’re committed to pushing the frontier of security vulnerability detection by continuously evaluating our agent and staying competitive with newer, more challenging benchmarks.

Optimizing testing and compute budget

One challenge for penetration testing is determining the balance between exploitation and exploration. Using a depth-first approach can waste too much compute on specific directions, leading to lower vulnerability coverage under a fixed compute budget. Compare that to breadth-first search, which is unlikely to discover deep vulnerabilities that require testing multiple approaches. Therefore, a balance between the two approaches is needed to maximize coverage for a given compute budget. Our proposed system design aims to include a hybrid approach. A more efficient dynamic solution that generalizes across various vulnerabilities and different web applications remains an open research question.

Another challenge with penetration testing is non-determinism. Because of the underlying LLMs, the output of penetration test runs can vary from one run to another. Having different findings across multiple runs can lead to confusion. One option to mitigate this is to perform multiple runs and consolidate the findings across them.

Conclusion

The multi-agent architecture presented in this post demonstrates how you can use specialized agents that can collaborate to tackle complex penetration testing workflows—from intelligent authentication and baseline scanning through managed and guided exploration phases, culminating in rigorous validation. By orchestrating these specialized components with adaptive task generation and assertion-based validation, the system delivers comprehensive security coverage that evolves based on application-specific context and discovered patterns.

AWS Security Agent is now in public preview, for more information, see Getting Started with AWS Security Agent.

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

See the agentic SOC come to life at Cortex^® Symphony 2026, the ultimate SOC event.

Today, the Cortex^® platform takes a massive step toward delivering the perfect union of human expertise and agentic AI across all of security operations. Our latest release embeds immersive, context-aware agentic AI across the platform, from code to cloud to SOC, delivering an agentic-first analyst experience for our customers.

With new Cortex AgentiX agents built to tackle more use cases and an expanded AI-ready data foundation, this release slashes response times and redefines what high-efficiency SOC operations look like.

Attack Velocity Has Fundamentally Changed

Not long ago, adversaries took days to move from initial access to impact. Today, they weaponize AI across the attack lifecycle to operate up to 4x faster than just one year ago, executing end-to-end attacks in as little as 72 minutes, according to Unit 42^® research.

These attacks are making manual response obsolete. Teams need the next generation of AI technology that can analyze, decide and act in real time. Our latest innovations, fueled by unified, high-fidelity data, help give defenders the edge they need to outmaneuver modern attacks.

An AI-Ready Data Foundation for the Agentic SOC

Agentic AI depends on data that is fast, flexible and built for scale. Cortex Extended Data Lake (XDL) provides that data foundation for Cortex XSIAM and the broader Cortex platform, serving as a single source of truth for security operations. Built for AI and analytics, it ingests more than 15 PB of telemetry daily across 1,100+ integrations, and is designed to provide the comprehensive data required for effective detection, investigation, and response.

With the introduction of Cortex XDL 2.0, we are revolutionizing how organizations store, access and manage data, enabling new levels of flexibility and control.

New capabilities added with the Cortex XDL 2.0 release:

• Cost-efficient data lake tier that can lower SOC costs with flexible long-term retention for compliance, forensics and investigations.
• Federated search to query distributed data sources without incurring additional ingestion or storage costs.
• Native Chronosphere Telemetry Pipeline integration to filter and route telemetry at the source
• AI-driven parsing that automatically builds production-ready parsers from sample logs using generative AI, removing hours of manual effort and accelerating time to value.

Together, these capabilities power AI agents with critical security signals and give security teams the data they need, when and where they need it, while controlling costs.

Redefining How Analysts Work in the SOC

Cortex introduces an agentic-first analyst experience that embeds advanced AI directly into the analyst’s daily workflow. Designed to reduce investigation time, the elevated experience brings together automatically generated case summaries, visualized issue relationships, and a centralized Resolution Center within a unified case management workspace.

AI now spans the Cortex console, allowing context-aware agents to work in real time alongside analysts. Using the Cortex Agentic Assistant, teams can call on agents to plan and execute investigation workflows directly within their cases.

This release also doubles the number of AI agents who are purpose-built for SecOps and Cloud Security. Here are three of the newest additions.

• The Case Investigation agent delivers context-aware assistance that analyzes case artifacts and complex signals to accelerate triage. It recommends next steps, highlights critical evidence, builds AI case summaries, and takes action with analyst oversight.
• The Cloud Posture agent helps teams uncover, triage and resolve misconfigurations and posture risks across cloud environments. It streamlines analyst workflows by proactively prioritizing risk, enriching exposures and applying approved fixes.
• The Automation Engineer agent tackles one of automation’s biggest pain points: Building and maintaining complex workflows. With simple natural language prompts, teams can generate working code and scripts for agents or playbooks.

Our new agentic playbooks bring AI directly into automation workflows, embedding AI tasks that adapt in real time to help teams resolve incidents faster. They automate complex operations, analyze inputs with large language models (LLMs), and produce context-specific outputs.

Matt Bunch, Global CISO, Tyson Foods:

At Tyson Foods, protecting a complex global supply chain in an era of AI-driven threats requires us to move with the same machine speed as our adversaries. By consolidating onto the Palo Alto Networks Cortex platform, we’ve effectively closed the gap between detection and response. The impact has been transformative as we’ve increased our log visibility by 40% while reducing median time to respond by 50%. The agentic capabilities in the platform have allowed our teams to move from manual triage to high-level strategic defense, ensuring our global operations remain resilient and secure.

The Cortex Agentix Platform Has Arrived

The standalone Cortex Agentix platform brings the power of AI to everyone, delivering advanced orchestration and automation for the modern SOC. For Cortex XSOAR^® customers, this marks the natural evolution of our market-leading SOAR platform, now enhanced with agentic intelligence to unlock meaningful productivity gains.

With more than 1,300 playbooks, 1,100 integrations, and built-in MCP support, Cortex Agentix combines over a decade of SOAR leadership with powerful AI capabilities to help security teams operate with greater speed, coordination and efficiency across the SOC.

Securing the Agentic Endpoint

As users increasingly run AI-powered code packages, browser extensions, plugins and more, they are opening the door to a new class of AI-driven threats at the endpoint. That is why we announced our intent to acquire Koi to help secure the emerging agentic endpoint. Once completed, the acquisition will strengthen our visibility and protection at the endpoint, extending our ironclad protection from the SOC to where AI code actually runs.

See the Agentic SOC Take Center Stage at Cortex Symphony 2026

To experience these innovations firsthand, join Lee Klarich, Chief Product and Technology Officer, and Gonen Fink, EVP of Products, alongside other industry leaders at Cortex Symphony 2026, the ultimate SOC event.

Forward-Looking Statements (unreleased feature only)

This blog contains forward-looking statements that involve risks, uncertainties and assumptions, including, without limitation, statements regarding the benefits, impact, or performance or potential benefits, impact or performance of our products and technologies or future products and technologies. Any unreleased services or features (and any services or features not generally available to customers) referenced in this or other press releases or public statements are not currently available (or are not yet generally available to customers) and may not be delivered when expected or at all. Customers who purchase Palo Alto Networks applications should make their purchase decisions based on services and features currently generally available.

The post The SOC Is Now Agentic — Introducing the Next Evolution of Cortex appeared first on Palo Alto Networks Blog.

We’ve shared how Android’s proactive, multi-layered scam defenses utilize Google AI to protect users around the world from over 10 billion suspected malicious calls and messages every month¹. While that scale is significant, the true impact of these protections is best understood through the stories of the individuals they help keep safe every day. This includes people like Majik B., an IT professional in Sunnyvale, California.

Despite his technical background, Majik recently found himself on a call that felt dangerously legitimate. While using his Pixel, he received a call that appeared to be from his bank. The number looked correct, the caller knew his name and his address, and the story about a "suspicious charge" made perfect sense. "I’m usually pretty careful about this stuff," Majik recalled, "but I stayed on the line longer than I normally would. Even knowing how these scams work, it was convincing in the moment."

The turning point came when his phone displayed a Scam Detection warning during the call, which provided a critical moment to pause and reflect. Majik hung up, checked his bank app directly, and confirmed there was no fraudulent charge. For Majik, Scam Detection was the intervention he needed: “The warning is what made me pause and avoid a bad situation”.

While stories like Majik’s show how our existing protections provide a robust shield against scams, our work isn't done. As scammers evolve their tactics and create more convincing and personalized threats, we’re using the best of Google AI to stay one step ahead.

A recent evaluation by Counterpoint Research found that Android smartphones provide the most comprehensive AI-powered protections of any mobile platform. We are committed to building on this foundation by expanding our AI-powered protections to more users and devices, while rolling out new features that utilize on-device AI to defend against increasingly sophisticated threats.

Expanding Scam Detection for Calls to Samsung Devices

To help protect you during phone calls, Scam Detection alerts you if a caller uses speech patterns commonly associated with fraud. We are bringing these protections to more of our users through new regional expansion and availability on new devices. Scam Detection for phone calls on Google Pixel devices is available in the US, UK, Australia, Canada, France, Germany, India, Ireland, Italy, Japan, Mexico, and Spain.

Scam Detection is already helping millions of users to stay safe from scammers, and we are expanding this feature to more manufacturers, starting with the Samsung Galaxy S26 series in the U.S. We are continuing to work with our partners to bring these industry-leading protections to even more users.

Powered by Gemini’s on-device model, Scam Detection provides intelligent protection against scam calls while ensuring that the processing stays on your device. This keeps your conversations private while delivering warnings in real-time. To preserve your privacy, the phone conversation processed by Scam Detection is neither stored on your device, nor shared outside of the device. To ensure you stay in total control of your experience, Scam Detection is turned off by default. When enabled, the feature only applies to calls identified as potential scams and is never used in calls with your contacts. You can easily manage these preferences in your phone settings whenever you choose.

Enhanced Protection Against Messaging Scams

We want everyone to feel secure when they open their messages, no matter where they are or what language they speak. To make this possible, we’ve now expanded Scam Detection for Google Messages to more than 20 countries. This includes support for several languages including English, Arabic, French, German, Portuguese, and Spanish.

Beyond reaching more people, we are also making our protections more intelligent. We are enhancing how Google Messages identifies fraudulent texts by utilizing our Gemini on-device model on the latest Android flagship devices in the US, Canada, and the UK. The added power of Gemini’s on-device model allows for a much more nuanced analysis of complex conversational threats.

For example, it can better detect the subtle conversational patterns used in job offer scams or sophisticated romance baiting scams (also known as “pig butchering”), a deceptive tactic where a scammer builds a long-term "relationship" with a potential victim to gain their trust, before tricking them into a fraudulent investment. Because these methods rely on gradual manipulation and don’t present typical warning signs, they need more advanced capabilities to catch them at scale. These advanced protections are now rolling out on Google Pixel 10 series and other select devices, and will be available on the Samsung Galaxy S26 series.

Gemini-powered Scam Detection alerts a user to a job offer scam

Using the Best of Google AI to Set the Standard in Mobile Scam Protection

Android continues to set the standard in mobile scam protections by leveraging advanced AI to identify and intercept threats as they happen. As scammer’s strategies shift, we remain committed to developing equally adaptive and intelligent defenses. Our goal is to provide you with peace of mind so you can continue to connect and communicate with confidence, knowing our multi-layered defenses are there to help protect your financial and personal data against mobile scams.

Disclaimers

1: This total comprises all instances where a message or call was proactively blocked or where a user was alerted to potential spam or scam activity.