For the eighth consecutive time, Palo Alto Networks is recognized as a Leader in the 2026 Gartner^® Magic Quadrant for Privileged Access Management. Once again, it is positioned furthest along in Completeness of Vision and has been positioned so in every Magic Quadrant published for this category.

Read the 2026 Gartner^® Magic Quadrant for Privileged Access Management.

Identity is not a siloed risk. As an increasingly attractive target for threat actors, it must be understood and secured across the entire attack surface. For our customers, the benefit is clear: as part of Palo Alto Networks, Idira helps you adopt AI innovation at speed while retaining control over privileged access.

Why Vision Matters Most Right Now

The 2026 Gartner Magic Quadrant evaluates providers across their Ability to Execute and Completeness of Vision. We feel that being positioned furthest in Completeness of Vision matters most in moments of technology change like this one. Idira is built for an environment where:

• Identity is the attack surface, and frontier AI lets attackers move faster, increasingly logging in rather than breaking in.
• Identities have exploded, and human and machine privilege alike must be controlled.
• The pace of change has never been higher, as organizations race to adopt AI and modernize infrastructure without losing control.

Customers need a partner that can execute on an innovative vision as the threat and technology landscape evolves. With the scale of Palo Alto Networks behind us, we are focused on securing every identity and every moment of privilege in the AI enterprise.

The AI Era Demands More From PAM

We are in a new era, where identity and access have fundamentally changed. Machine identities now outnumber humans 109 to 1, AI agents (which require access to enterprise systems to do useful work) are entering the workforce, and attackers have noticed. That access must be governed alongside the human and machine identities already protected. Increasingly, though, attackers log in rather than break in, and PAM must evolve to stay ahead of these challenges. PAM must now extend beyond vaulted administrator credentials to discover, control, and govern privilege across the enterprise.

Idira’s Modern PAM platform is enabling PAM to meet the demands of the AI era; it combines vaulted access and zero standing privileges (ZSP) with secrets management, workload security, and identity governance. We are bringing ZSP to every modern target, including cloud, apps, and Kubernetes. And access challenges go beyond humans, so Idira does as well. To secure non-human access, Idira added native SPIFFE/SVID support to deliver short-lived cryptographic identities to workloads and AI agents. Our ability to deliver and innovate on our vision enables Idira to deliver purpose-built, end-to-end protection at scale for the modern AI enterprise.

Modernizing PAM: Where We Deliver

Making modern PAM easier to adopt is central to our approach. 

While some of the largest organizations continue to trust Idira for our proven traditional PAM leadership, they now also rely on us to guide their modern PAM transformation initiatives.

Data-Driven Innovation: We believe that our Transformation Decision Engine, which uses customer environment data to quantify the value of PAM modernization, combined with our years of proven experience guiding both the implementation and modernization of PAM programs across our expansive customer base, demonstrates our ability to help customers through any stage of their PAM journey with an informed, data-driven approach. 

Customer Experience: Idira guides cross-functional security teams through complex operational shifts, from standing access to ZSP, supported by the Idira Blueprint, hands-on training, and an active peer community. 

Scale and Reach: Idira customers already have a partner with global reach, and joining Palo Alto Networks extends it further. Our security platform sees identity risk across the whole attack surface, so a stolen or abused privilege becomes a signal your defenses act on in the moment, backed by the investment and expertise of the industry's largest cybersecurity company.

Ready for the Demands of the AI Enterprise 

Eight reports, eight times furthest in vision. I'm grateful to the customers whose trust and expertise shaped every one of them. This is also a first, as it’s the first report to carry the Palo Alto Networks name. Since Idira joined forces with Palo Alto Networks, our PAM expertise and platform now sit inside the industry's leading cybersecurity company, where privileged access connects to real-time threat detection and response, AI security, and Unit 42 threat intelligence.

The next chapter of privileged access is being written now, by attackers with frontier AI models and by enterprises deciding how fast to move. We intend to stay furthest ahead of both.

Thank you to our customers and community for your trust, and for the partnership that helps shape our innovation.

 

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Gartner, Magic Quadrant for Privileged Access Management, Paul Mezzera, Shubham Gera, Tarun Rohilla, Scott Brinker, 21 October 2026.

Gartner and Magic Quadrant are trademarks of Gartner, Inc. and/or its affiliates.

Gartner does not endorse any company, vendor, product or service depicted in its publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner publications consist of the opinions of Gartner’s business and technology insights organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this publication, including any warranties of merchantability or fitness for a particular purpose.

 

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What is the Gartner® Magic Quadrant?

“Magic Quadrant reports are a culmination of rigorous, fact-based research in specific markets, providing a wide-angle view of the relative positions of providers in markets where growth is high and provider differentiation is distinct. Providers are positioned into four quadrants: Leaders, Challengers, Visionaries and Niche Players. The research enables you to get the most from market analysis in alignment with your unique business and technology needs."

How can PAM help secure AI agents in the enterprise?

PAM can help secure AI agents by controlling their access to systems and data, protecting the secrets they use, and recording their access and actions. Capabilities vary by solution; controls should limit each agent’s permissions and how long those permissions remain active. Learn more about Idira Agentic Identity Security.

How does zero standing privileges differ from vaulted access?

Vaulted access protects stored privileged credentials. ZSP removes standing privileged access by granting permissions for a specific task and revoking them when the task or authorized period ends. Modern PAM can combine both approaches to support different environments and access needs.

The post Palo Alto Networks Named a Leader in the 2026 Gartner® Magic Quadrant™ for Privileged Access Management appeared first on Palo Alto Networks Blog.

Palo Alto Networks announces that Unit 42® Continuous Frontier AI Defense and Prisma AIRS are now on the newly launched OpenAI Marketplace, making them directly available to OpenAI’s enterprise customers. Threat actors are deploying artificial intelligence to find and exploit vulnerabilities at unprecedented speed, compressing breach cycles by almost 97%—from weeks down to mere hours. Human-speed defense can no longer keep pace with adversaries executing automated, continuous attacks.

AI Investments: The OpenAI Marketplace Advantage

For enterprise organizations holding OpenAI spend commitments, this milestone offers a compelling 2-for-1 financial and strategic advantage. Security teams can now deploy machine-speed, proactive defense while retiring their pre-committed OpenAI spend.

Palo Alto Networks offers two flagship solutions available for procurement through the OpenAI Marketplace.

• Unit 42® Continuous Frontier AI Defense: An always-on, expert-led agentic offensive security service that uses defender-grade models to discover, validate, and accelerate the remediation of exposures across applications and network assets before weaponization.
• Prisma® AIRS: A comprehensive AI security platform designed to provide complete discovery, risk assessment, runtime protection, identity and data governance, and observability for AI applications, agents, models, and artifacts across enterprise cloud platforms, SaaS environments, and endpoints.

Machine-Speed Defense with Unit 42 Continuous Frontier AI Defense

Unit 42 Continuous Frontier AI Defense is an expert-led, agentic offensive security service engineered to identify, validate, and remediate enterprise exposures as an organization’s environment changes.

This always-on, agentic service combines our offensive security experts with our proprietary multi-model harness, which uses defender-grade frontier models to discover vulnerabilities, validate real-world exploitability, and accelerate remediation across applications, identities, cloud infrastructure, and network assets as an organization’s environment changes. At the core of this capability is our proprietary multi-model software harness that dynamically routes security tasks to the optimal AI model, maximizing efficacy and coverage while managing the cost of frontier AI at scale.

The harness integrates defender-grade models, including OpenAI’s GPT-5.6-Cyber, and other commercial models alongside open-weight models and Unit 42 threat intelligence. By executing non-destructive adversary simulations across first- and third-party environments, the system proves actual exploitability and delivers prioritized remediation guidance including actionable fixes, code-level changes, and virtual patches. Organizations can pair this with Frontier Virtual Patching to block exploits long before official patches exist or CVEs are publicly disclosed.

The real-world impact of this proactive methodology was validated over six months of in-house testing and across more than 75 customer engagements backed by a $17 million R&D investment. During internal deployment within Palo Alto Networks, the service uncovered over two years’ worth of exposures in just three weeks. In customer assessments, the underlying analysis identified exposures in 100% of assessed organizations, with 37% rated high or critical severity. Crucially, over two-thirds of exposures found in third-party applications had no known CVE, proving the urgent necessity of continuous, AI-driven discovery.

Unit 42 Continuous Frontier AI Defense is available worldwide today on an annual subscription basis. Register for our upcoming Virtual Threat Briefing to see how we apply Frontier AI, our proprietary multi-model harness, and offensive security expertise to protect Palo Alto Networks and our customers.

To learn more about Unit 42 Continuous Frontier AI Defense, register for the upcoming Virtual Threat Briefing here.

Scale Enterprise AI Adoption Safely with Prisma AIRS

As OpenAI customers rapidly scale their use of powerful AI models, AI coding assistants, and autonomous agents, the enterprise attack surface is expanding faster than traditional security teams can manage. The transition to autonomous AI breaks traditional security boundaries, creating shadow AI blind spots, supply chain risks in open-source base models, unsafe actions from malicious inputs, and uncontrolled AI agents executing tasks with excessive permissions.

Prisma AIRS: Secure Every AI Ambition

Prisma AIRS is the Palo Alto Networks unified AI security platform, enabling enterprises to accelerate AI adoption while maintaining control over sensitive data, agent identities, autonomous actions and AI costs. Recognized by Gartner as a Market Shaper in the September 2026 Emerging Market Quadrant for AI Application Security – Established Vendors, Palo Alto Networks brings together discovery, continuous risk assessment, real-time protection and centralized governance to secure AI applications and agents across enterprise environments.

• Gain Visibility Across Enterprise AI: Discover and inventory AI applications, coding assistants, autonomous agents and associated assets across endpoints, cloud and enterprise environments. Identify shadow AI and gain visibility into the AI systems operating across the organization.
• Identify Risks Before They Become Threats: Continuously assess AI security posture, scan models and agent artifacts—including skills and MCP servers—for vulnerabilities and supply chain risks, and proactively red team AI applications and agents to uncover exploitable weaknesses before deployment.
• Secure AI Interactions, Data and Agent Identities: Enforce real-time protection against prompt injection, malicious interactions and sensitive data exposure. Verify agent identities, enforce least-privilege access and prevent unauthorized actions as agents interact with enterprise systems and data.
• Centralize AI Policy, Access and Spend: Use AI Gateway to govern connections to LLMs, MCP servers, APIs and enterprise services. Centralize policy enforcement, control model and provider access, monitor consumption and manage AI usage and costs across teams and applications.

Together, these capabilities help OpenAI enterprise customers secure AI-powered workflows, maintain accountability for autonomous actions and scale AI adoption with consistent security and governance.

Book a demo to see our AI Security solution in action.

The post Palo Alto Networks Brings Continuous AI Defense and Prisma AIRS to the OpenAI Marketplace appeared first on Palo Alto Networks Blog.

Two critical vulnerabilities in PHP (CVE-2026-109618 and CVE-2026-109619) have been published. Both vulnerabilities carry a severe CVSS score of 9.2 (more technical details are in the Unit 42 Threat Brief). 

This is not just another high-severity vulnerability disclosure, it marks the opening chapter in a massive acceleration of cyberthreats that will define the foreseeable future. Because PHP is widely used globally (by some estimates, more than half of all websites), the sheer scale of this is massive. Navigating this new reality demands immediate imperatives, including continuous, proactive vulnerability discovery before adversaries weaponize code, and a modern defense strategy that pairs traditional patching with real-time virtual patching. At a footprint of 22 million exposed instances, relying on manual remediation cycles alone is not a viable defense.

For context, exposures are typically in the thousands, for example around 50,000 potentially vulnerable exposed instances for recent NetScaler vulnerabilities, and about 16,000 exposed instances of BeyondTrust, potentially vulnerable to CVE-2026-1731. Pervasive open-source software introduces an exposure footprint on an entirely different order of magnitude. 

This situation illustrates what I shared in Defender’s Guide to the Frontier AI Impact on Cybersecurity. We’re officially in a new reality, and this is going to be an ongoing cycle.

An Accelerating Cycle

The latest frontier AI models are extraordinarily capable at finding vulnerabilities in code. We’ll see these models continue to surface critical issues at a pace faster than ever before.

But here’s the challenge: As soon as patches for vulnerabilities are available, attackers will reverse engineer the patch, locate the root flaw, and target them in near real time. It simply takes too long to apply patches to every system. This vulnerability deluge demands urgency and new technologies, such as virtual patching, are needed.

What Organizations Should Do Now

(1) Assess Your Open Source Inventory

Given the unprecedented speed at which new, critical vulnerabilities are being found with frontier AI, organizations need the ability to understand their true exposure at a moment’s notice. Attack surface management solutions have never been more critical for finding these blind spots across your entire codebase and supply chain. You can’t protect what you can’t see.

Additionally, services like Unit 42 Continuous Frontier AI Defense put defender-grade AI models in the hands of defenders to find and fix vulnerabilities before attackers can weaponize them.

(2) Prioritize Remediation Based on Reachability

You have to reduce what is reachable by attackers. You can't instantly patch everything everywhere, so prioritize what needs to be fixed first based on attack-path reachability. Secure what must be accessible to the public internet, and evaluate your environments to understand which of those exposed PHP instances carry the most immediate risk. For example, Cortex Xpanse and exposure management are great tools for determining reachability and automating remediation.

(3) Secure your AI deployments

While not the main focus for these PHP vulnerabilities, rapid deployment of AI across organizations is expanding the attack surface. All AI deployments must be secure AI deployments.

(4) Mitigate and Ensure Attack Protections

While you coordinate patching with your development teams, ensure best-in-class protections are in place to block the actual exploits. Mitigate against potential attacks immediately through proactive virtual patching and reducing overall exposure. Deploying high-fidelity virtual patches across your network and cloud security provides the mitigation layer necessary to give your teams the time they need to update the actual software safely.

Our new Frontier Virtual Patching capability delivered preemptive coverage for these PHP vulnerabilities ahead of today’s public disclosure. Additionally, Palo Alto Networks customers are protected from exploitation of these vulnerabilities by Advanced Threat Prevention available on our next-generation firewalls and Prisma Access.

(5) Deploy Real-Time Security Operations

Autonomous AI-driven attacks will drive attack lifecycles to minutes requiring every SOC to achieve single-digit mean time to detect (MTTD) and mean time to respond (MTTR).

The Path Forward: Institutionalize Defenses

This isn’t a one-and-done situation. With frontier AI compressing the patch-to-exploit window to minutes, assessing exposure, prioritizing reachability, mitigating and ensuring attack protections must become a continuous, automated motion.

This is a pivotal moment. While the scale of the challenge presented by frontier AI is real, I’m confident in our ability to solve it. Take the steps to secure your organization today, and build the foundation needed to ensure the advantage remains with the defender.

The post Millions of PHP Instances Exposed - What Organizations Need To Do Right Now appeared first on Palo Alto Networks Blog.

AI is moving from answering questions to taking action. Agents can use tools, access enterprise data, and carry out work across systems on our behalf or autonomously. This creates opportunities for businesses to move faster, but it also changes what security must protect. An agent’s identity, the authority it carries, and the actions it takes all become security decisions.

As enterprises put agents into production, they need controls that keep pace without hindering the work those agents are designed to do. Palo Alto Networks is working with NVIDIA to advance this goal across five connected areas in support of the NVIDIA OpenShell Reference Architecture for safe AI agents: an AI gateway on NVIDIA Vera CPU systems; runtime protection on NVIDIA BlueField DPUs; agent identity governance on NVIDIA Sentry; AI factory visibility through an AI-driven, agentic security operations center (SOC); and partner services that help customers implement a secure architecture.

Palo Alto Networks AI Runtime Security Extends Protection Across the AI Factory and Edge

Until now, the AI factory conversation has centered primarily on compute capacity and token generation. But as autonomous agents execute higher-stakes workloads, runtime security has become just as essential. Agents can be steered past their guardrails, and a compromised host cannot be trusted to accurately report what occurred. To be effective, security teams require visibility and enforcement that operate independently of the host environment.

Palo Alto Networks AI Runtime Security on NVIDIA BlueField DPUs isolates security processing in a dedicated infrastructure domain. Because BlueField sits in the path between agents and the models, data, and services they access, security policies are enforced in silicon — beyond the reach of a compromised host. This capability safeguards both GPU-accelerated AI workloads and CPU-based agent runtimes and is now generally available across AI factory and industrial edge deployments. Offloading security from the host node delivers two critical benefits: increased token throughput and stronger runtime protection across the entire AI factory.

Deployment at scale must be manageable. Our work with Spectro Cloud manages the deployment and operation of AI Runtime Security on BlueField DPUs, enabling consistent management across the AI factory infrastructure — from the data center to distributed edge environments. 

Idira Governs the Identity Behind Every Agent Action

Runtime protection answers what an agent is doing. Identity governance helps determine whether it is authorized to do it.

An agent may act for a person, a team, or an autonomous business process, often requiring access to multiple tools and data sources across the organization. Granting an agent broad, persistent access creates risk, while making access too restrictive limits its usefulness. To achieve the optimal balance of control and agent access, Palo Alto Networks is advancing an integration between agentic identity security and NVIDIA Sentry on BlueField — bringing identity and delegated authority closer to where agent interactions are governed.

The goal is to give each agent a verifiable identity, grant access appropriate to its task, and make its actions attributable. Once available, this solution will deliver zero-trust protection and tamper-resistant auditing for agents by moving the security controls — including identity — to the NVIDIA Bluefield DPU, separate from the host operating system. This addresses an immediate business need — enabling enterprises to scale agent deployments while retaining control over whose authority agents use and which resources they can reach.

Bringing AI Factory Signals into Cortex

Security teams need visibility into activity across the infrastructure that runs AI agents. Palo Alto Networks has announced an integration of Cortex XSIAM with NVIDIA DOCA Argus, which monitors host activity from BlueField’s independent trust domain. This provides security operations teams with runtime signals that do not rely on an agent installed on the host being monitored.

By bringing those signals together with network, cloud, and identity data in an AI-driven SOC, security teams can investigate AI infrastructure in the context of the broader enterprise. Palo Alto Networks is continuing to develop this foundation to support broader fleet visibility and more comprehensive response workflows — enabling security teams to identify unusual activity and act with greater confidence as AI deployments grow.

AI agents run within the NVIDIA OpenShell secure runtime, operated directly on the NVIDIA Vera CPU within the NVIDIA OpenShell Platform. Security observability is completely offloaded to the NVIDIA BlueField DPU. Operating out-of-band, NVIDIA Sentry, built on DOCA Argus, provides real-time memory and network inspection to detect threats without slowing AI inference. When malicious events or policy violations occur, DOCA Argus streams high-fidelity alerts directly to Palo Alto Networks Cortex XSIAM, where they can be automatically correlated and surfaced for rapid threat response. 

Turning Secure AI Factory Designs into Deployments

Customers need more than an architecture diagram to bring an AI factory into production. They need a practical path to design, validate, and operate security across compute, networking, applications, and the SOC.

Palo Alto Networks is working with strategic vendors , including Deloitte and World Wide Technology (WWT), to help customers plan and deploy secure NVIDIA AI factories. Deloitte’s Silicon-to-Service (S2S) offering, combined with its Secure AI Foundations, helps organizations implement AI use cases on NVIDIA AI infrastructure through validated architectures and managed services. By building security into the full AI factory stack, Deloitte helps clients balance performance, safety, and cost as they scale from pilot to production.  

WWT’s AI Proving Ground allows customers to evaluate and test an integrated stack before committing to production deployment.These engagements can help enterprises move from individual security components to a repeatable approach tailored to their environments and requirements.

Building Confidence into the Next Phase of Agentic AI

The value of agentic AI depends on an enterprise’s ability to give agents useful access while maintaining control over their actions. This requires runtime protection, governed identities, independent visibility, and a unified way to manage controls across the enterprise. 

Palo Alto Networks delivers comprehensive security across every layer of the AI factory through a unified platform. In collaboration with NVIDIA, we support the NVIDIA OpenShell Reference Architecture for enterprise governance, isolation, and security.  Together, we deliver the continuous protection needed to move safely from AI agent pilots to full-scale enterprise production.

To learn more about the general availability of AI Runtime Security on the BlueField DPU, read our blog, Palo Alto Networks Announces Support for NVIDIA Enterprise AI Factory. 

The post Advancing Agentic AI Security with Palo Alto Networks and NVIDIA appeared first on Palo Alto Networks Blog.

Enterprises spent the last two years racing to capture the vast potential of AI, authorizing AI agents to touch production code, internal databases, customer tickets and financial ledgers. Because these agents operate non-deterministically with broad capabilities, they can easily be manipulated into unexpected or malicious behavior. The result is agents breaking out of isolation, shifting AI risk from a hallucinating chatbot to an operational liability. 

The standard fixes, such as software containers or prompt-level guardrails, aren't sufficient to prevent malicious agent behavior. And traditional endpoint security was not designed to secure agents, which operate with real user access. Just as we pioneered XDR to include network, identity and cloud, we must now expand protection to secure agents in the enterprise. 

Cortex XDR One brings together XDR and Koi Agentic Endpoint Security to protect both traditional system processes and AI agents, delivering layered enforcement across an agent’s execution path. XDR One is designed to control an agent’s actions at the source, enforce hard boundaries at the operating system level, and spot rogue behavior through an AI-driven layer of defense.

An Emerging Security Blind Spot

Endpoints have evolved from controlled execution environments into complex ecosystems of AI coding agents, AI co-pilots, MCP servers and personal agents with access to sensitive data and systems. These artifacts can bypass traditional controls and operate in critical blind spots. 

When an agent ingests an untrusted input, connects to a compromised enterprise app or executes an unauthorized command, it transforms from a productivity tool into an active risk vector. Because agents operate with real user permissions and non-deterministic behavior, security teams face a compounding set of enterprise challenges that extend far beyond simple prompt injections.

Employees are adopting personal AI agents and browser tools that bypass IT oversight, spreading shadow AI across endpoints. At the same time, attackers are exploiting connected systems to manipulate these agents. By poisoning the external data, databases, or SaaS applications an agent relies on, adversaries can alter its behavior indirectly without ever touching the AI model itself. Without hard boundaries, an agent can be tricked into escalating host privileges, bypassing local containers or executing arbitrary system commands. In short, the agent escapes its intended sandbox and becomes an unwitting insider threat.

Adversaries are already exploiting this new vulnerability. In the ClawHavoc supply chain attack, adversaries targeted agents by uploading hundreds of malicious skills to OpenClaw's marketplace; once installed, these rogue skills broke runtime isolation to give remote attackers direct command-line access on the host. In a separate incident, Unit 42 research showed how agents can be tricked into revealing secret system prompts or sensitive information through indirect data poisoning from connected applications. Additionally, agents can bypass system and access controls when tricked into issuing unauthorized API calls or executing system tools under the guise of legitimate user permissions. Compounding these risks is the rise of shadow AI, as ungoverned, unmonitored agents deployed by employees silently inherit elevated system privileges without IT visibility.

When agentic AI was just emerging, we saw the potential risks it would bring and immediately sought out a solution to secure our internal AI workloads. Our InfoSec team found and deployed Koi while the startup was still in stealth mode. Impressed by Koi’s unique ability to secure new agentic endpoint behavior, we welcomed the talented Koi team into the Palo Alto Networks family to deliver this protection directly to our customers.

Cortex XDR One: Securing Every Endpoint and Agent from a Single Platform

XDR One integrates Cortex XDR and Cortex Agentic Endpoint Security with a single user interface and sensor, providing centralized visibility, management and investigation.

From sanctioned agents to shadow AI, XDR One gives teams a real-time inventory of every AI artifact the moment it appears on an endpoint. Armed with visibility into all the agent activity in the enterprise, security teams can control what packages, extensions, agents and AI artifacts are allowed to be installed or used. For example, a CISO can evaluate which vibe coding agents are actually being used by engineering teams and make an informed decision about the productivity impact of removing access to a specific agent. 

Securing the modern endpoint requires unified defense across both the AI agent lifecycle and the underlying operating system. XDR One delivers this protection, from software installation to live execution. Within the agent layer, it enforces runtime guardrails to help stop prompt injections and destructive commands. Simultaneously at the OS layer, XDR One continuously monitors runtime process chains to block zero-day exploits and living-off-the-land attacks in real time without affecting performance. To complement these defenses, optimized small language models run directly on the endpoint sensor to guard against evasive, fileless threats even while offline and without sending sensitive code to the cloud. By stitching cross-domain telemetry into clear, end-to-end attack chains, XDR One cuts false positives by up to 95% and drives automated remediation in minutes.

Ultimately, consolidating this security telemetry onto a single sensor eliminates fragmented point tools and establishes a powerful operational foundation, equipping Cortex XSIAM with the critical data needed to better govern AI risk across the enterprise.

Flexible Protection with Standalone Agentic Endpoint Security or Native Integration

Cortex Agentic Endpoint Security (AES) specifically governs AI tools, MCP servers and agentic workflows running on those hosts. Organizations have the flexibility to deploy AES alongside existing third-party EDR environments using the Cortex targeted sensor.

However, organizations can simplify operations when AES and XDR come together natively in Cortex XDR One. By unifying agentic defense and extended detection and response into a single sensor and platform, XDR One bridges the gap between traditional host security and AI risks, delivering seamless visibility, management and real-time investigation.

Setting a New Baseline for Endpoint Security

The era of standalone EDR has passed. Just as XDR moved beyond siloed endpoint detection and response to encompass network, identity and cloud, securing the modern enterprise now demands native protection for autonomous AI workloads. EDR without integrated agentic security is incomplete and fundamentally legacy. Disconnected point solutions lack visibility into OS execution, while bolt-on EDR offerings fail to correlate the agent’s prompt and tool context with subsequent system behaviors. Relying on a bird of prey may seem like high-end security, until you discover it only swoops in after the wolf has already attacked the flock.

XDR One sets the new standard for endpoint security, providing unmatched visibility and preemptive control across both the OS and AI layers within a single sensor.

Scaling Enterprise AI with Confidence

While XDR One delivers complete standalone protection, security leaders grappling with this complex new attack surface can augment their team’s capabilities with Unit 42 MDR, which will now include Managed Agentic Endpoint Security. By combining XDR One telemetry with elite threat intelligence, Unit 42 experts deliver proactive threat hunting, incident investigation, and AI risk governance to strengthen overall resilience.

Securing the Agentic Frontier

As organizations race to adopt AI agents, security teams face a pivotal transition. The expansion of agentic capabilities across enterprise endpoints demands a security posture that treats agents with the same rigor and control as traditional system processes.

By unifying agentic endpoint defense and XDR into a single sensor and platform, XDR One sets a new standard for endpoint security in the agentic era. Whether deployed independently or backed by Unit 42 expertise, XDR One gives security leaders the visibility and control needed to eliminate shadow AI, enforce operational guardrails and accelerate their AI transformation.

 

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Forward-Looking Statements
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 Reinventing Endpoint Security with Cortex XDR One appeared first on Palo Alto Networks Blog.

AI agents and AI-enabled tools are fundamentally changing how work gets done. From coding assistants to Model Context Protocol (MCP) servers, autonomous tools now access sensitive data, execute commands, and modify local workflows directly from the endpoint. Traditional endpoint security models fail to offer the visibility or governance required to manage these autonomous actions.

The security gap is already significant. Only 6% of organizations have an advanced AI security strategy. And the fastest attacks can move from initial access to data exfiltration in just 72 minutes, according to the 2026 Unit 42 Global Incident Response Report. As AI adoption grows, teams need context to prioritize risk and experts ready to respond so the business can continue adopting AI with confidence.

Today, we are announcing Unit 42 Managed Agentic Endpoint Security (Managed AES). We built the service on Cortex AES, our AI-native endpoint security platform that provides visibility, contextual risk intelligence and protection for AI agents and artifacts. Managed AES adds 24/7 Unit 42 expertise to help organizations put those capabilities into action. Backed by Unit 42 threat intelligence, our experts provide AI governance, threat hunting, investigation and response, and AI-related cyber hygiene. They apply human judgment to turn contextual intelligence into policy recommendations and prioritized actions as the environment evolves.

Continuous Expert Defense for the AI Environment

Managed AES brings continuous Unit 42 expertise to four areas of AI endpoint security.

• Govern AI Adoption with Greater Confidence
  AI adoption often moves faster than security teams can assess it. Our experts help identify AI tools and associated risks, evaluate exposure and recommend improvements to policies and controls. Security leaders gain a clearer view of AI use across their environment.
• Hunt for Emerging and Zero-Day Threats
  AI-related threats can emerge before they trigger a known alert. Our threat hunters use frontline threat intelligence and Cortex AES signals to proactively hunt across the customer’s AI environment. They evaluate findings, recommend action and refine policies and correlation rules to strengthen protection over time.
• Investigate and Respond 24/7
  Our analysts continuously monitor activity, investigate suspicious behavior and take permitted actions on the customer’s behalf to contain AI-related threats. Security teams gain specialized support to understand what happened, determine the potential impact and prioritize next steps.
• Continuously Strengthen AI-Related Cyber Hygiene
  Unit 42 assesses the customer’s AI security posture as adoption evolves. AI Security Posture Reports identify policy and control improvements, while Threat Impact Reports detail threats, potential impact, response actions taken and recommended next steps. These insights help teams prioritize improvements.

Expert Protection Across Your AES Environment

Organizations can choose the Cortex AES deployment model that best fits their environment while receiving the same 24/7 Unit 42 expertise.

• Managed AES with Standalone Cortex AES
  Organizations can deploy Cortex AES alongside their existing endpoint security solution for AI-native visibility, contextual risk intelligence, governance and protection for AI agents and AI-enabled tools. Managed AES adds Unit 42 experts who help customers act on that visibility and risk intelligence.
• Managed AES with Cortex XDR One
  Cortex XDR One brings together Cortex AES and Cortex XDR into a single sensor and console experience. It combines endpoint detection and response with AI-native security for AI agents and artifacts. Unit 42 experts provide 24/7 governance, threat hunting, investigation and response across the entire endpoint attack surface.

Securely Scale AI Adoption

Unit 42 Managed AES helps security teams understand how AI is being used, address risk before it escalates and respond quickly when threats emerge. By combining Cortex AES context with frontline threat intelligence and expert analysis, Managed AES helps businesses adopt AI with confidence.

Register for our upcoming webinar, The Next Era of Endpoint Security, to learn how Cortex AES and Unit 42 experts help your team govern AI risks, strengthen visibility and protect the agentic endpoint.

The post Introducing Unit 42 Managed Agentic Endpoint Security appeared first on Palo Alto Networks Blog.

Your Template for OMB M-26-15 Migration

The transition to Post-Quantum Cryptography (PQC) is not just a technical upgrade; it is a vital national security imperative that represents the most significant cryptographic reset in the history of the Federal Government. Driven by Executive Order 14412 and Office of Management and Budget (OMB) M-26-15, Federal Civilian Executive Branch (FCEB) agencies face a strict October 22, 2026 deadline to submit comprehensive PQC Migration Plans to OMB and the Office of the National Cyber Director (ONCD).

To take the heavy lifting off agency leaders facing this tight 120-day window, we developed a ready-to-use Template for OMB M-26-15 Readiness and OMB M-26-15 PQC Migration Plan Instructions. Precision-engineered to align directly with the White House mandates, this template serves as a "fast-track" for federal teams. Instead of spending limited cycles building complex compliance documentation from scratch, security leaders can leverage this template to rapidly map their transition pathways and shift their focus to active discovery and remediation.

Per M-26-15, each agency’s migration plan must contain, at a minimum, nine core elements, all of which are featured in the PQC Migration Plan Template:

• System prioritization strategy with risk-based justification.
• Timelines and milestones to achieve the first three migration phases.
• TLS 1.3 support milestones for the January 2, 2030, deadline.
• Methodologies and automated tools for cryptographic inventory.
• Cryptographic agile architecture implementation plan.
• Third-party coordination plan.
• Estimate of funding and personnel resources.
• Risk management strategy for the migration period.
• Governance roles and responsibilities definition.

Agencies must specifically prioritize high impact systems, High Value Assets (HVAs), and any systems vulnerable to cryptographically relevant quantum computer (CRQC) based attacks. This includes logical access control systems based in asymmetric encryption (such as PKI) and any data expected to remain mission-sensitive through 2030.

For most agencies, the core challenge of a migration plan is uncovering "cryptographic debt,” the years of hidden, legacy encryption buried in fractured IT systems. Because this debt creates unseen risks, establishing cryptographic visibility is the primary enabler for success. Once you can see your entire environment, the path to a quantum-safe future becomes clear and manageable.

A successful migration plan depends on an agency's ability to move quickly and accurately, requiring a fundamental shift in security architecture that prioritizes active discovery over passive lists. By deploying a structured and automated methodology, agencies gain the real-time visibility necessary to:

1. Discover their full cryptographic footprint with total clarity.
2. Protect critical assets against immediate and emerging threats.
3. Accelerate the rollout of quantum-safe resilience across the enterprise.

To maximize your team’s resources, our OMB M-26-15 PQC Migration Plan Instructions and Template for OMB M-26-15 Readiness are a practical "fast-track" for building a compliant transition plan. This template provides a clear implementation methodology and a detailed checklist to ensure all required elements are addressed.

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I. Policy Foundation

The federal mandate is driven by two specific directives that agencies must operationalize:

• Executive Order 14412: Accelerates the Federal Government's transition to post-quantum cryptography to mitigate emerging quantum computing threats.
• OMB Memorandum M-26-15: Directs each agency to prioritize critical information technology for migration and develop a plan that integrates PQC readiness into existing governance structures.

While this specific policy targets federal agencies, the global trend is clear: critical infrastructure operators and private sector entities should view this mandate as a strategic blueprint.

II. Compliance Timeline & Milestones

M-26-15 establishes a multi-phased timeline for agency compliance. While initial efforts focus on prioritizing HVAs and high-impact systems to mitigate immediate risk, agencies are expected to complete migration across remaining systems by 2035.

Deadline	Policy Requirement & Milestone
July 22, 2026	PQC Migration Lead Designation: Agencies must formally identify a PQC migration lead.
Oct 22, 2026	Submittal of PQC Migration Plan: Deadline for agencies to deliver a dynamic migration roadmap to OMB and ONCD.
Jan 2, 2030	TLS 1.3 Implementation: Agencies must support TLS 1.3 (or a successor version) to enable the extensible handshake needed for hybrid-PQC key exchange models.
Dec 31, 2030	Key Establishment Deadline: Agencies must transition all HVAs and high impact systems to PQC (FIPS 203 ML-KEM) for key establishment.
Dec 31, 2031	Digital Signature Deadline: Transition all HVAs and high impact systems to PQC for digital signatures using FIPS 204 (ML-DSA) or FIPS 205 (SLH-DSA).

III. Mandatory Elements of the PQC Migration Plan

Agency heads must ensure that their submitted plans address the following nine core elements:

1. System Prioritization Strategy: Risk-based justification addressing HVAs and PKI-based logical access control systems.
2. Timelines and Milestones: Phased migration plans within the initial planning horizon.
3. TLS 1.3 Support Milestones: Testing and deployment timelines for the 2030 deadline.
4. Methodologies and Automated Tools: Documentation of tools used for cryptographic inventory.
5. Cryptographic Agile Architecture: Strategy for algorithm rotation via configuration rather than code.
6. Third-Party Coordination Plan: Delineating the Shared Responsibility Model with cloud providers.
7. Resource & Personnel Allocation: Estimates for annual budget requests.
8. Risk Management Strategy: Specific to the migration period itself.
9. Governance Roles and Responsibilities: Defining implementation and risk acceptance accountability.

IV. Strategic Phasing: Multi-Year Transformation

• Discovery & Strategy (2026-2027): Inventory, assessment, and governance framework setup.
• Piloting (2027-2028): Early migration of prioritized systems.
• Prioritized Key Migration (2028-2030): HVAs and systems vulnerable to CRQC-based attacks.
• Signature Migration (2031): Digital signature protocols for prioritized systems.
• Full Enterprise Migration (2035): Completion for all remaining systems.

V. Operationalizing the Transition with QSS

Quantum-Safe Security (QSS), a FedRAMP Moderate authorized tool, helps agencies move from box-checking to real operational resilience.

Strategic alignment with M-26-15:

• Automated Discovery and CBOM Generation: Real-time visibility and generation of Cryptographic Bills of Materials (CBOM) to identify hidden dependencies.
• FedRAMP Authorized: Accelerates adoption by removing the need for a separate agency-level ATO for the discovery tool.
• Validation of Cryptographic Agility: Ensures systems can rotate to NIST standards (ML-KEM, ML-DSA) without hard-coded updates.
• Integration with Zero Trust: Secures the cryptographic roots of device attestation and inter-service communication.

VI. Conclusion

The transition to quantum-safe cryptography demands immediate leadership. Agencies cannot afford to delay while adversaries continue to archive sensitive data.

Don't start from scratch, fast-track your PQC strategy!

The post Fast-Track Your PQC Strategy: A Template for OMB M-26-15 Readiness appeared first on Palo Alto Networks Blog.

Are organisations becoming so reliant on third party space assets that they would face severe disruption if the assets were impacted by a cyber attack?

When most people think of space technology, they imagine routine weather forecasts or blue-dot navigation on a smartphone map. But behind those quiet satellite signals lies an invisible digital thread holding modern society together. From crop harvesting schedules to international flight routes and supermarket supply chains, daily life on Earth is deeply dependent on data from space.

Yet space security remains the cyber risk that very few in executive boardrooms are addressing. As the space economy surges toward a $1.01 trillion valuation by 2034, a dangerous blind spot has emerged. Cyber threats to orbital infrastructure are no longer a theoretical hazard—they are happening now.

Privatization Outpacing Security

Historically, space operations were the exclusive domain of governments operating in strictly air-gapped environments with near-zero tolerance for risk. Today, rapid commercial privatization has slashed launch costs, but speed to market is dangerously outstripping security by design. Commercial operators inherit standard enterprise IT vulnerabilities, complex supply chain risks, unpatched APIs, and operational technology gaps.

The systemic disconnect is stark:

• According to data from the Organisation for Economic Co-operation and Development (OECD), space-based systems now support more than 50% of all critical services and infrastructure across modern economies.
• The European Commission’s latest assessment is that 10% of the EU’s GDP is enabled directly or indirectly by satellite navigation signals. This equates to around €1.88 trillion based on the EU’s GDP for 2025.
• Yet 85% of organisations aren’t factoring space assets into their cyber risk mitigation strategy, according to the World Economic Forum.

Physical Consequences on the Ground

Unlike typical corporate breaches, orbital cyber attacks can trigger immediate physical impacts on Earth. Tampering with satellite telemetry can destabilize regional crop yields by altering planting schedules or paralyze commercial aviation by injecting false data into flight-navigation networks.

Crucially, these risks are not limited to assets in orbit. Ground-based power substations, telemetry command hubs, and downlink facility infrastructure have become primary cyber-physical targets. A coordinated cyber attack on local energy substations supporting satellite ground operations could sever communication links and ground control just as effectively as an in-orbit compromise.

A study commissioned by the European Space Agency (ESA) modeled the impact of a hypothetical 7-day satellite communications outage across ESA member states and Canada. The potential cost reaches up to €20 billion ($21.8 billion) in total economic losses. This includes €19 billion in maritime logistics and port bottlenecks, €558 million in direct aviation losses (canceling ~4,000 transatlantic flights), €352 million in lost connectivity for emergency services and remote communities, and €89.8 million from stalled offshore energy operations.

Aging Tech Meets Advanced Threat Actors

Geostationary satellites carry mission lifespans of 10 to 15 years, locking critical communications into legacy hardware designed before modern cyber threats existed. Nation-states and cybercriminals are exploiting these gaps; Palo Alto Networks Unit 42 threat intelligence currently tracks multiple state-sponsored groups actively targeting space-sector systems.

What Leaders Must Do Now

Governments and enterprise boards must recognize space-based timing, navigation, and observation as core national critical infrastructure. Leadership teams should execute four priorities immediately:

1. Map Space Dependencies: Audit third-party services, cloud links, and automated logistics relying on satellite telemetry.
2. Establish Terrestrial Backups: Build manual redundancies—including ground-based radio networks and offline inventory controls—for scenario planning when space signals go dark.
3. Elevate Supply Chain Audits: Evaluate whether key vendors rely on single-point-of-failure commercial satellite constellations or unfortified ground substations.
4. Update Risk Registers: Formally integrate space dependencies into corporate cyber risk registers.

Translating these action items into real-world operational resilience requires moving beyond static compliance policies. To protect complex ecosystems—where ground stations, power substations, cloud environments, and orbital data streams converge—organizations need a unified, platformized approach to security that provides continuous visibility across every link in the space supply chain.

In the modern threat landscape, business resilience, national sovereignty, and space security are inseparable. It is time for leaders to look up before an orbital breach brings ground operations to a standstill.

The post The Blind Spot in Orbit: Why Space Could Be the Next Infrastructure Crisis appeared first on Palo Alto Networks Blog.

The NHS has set an ambitious goal of achieving cyber resilience across healthcare organisations by 2030. Delivering on that vision requires technology, expertise and close collaboration across the public and private sectors.

This is clearly a highly complex task, but one we're delighted to help deliver in partnership with IBM. Through this partnership, Palo Alto Networks will provide advanced cloud-based perimeter technology, alongside IBM Consulting’s specialist guidance, implementation support and tailored onboarding for NHS organisations.

As health and care services continue to modernise, maintaining resilient, secure digital infrastructure has never been more important. Healthcare organisations are operating in an increasingly complex environment, with growing digital dependencies, expanding supply chains, evolving cyber threats and new technologies such as AI reshaping the landscape.

According to IBM's Cost of a Data Breach Report, healthcare continues to be one of the most impacted sectors globally when it comes to the financial consequences of cyber incidents. This highlights the importance of taking a proactive, long-term approach to cyber resilience across critical public services.

Palo Alto Networks' continued work with the NHS demonstrates the scale and complexity of securing modern healthcare environments. As organisations balance innovation, operational demands and evolving security requirements, there is a growing need for solutions that provide both strong protection and the flexibility to meet local needs.

Introducing the Next Generation of the NHS Secure Boundary Programme

Palo Alto Networks will deliver next-generation, cloud-based security solutions through the next iteration of the NHS Secure Boundary programme. The service has been designed to help strengthen cyber resilience across UK health and social care organisations while supporting the safe and reliable delivery of services.

Secure Boundary provides the capability to support a network spanning more than 1.5 million NHS staff, over 200 NHS trusts, more than 55,000 organisations handling health data and approximately 2 million devices under national cyber monitoring. The nationally integrated and centrally funded service embodies a "Defend as One" approach, helping organisations benefit from shared protection while retaining local control.

Secure Boundary supports NHS organisations through:

• An AI-enabled cybersecurity capability designed to strengthen resilience and support the safe, reliable delivery of healthcare services.
• Enhanced visibility and protection for internet traffic across the Health and Social Care Network.
• Improved security for infrastructure and clinical applications operating across multiple service providers.
• Increased protection for information shared across NHS organisations and trusted third parties.
• Greater privacy and security for staff accessing web-based services, whether on-site or remotely.
• A "Defend as One" approach that complements existing local security measures while allowing organisations to tailor policies to their specific requirements.
• Reduced complexity through the consolidation of security tools and management processes.
• Improved efficiency through a centrally funded model that helps reduce duplication of technology and investment.

The new Secure Boundary service represents a significant step forward in strengthening the NHS's digital resilience. By providing a more secure foundation for NHS services, it helps support operational continuity, safeguard sensitive information and enable healthcare teams to continue delivering high-quality care in communities across the UK for years to come.

If you would like to find out more about Secure Boundary and how it can help your organisation, please contact ukpubsec@paloaltonetworks.com. 

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Forward-Looking Statements

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. These forward-looking statements are not guarantees of future performance, and there are a significant number of factors that could cause actual results to differ materially from statements made in this blog. We identify certain important risks and uncertainties that could affect our results and performance in our most recent Annual Report on Form 10-K, our most recent Quarterly Report on Form 10-Q, and our other filings with the U.S. Securities and Exchange Commission from time-to-time, each of which are available on our website at investors.paloaltonetworks.com and on the SEC's website at www.sec.gov.  All forward-looking statements in this blog are based on information available to us as of the date hereof, and we do not assume any obligation to update the forward-looking statements provided to reflect events that occur or circumstances that exist after the date on which they were made.  

 

The post NHS England evolves its cybersecurity strategy with Palo Alto Networks and IBM appeared first on Palo Alto Networks Blog.

Hope is not a strategy. Neither is nostalgia.

Mark Carney, Prime Minister of Canada

 

Business leaders cannot respond to AI-era cyber risk by longing for a simpler technology stack, slower decision-making or a security model built around a trusted internal network. AI is changing how organisations operate—and how adversaries identify and exploit weaknesses—faster than many leadership teams can anticipate.

Cybersecurity has therefore evolved from a “coconut strategy”, built around a hardened perimeter and trusted interior, to what I call a “mango strategy”. The modern enterprise and its digitally connected ecosystem have a porous, continuously changing edge, but it must place its strongest protection around the core: its data, identities, code repositories, applications, digital essentials and critical operations.

What is the CEO’s role in AI governance and cybersecurity?

This shift makes cybersecurity a CEO-level business priority. CEOs do not need to oversee every AI deployment, but they must define how the organisation adopts, governs and secures AI while maintaining resilience, accountability and trust. That means setting risk thresholds, assigning accountability, protecting critical data and identities, and ensuring that security can operate at the same machine-speed as autonomous systems.

Why autonomous AI agents require workforce-level oversight

Given this dual nature, business leaders cannot afford to ignore AI. Like water, AI adoption will find its way and follow the path of least resistance as a senior cybersecurity leader from a large European bank put it. Without clear governance, it can expose weak controls, while AI-enabled attackers can exploit those weaknesses at unprecedented speed and scale.

Some have suggested that CEOs should double as Chief AI Officers. However, as executive responsibilities expand, expecting a CEO to oversee day-to-day AI implementations while setting broader corporate vision is unrealistic. While the CEO may not manage specific AI initiatives, they must actively shape the overarching strategy, the “AI surround and framework”.

This leadership starts with developing and hiring highly capable talent to execute the vision. The CEO’s true value lies in ensuring that AI is embraced across every business unit, integrated into risk models, and governed by robust safeguards. Proper administration and oversight are no longer secondary concerns—they are core executive functions.

A critical dimension of this responsibility is governing the use of agentic AI. Autonomous agents function effectively as "authorized insiders." Custom-built for specific workflows, they leverage elevated privileges to access critical applications, databases, and proprietary tools around the clock.

In practice, these agents represent a new class of virtual employees. Though non-human, their operational impact is comparable to their human counterparts, yet they operate 24/7. CEOs must ensure that autonomous agents are deployed, monitored, and evaluated with the same rigors applied to the rest of the workforce.

How can leaders build resilience against AI-driven instability?

Translating this new operational reality into steady organizational resilience requires recognizing AI's broader societal impact. While the technology holds vast potential for good, it can also accelerate systemic and geopolitical instability when misused. For instance, recent developments in frontier AI models demonstrating advanced exploit-generation capabilities highlight the immediate necessity of defense-in-depth.

Palo Alto Networks CEO Nikesh Arora highlighted this shift in this article - Weaponised Intelligence -  “These are not incremental improvements. Imagine a horde of agents methodically cataloging every weakness in your technology infrastructure, constantly. Over the next six months, the barrier to entry for sophisticated attacks will continue to diminish. A hacker’s dream weapon will be available to anyone with a credit card and computer.”

To counter these emerging risks, leading AI developers and security organizations are actively collaborating. Through initiatives like Project Glasswing, partners across technology and financial services are stress-testing defender-grade models to harden critical software and establish proactive defenses before threats reach the wild.

These collaborative initiatives address the exact questions executive boards are asking today: Where are our exposure points, how severe are they, and how quickly can we contain them?

Answering these questions requires a strong security culture. Defining how an organization adopts and governs AI is now a core mandate for executive leadership. Even with specialized AI leadership in place, the CEO must drive the culture to align AI implementation with business values and strategic priorities.

Ultimately, executive leadership must set clear parameters: defining operational boundaries, establishing accountability, tracking key progress metrics, and encouraging responsible innovation within controlled risk thresholds.

Four cybersecurity priorities for the agentic AI era

The cybersecurity landscape has reached an inescapable inflection point: we have entered the "Agentic Era," where autonomous AI agents drive attacks at machine speed. In this environment, manual, reactive patching is no longer just slow—it is a mathematical dead end that can leave enterprises effectively unprotected.

Navigating this shift requires a deliberate transition from reactive measures to proactive architecture built on four strategic pillars:

1. Fight Machine with real-time machine speed: Because automated adversaries can scan millions of endpoints and chain multiple low-severity vulnerabilities into catastrophic exploit paths, human-led response is a guaranteed failure. Organizations must move to a prevention-first posture, using AI-driven systems that natively integrate across network, cloud, and endpoint telemetry to block threats in real-time.
2. The "Customer Zero" Mandate: The time between a model's release and its weaponization is shrinking to zero. Palo Alto Networks acts as "Customer Zero" by stress-testing frontier models during their development phase, hardening the security stack against specific autonomous logic capabilities well before they are public.
3. Move Beyond "Find-and-Fix": Finding vulnerabilities is only half the battle. The new imperative is to prioritize remediation based on exploitability and reachability—identifying which vulnerabilities can be chained together by an attacker—rather than just severity. This requires shifting operations to machine-speed defense via unified platforms and services like Unit 42 Frontier AI Defense or virtual patching, allowing you to safely patch your digital essentials when real-time agentic attacks are happening.
4. Prioritize Platformization: Fragmented security architectures—where point solutions cannot communicate—are an open invitation to automated lateral movement. A unified, data-driven architecture is a prerequisite for security, enabling a threat blocked at the endpoint to harden an organization’s perimeter.

How CEOs can create the defender’s advantage

For the C-suite, the takeaway is clear: Digital transformation and cybersecurity transformation are now inextricably linked. The organizations that adapt by rebuilding their architecture around unified data and autonomous prevention will hold the "defender's advantage," while those relying on legacy, manual workflows remain exposed to a rapidly accelerating threat landscape.

Nostalgia offers no protection against the speed and complexity of the modern threat landscape. Operating under the assumption that perimeters remain secure is a legacy mindset. 

As executive leaders refine their strategy for the AI era, two core principles should guide their approach:

• Cybersecurity is a data issue, not a technology issue. The goal is to protect data, not just to harden infrastructure.
• Many of our core best practices in cybersecurity, such as Zero Trust and modular platformization, are more appropriate than ever in the era of AI and automated agents. 

Helmut Reisinger is CEO for Europe, Middle East and Africa at Palo Alto Networks.

The post The Mango Strategy: A CEO’s Guide to AI Cybersecurity appeared first on Palo Alto Networks Blog.

Introducing Cortex XCOR: AI-Driven Observability that Delivers Autonomous Response with AI SRE

I’ve been tackling the observability problem for more than a decade, since before it was coined observability. From building internal monitoring for EC2 at AWS to building the observability platform for Uber, the holy grail was always to create a solution that would automatically tell you what the issues were and help remediate them, instead of just being a tool that showed you dashboards. But standing in the way were the foundational scale and cost challenges of cloud-native architectures, which is what my co-founder Rob Skillington and I solved by creating Chronosphere. 

With the ability to effectively manage observability data at scale and as we undergo another major industry shift with AI, we’re finally at a point in time where the technology available puts the holy grail within grasp. Six months after joining Palo Alto Networks, we’re taking a major step forward with the launch of Cortex XCOR - an AI-native observability platform that delivers on that long promise of automatically root causing the issue. 

Reimagining Observability for the Modern SDLC

Getting to this point required a fundamental leap in model intelligence. When generative AI was broadly released in late 2022, LLMs were intriguing, but the reasoning capabilities were rudimentary. Although we saw limited applications for chatbots and assistants within observability, those early models could only help the end user, they couldn’t ultimately solve the issue. They assisted investigations by summarizing logs or translating queries, but weren’t transformative. That changed in late 2025 with frontier models like OpenAI’s GPT-5 and Anthropic’s Claude Sonnet and Opus. 

My own AI moment in observability came a couple of months ago when our chat functionality evolved from acting like an assistant to functioning as an autonomous expert that gave me the answer and showed me how it got there. As I pushed our agents with increasingly complex scenarios, they began reasoning through problems far beyond their initial capabilities. Much like how LLMs have replaced my need for web search, our chat is now my primary interface in our product, completely overhauling how I interact with it. 

This shift for observability is needed now more than ever as operations teams struggle to keep up with the surge in AI-driven software development, caused by the same step up in reasoning capabilities from late 2025. Research shows that 42% of developers now report that AI writes at least half their code, up from 12% last year. Rapid code releases continuously expand the surface area for production failures and drive up the burden on developers and SREs to manually identify root causes. To match this velocity, observability needs to modernize now to avoid becoming the bottleneck for product innovation.

Introducing Cortex XCOR, the Next-Generation AI-Native Observability Platform

That is precisely why we are launching XCOR. We designed it to transform observability from static dashboards and manual remediation to an AI-first experience that automates both routine and complex investigations.

At the heart is our XCOR Operator, an AI assistant embedded throughout the platform that helps operations teams match AI-coding velocity. What makes the XCOR Operator especially powerful is that it understands the user intent and serves as the conversational interface for our specialized agents that execute behind the scenes. The user of an observability platform plays many roles depending on the situation: investigating incidents, tuning alerts and dashboards, optimizing data volumes, and so on. Each of these roles are mirrored by specialized AI agents that have been optimized to complete those task-specific workflows from end to end.

The most challenging role is the AI SRE - investigating an incident to get to the root cause and identify remediation. Triggered automatically when an alert fires, the AI SRE agent autonomously reasons through underlying issues and recommends actions and mitigations in under three minutes on average, with a 75% success rate of root cause analysis in complex production environments and a further 19% of incidents where the analysis was deemed useful. By comparison, manual responses can take 20 minutes just to locate the relevant issues, gather initial context and find the right on-call engineer.

None of this automated reasoning is possible without complete end-to-end visibility and context. With the recent acquisition of Embrace, we now bring together front-end Real User Monitoring (XCOR RUM) and proactive XCOR Synthetics with our backend and infrastructure observability to deliver a true, full-stack platform. RUM captures every web and mobile session in full fidelity without sampling, tying crashes and slow loads directly to session timelines, while synthetics simulates global user traffic to proactively catch regressions before they hit production.

Underpinning this full-stack visibility is the XCOR Fabric, which we engineered to provide our agents with rich, real-time institutional, application and infrastructure context and draws from:

• Knowledge Graph: A real-time, detailed model incorporating your infrastructure, applications and business logic. Unlike many other solutions, it normalizes and incorporates custom telemetry to provide the most complete context.
• Operational Memory: Institutional learnings and historical context captured from past incident investigations.
• User Behavior: Patterns revealing which specific queries and dashboards senior engineers rely on during active resolutions.
• Human Knowledge: Context pulled directly from runbooks, documentation, and operational files.

By delivering total visibility and grounding our AI agents in this deep operational context, XCOR delivers rapid root-cause analysis and recommends steps toward remediation.

Automated Optimization Continues to Deliver the Most Cost-Effective Platform on the Market

Yet observability without cost control is like an untrained puppy, adorable until it chews through your furniture, shoes and budget. Managing observability costs remains a persistent challenge because modern cloud and AI-native architectures produce massive volumes of telemetry data. We designed Chronosphere to solve this exact problem, and it remains a major pillar of XCOR. 

We help customers optimize data by an average of 89% without sacrificing visibility, earning top industry rankings for cost efficiency and the trust of industry leaders like OpenAI, DoorDash and Compass. Now with expanded consumption-based pricing models across the XCOR platform and granular consumption budgets that automatically adjust to usage patterns over time, we eliminate the tradeoff between critical visibility and rising costs. 

The quest for observability’s holy grail is finally within reach. As engineering teams race to keep pace with the speed and scale of AI-driven development, XCOR moves operations beyond static dashboards and manual troubleshooting toward true autonomous remediation. This is observability's AI moment.

The post Observability’s AI Moment appeared first on Palo Alto Networks Blog.

We are thrilled to announce that KDDI Corporation, a long-standing NextWave partner of Palo Alto Networks and a Managed Security Service Provider (MSSP) with whom we have built a strong partnership, has launched an innovative new service, "SASE Gateway powered by Palo Alto Networks," equipped with our latest "SP Interconnect (SPI)" architecture. 

As the implementation of AI in business operations and the shift to the cloud accelerate, and enterprise data becomes more distributed than ever, cyberattacks are also leveraging AI to become more advanced and sophisticated. In such an environment, enterprise security must completely transition from traditional perimeter defense to a Zero Trust architecture that continuously verifies all traffic. Our Prisma SASE is the industry's premier platform built to defend, scale, and secure your enterprise at the speed of AI. Powered by Precision AI®, it enforces dynamic Zero Trust guardrails to proactively defend against frontier AI attacks and secures the adoption of GenAI and autonomous agents across the enterprise.

The "SP Interconnect (SPI)" adopted by KDDI this time is a next-generation architecture that directly interconnects telecommunications carriers' high-quality networks with our Prisma SASE. This integration makes it possible to directly connect traffic from closed networks (KDDI Wide Area Virtual Switch 2: WVS2)—which remain highly demanded among Japanese enterprises—to SASE, in addition to internet connections. In particular, the latest SPI achieves the flexibility and performance required to meet the strict requirements of the Japanese market, including the elimination of IPsec connection overhead . As a result, an ideal infrastructure foundation has been completed that uncompromisingly balances the communication quality of the carrier backbone and the latest security of Palo Alto Networks, allowing unified management under a single policy engine.

Executive Perspective: KDDI Leadership on Next-Gen Security

Regarding this release, Junji Hori, General Manager of Product Management at KDDI Corporation, stated:

"With the launch of Prisma SASE with SP Interconnect, the convergence of closed networks and the internet has accelerated. This service serves as the core first-phase solution in the Security-as-a-Service domain under KDDI’s 'AccelWaves Vision,' and we position it as a major milestone toward realizing 'KDDI AccelWaves Network,' an autonomous network service for enterprises scheduled for release in 2028.

By simply unifying the individual design and management of security across both closed networks and the internet, this newly announced service reduces the operational burden on security and networking while contributing to rapid response in the event of an incident. This is an advanced initiative made possible by combining the long-standing partnership and expertise cultivated between both companies.

Equipped with AI security capabilities, we are confident this service will deliver new value not only to existing WVS2 customers, but also to customers aiming to centralize security operations and optimize their networks.

KDDI will continue to further strengthen its partnership with Palo Alto Networks and strive to deliver innovative services that accelerate our customers' AI implementation." 

Key Architectural Advantages of SP Interconnect 

The SP Interconnect that underpins the "SASE Gateway" offers the following outstanding benefits to customers: 

• Direct Connection to KDDI's Closed Network and Ensuring Reliability: Achieves a low-latency and stable communication environment through the high SLA and cloud scale of Prisma SASE, and closed connection with KDDI's high-quality, wide-band backbone.
• Realization of Comprehensive ZTNA: Consistently applies best-in-class Zero Trust security to access SaaS and private applications, regardless of the line type (closed network, Internet, etc.).
• Securing the AI-Driven Enterprise: By routing high-speed closed network traffic through Prisma SASE, organizations can safely adopt GenAI and agentic AI tools. SP Interconnect enables  this traffic to benefit from machine-speed inline inspection, stopping automated, frontier AI attacks (like machine-scale vulnerability scanning and prompt injections) while enforcing complete visibility, identity control, and advanced data loss prevention (DLP).
• Proactive IT Operations Powered by AI: Accelerate time-to-value by transforming reactive IT support into proactive, self-healing operations. It leverages AIOps and ADEM (Autonomous Digital Experience Management) to comprehensively visualize and manage transport networks across branches, clouds, and data centers, predicting and resolving network issues before users even notice them.

Furthermore, to realize a platform-based approach to security, KDDI offers the "SASE Gateway" service alongside the extensibility to introduce advanced security solutions such as our "Cortex XDR" and "Prisma Browser" (a secure enterprise browser). Additionally, to provide end-to-end support for customers' security operations and management, this offering can be combined with SOC and IR (Incident Response) services provided by LAC Co., Ltd. 

Palo Alto Networks is confident that this "SASE Gateway" will become an essential business foundation supporting secure DX (Digital Transformation) for many Japanese enterprises advancing AI adoption, hybrid work and cloud migration. Through our strong alliance with KDDI, we look forward to further deepening our technical collaboration and greatly expect them to powerfully lead the expansion of sales and adoption among enterprise companies as a strong core solution for KDDI's "AccelWaves Vision".

KDDI’s SASE Gateway with Palo Alto Networks is now available as part of the companies’ joint integrated security portfolio.

Modernize Your Closed Network Security Today

Ready to simplify your hybrid network management and protect your organization against AI-driven threats? Contact us to schedule a custom architecture review or explore how KDDI SASE Gateway can fit into your cloud strategy.

The post “SASE Gateway” provided by KDDI : How SP Interconnect Simplifies Closed Network Zero Trust appeared first on Palo Alto Networks Blog.

Most cloud and security teams struggle with two fundamental questions: What is actually running across our clouds, and what is exposed? As workloads sprawl and AI models deploy faster than ever, critical posture data gets locked inside specialized consoles. Gaps go unseen until they are exploited, and shadow AI leaves teams flying blind.

To solve this visibility challenge, Palo Alto Networks is introducing CLARA Agent.

By connecting Palo Alto Networks Strata Cloud Manager (SCM) directly to Google Cloud Gemini Enterprise, CLARA Agent turns complex cloud security data into clear, conversational insights. SCM powers this integration by consolidating security across environments, automating AI-driven operations to prevent misconfigurations, and continuously enforcing zero-trust policies with real-time threat prevention. Instead of digging through endless dashboards or running manual queries, teams can simply ask plain-language questions directly inside Gemini Enterprise to get immediate, evidence-backed answers.

CLARA Agent, which will be available on Google Cloud Marketplace, provides instant infrastructure discovery, automatically surfaces production AI/ML models alongside unsanctioned shadow AI, and ranks critical vulnerabilities so teams know exactly what is exposed. By bringing security intelligence directly into everyday workplace tools, it democratizes visibility for cloud, platform, and AI teams alike.

Bringing Palo Alto Networks CLARA Agent to Google Cloud Marketplace will help customers quickly deploy, manage, and grow the AI agent on Google Cloud’s trusted, global infrastructure. Palo Alto Networks can now securely scale and support organizations using its CLARA Agent to enable their security teams to turn cloud security data into conversational insights.

Vineet Bhan, Director, Security and Identity Partnerships, Google Cloud

Built for Immediate Value and Enterprise Security

CLARA Agent supercharges your existing investment in Strata Cloud Manager by bringing its rich security telemetry directly into everyday workflows. Greenfield deployments will benefit from additional visibility into their AI and non-AI workloads.

Designed to complement SCM’s robust security architecture, CLARA Agent leverages the open Agent-to-Agent (A2A) protocol and Model Context Protocol (MCP) servers to deliver fast, secure, and context-aware responses without requiring additional infrastructure setup. This integration creates a more seamless experience for security teams, empowering them to audit cloud posture, prioritize zero-day vulnerabilities, and secure rapid AI adoption faster and more intuitively than ever before. 

See CLARA Agent Live at the Security Innovations Forum 2026

Palo Alto Networks is proud to be a sponsor of the Security Innovations Forum 2026 on September 29 at the Google Event Center MP7 in Sunnyvale, CA. Join Rich Campagna, Senior VP of Products at Palo Alto Networks, and Joshua Haslett, Strategic Technology Partner  Manager at Google, on the executive panel, "Co-Innovating the Future of Cybersecurity: A C-Suite Perspective" (10:50 AM – 11:35 AM). They will discuss how agentic platforms are revolutionizing threat response and how organizations can deploy advanced AI agents at scale.

You can also visit the Palo Alto Networks kiosk during the forum to see a live demonstration of CLARA Agent, connect with our experts, and discover how we are co-innovating with Google Cloud to help safeguard agentic AI environments.

Get Started

Visit our booth at Google Cloud’s  Security Innovations Forum 2026, or contact your Palo Alto Networks representative to schedule a demo.

 

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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 Introducing CLARA Agent: Instant Cloud & AI Risk Insights Available on Google Cloud Gemini Enterprise appeared first on Palo Alto Networks Blog.

For decades, industrial cybersecurity rested on a comfortable assumption: operational technology (OT) was protected by physical isolation. The "air gap" guaranteed that power grids, manufacturing lines, and water systems remained safely disconnected from the chaos of the public internet.

That assumption is no longer just outdated - it is a strategic liability.

Driven by the imperative for real-time efficiency, predictive maintenance, and AI-led automation, industrial operations are integrating with enterprise IT and cloud environments at an unprecedented scale. Telemetry from Palo Alto Networks reveals a 332% surge in unique internet-exposed OT devices and services, bringing nearly 20 million OT-related assets onto the public internet.

Digital transformation has fundamentally redrawn the perimeter. What was once an engineering challenge managed on the plant floor has transformed into an existential risk that demands board-level oversight.

Every Industrial Asset Is Now a Digital Asset

Modernizing critical infrastructure is rarely framed as a security initiative; it is an operational mandate. Heavy machinery engineered to last thirty years is now instrumented with IoT sensors, managed through cloud-native platforms, and remotely monitored.

This connectivity creates tight dependencies between core business IT and physical operations. Modern industrial facilities rely on enterprise identity management, cloud telemetry, and continuous software updating to function day to day.

Consequently, a threat actor does not need to breach the factory floor to stop production.

When an enterprise IT network suffers an incident, executive leadership often initiates a manual shutdown of operational lines out of caution or because shared authentication mechanisms become unavailable. The business processes linking enterprise IT and OT have become functionally inseparable.

The Anatomy of Modern Industrial Threats

Attackers no longer require sophisticated, state-sponsored zero-day exploits to disrupt physical infrastructure. As seen in recent cyber incidents targeting municipal water systems in the US and the breach of a Polish power plant, threat actors continuously exploit basic internet exposure, unpatched vulnerabilities, weak default credentials, and unsegmented remote connections.

Threat dynamics across critical infrastructure have shifted toward two major vectors:

• Low-Friction Initial Access: Basic internet discovery tools allow threat actors to identify exposed control systems and programmable logic controllers (PLCs) in minutes. Weak factory default passwords or unsegmented cellular modems provide direct entry points into physical operations.
• Operational Blindness: Modern attacks deliberately target the reporting layer - such as HMI and SCADA interfaces - to suppress alarms and present "normal" operational telemetry to engineers, even while underlying physical processes operate dangerously out of specification.

Operational Realities and the Zero Trust Imperative

The integration of artificial intelligence and automated workflows into industrial environments accelerates this challenge. Beyond human engineers logging into control networks, automated software pipelines, third-party monitoring platforms, and autonomous AI agents are executing commands within OT environments.

Data from recent identity landscape research reveals that machine identities outnumber human identities by 109 to 1 across EMEA organizations.

In an environment populated by millions of interconnected human, machine and AI actors, implicit trust based on network location is dangerous. Applying Zero Trust to OT requires moving away from traditional perimeter defense toward continuous, explicit verification:

• Verify Explicitly: Authenticate and authorize every access request - human or machine - regardless of where the request originates.
• Enforce Least-Privilege Access: Limit access strictly to the assets and commands necessary for a specific operational role or process.
• Assume Breach: Design networks under the assumption that enterprise systems are compromised, preventing lateral movement into physical control layers.

The Industrial "Red Button": Engineering for Containment

While IT security prioritizes data confidentiality, OT security prioritizes human safety, system availability, and operational continuity. Halting a processing facility or energy grid carries immediate financial, environmental, and regulatory consequences. Security controls designed for corporate office networks cannot simply be retrofitted onto plant floors.

Cyber resilience is measured not merely by prevention, but by limiting the blast radius when a breach occurs.

Leading industrial organizations are establishing a programmable "Red Button" capability - a combination of architectural micro-segmentation and automated playbooks. This architecture allows security teams to rapidly isolate critical physical operations from compromised IT networks or cloud dependencies without bringing production to a complete halt.

Five Strategic Actions for Executive Boards

With regulatory frameworks like NIS2 increasing direct accountability for senior leadership, OT cybersecurity requires strategic governance centered on comprehensive visibility, robust micro-segmentation, operational risk scoring, and virtual patching:

• Establish Complete Asset Visibility: Eliminate dark corners across physical environments. Organizations must maintain a continuous, real-time inventory of every connected industrial asset, controller, and external dependency.
• Embed Security by Design and Virtual Patching: Integrate cybersecurity requirements directly into industrial modernization programs from initiation. Deploy virtual patching mechanisms - such as Frontier Virtual Patching - to shield unpatchable legacy systems against rapidly evolving AI-driven threats.
• Architect for Degraded Mode: Design critical infrastructure to maintain safe, baseline operations even if cloud access, enterprise IT, or external network connections are severed.
• Practice Operational Recovery: Develop and stress-test incident response playbooks specifically designed for physical environments, including offline configuration backups and manual operational overrides.
• Unify Risk Governance: Align executive leadership, security operations, and plant engineering around operational-based risk scoring and shared business metrics: physical safety, system uptime, and operational continuity.

Accelerating digital transformation should not mean compromising physical safety or operational stability. Palo Alto Networks provides an integrated platform built to address the unique constraints of operational environments. By combining AI-driven asset discovery, granular micro-segmentation, continuous risk assessment, and virtual patching across both enterprise IT and physical control networks, Palo Alto Networks empowers industrial organizations to modernize with confidence - securing operations, protecting revenue, and safeguarding national infrastructure.

Further interesting reading:

• ‘Complexity at the intersection of IT and OT’ – Executive Edge Peer Guide  
• A CISO’s guide to navigating OT & IT convergence in the manufacturing industry

The post Why OT Resilience Is Now a Boardroom Imperative appeared first on Palo Alto Networks Blog.

AI agents are crossing an important threshold.

They are moving from assistants that respond to us to autonomous systems that reason, plan, and act on our behalf. They can write and execute code, access enterprise data, call tools and APIs, and keep working toward a goal with limited human intervention.

That changes the security model.

An autonomous agent is designed to find a way to accomplish its objective. If one path is blocked, it reasons about another. That is what makes agents powerful, and it is why models’ built-in prompt guardrails alone are not enough.

The harness can guide what an agent should do. Security must control what an agent can do. That requires security authority outside the agent itself.

Palo Alto Networks and NVIDIA Team Up to Secure Agentic AI

NVIDIA provides the accelerated computing platform, secure runtime foundation, and reference system design for agentic AI. Palo Alto Networks brings the AI security capabilities enterprises need to govern interactions, protect workloads, and control identity and access.

Together, we are extending security across three areas: AI governance with Prisma AIRS AI Gateway, runtime protection with Prisma AIRS AI Runtime Security, and Agent Identity Security powered by IDIRA.

This is the focus of our expanded collaboration with NVIDIA on the NVIDIA Open Agent Safety Platform reference design.

a) Prisma AIRS AI Gateway on NVIDIA Vera CPU

Our forward-looking architecture includes Prisma AIRS AI Gateway software running on NVIDIA Vera CPU systems, bringing AI governance and enforcement closer to infrastructure designed for agentic workloads.

The AI Gateway is the real-time enforcement point for AI Governance, Runtime, and Identity security. It continuously verifies agent identities, enforces strict least-privilege access, redacts sensitive data before it is exposed, and deeply inspects every interaction—stopping unauthorized actions before they ever reach critical host systems or cloud endpoints.

• Operationalize AI agents: Connect applications and agents to thousands of models and tools through one control point, with the reliability and flexibility to scale AI across the enterprise.
• Track usage and control costs: Monitor every AI interaction across your company to see exactly what teams are doing, track your spending, and restrict access to approved tools only.
• Enforce agent security across the enterprise: Block sensitive data exposure and malicious commands in real time, and verify agent identity before actions are taken.

b) Announcing Prisma AIRS AI Runtime Security on NVIDIA BlueField

The Palo Alto Networks AI Runtime firewall running natively on NVIDIA BlueField DPUs extends Zero Trust protection to the front-end network traffic of the AI factory and to the AI Edge, creating a tamper-proof boundary immune to application-layer attacks. Drawing on the NVIDIA Sentry reference design, Prisma AIRS AI Runtime Security accelerated on NVIDIA BlueField delivers: 

• Hardware-Isolated Boundaries: Tamper-proof boundary, ensuring a compromised model or a rogue agent can never bypass runtime controls.
• Agentless Behavioral Introspection: Monitor agent execution paths, memory structures, and tool calls with NVIDIA DOCA Argus with zero CPU overhead, and feed real-time telemetry directly into Cortex XSIAM for automated observation, analysis, and remediation.
• Zero Trust Governance: Strict least-privilege access is enforced across the network and external APIs by default, delivering a verifiable audit trail for every allow and block action.

c) IDIRA Agent Identity Security

As agents gain greater autonomy, identity becomes fundamental to controlling what they can access and what actions they can take.

Looking ahead, we plan to integrate NVIDIA OpenShell with upcoming Agent Identity Security capabilities from Idira, including identity and secrets management for the OpenShell sandbox and agent identity broker service on NVIDIA BlueField. This will help organizations apply identity-based controls and least-privilege access to agents without relying on persistent human credentials.

The Path Ahead: Security at Scale for Enterprise Agents

As agents become more autonomous, governance must extend to identity. Prisma AIRS AI Gateway will also serve as an enforcement point for Agent Identity Security powered by IDIRA. We are also expanding to include Agent Identity Broker support directly on NVIDIA BlueField, providing identity verification and access governance independently of the host environment.

Together, these capabilities are designed to prevent unauthorized execution, reduce standing access, and stop privilege escalation as agents interact with enterprise systems.

As agents gain more autonomy, enterprises need consistent control over how they connect and what they can access.

Our work with NVIDIA brings those controls closer to where agentic workloads run, while Palo Alto Networks provides the security policy, governance, and enforcement enterprises need to manage them. 

To learn more about our GA of AI Runtime Security on the BlueField DPU, read our blog — Palo Alto Networks Announces Support for NVIDIA Enterprise AI Factory.

The next phase of AI will be defined not only by what agents can do, but by whether enterprises can trust them to operate safely at scale.

Palo Alto Networks was named a Market Shaper in AI application security. Read the report: Gartner® Emerging Market Quadrant for AI Application Security – Established Vendors, September 2026.

The post Securing AI Agents at Scale with NVIDIA appeared first on Palo Alto Networks Blog.

Cybersecurity is in the middle of a generational shift. AI has disrupted a 30-year balance of power between defenders and adversaries.

Armed with agentic AI tools and open-weight models stripped of safety guardrails, threat actors are discovering, validating, and chaining exposures at machine speed. In a recent Unit 42 investigation, the attacker used more than 50 MITRE ATT&CK techniques to reduce weeks of methodical intrusion tradecraft into less than 10 hours—97% faster than a skilled red team could have done. Time-to-exfiltration has compressed to under an hour in real-world attacks. And when new CVEs are publicly disclosed, automated adversary scanners are weaponizing them within 15 minutes.

Defending at human speed is no longer viable. Organizations must close years of accumulated exposure while securing the AI they are rapidly adopting. That demands action on five fronts: continuously find and fix vulnerabilities, reduce exposure, secure AI infrastructure, unify attack prevention, and automate detection and response at machine speed.

Today, we are announcing Unit 42 Continuous Frontier AI Defense to put the first two priorities into practice. Powered by Anthropic's Mythos models and OpenAI's latest GPT cyber models, this always-on, agentic service combines our offensive security experts with proprietary multi-model harnesses to discover vulnerabilities, validate real-world exploitability, and accelerate remediation across applications, identities, cloud infrastructure, and network assets.

Led by our offensive security experts, this service discovers, validates and remediates exposures before they can be exploited. We use proprietary AI harnesses and gated capability models—powerful models restricted from public use—to discover vulnerabilities, prove real-world exploitability, and accelerate remediation across applications, identities, cloud infrastructure, and network assets as an organization’s environment changes.

Continuous Frontier AI Defense builds on Unit 42’s Frontier AI Defense, which launched in April and introduced Frontier AI Exposure Analysis, a point-in-time exposure analysis, and a security blueprint that benchmarks current capabilities to help organizations modernize their cybersecurity systems. In August, Unit 42 expanded the list of frontier AI models to include  OpenAI’s GPT-5.6-Cyber and Anthropic's Claude Mythos 5, giving organizations access to additional AI capabilities.

A Single AI Model Is Not a Security Strategy

Frontier models are powerful, but no single AI model is a cybersecurity silver bullet and the models require equally powerful AI scanning harnesses.

Unit 42 research evaluation of model performance across enterprise codebases and live environments revealed two stark operational realities:

• Coverage caps: No single AI model catches more than 40% of vulnerabilities in a complex environment.
• Visibility: Leading cyber models such as Anthropic Claude Mythos 5 and OpenAI GPT-5.6-Cyber have less than 10% overlap in the exposures they identify.

To solve the single model gaps, Continuous Frontier AI Defense runs on proprietary multi-model harnesses. These harnesses act as an intelligent orchestration layer, routing specific offensive testing tasks to the model best suited for the job. This approach aligns model strengths, eliminates individual gaps, and keeps compute spend economical.

We combine these frontier models with Unit 42 threat intelligence, and frontline security expertise to turn raw vulnerability data into verified, actionable protection. Zero Data Retention (ZDR) architectures protect enterprise source code and telemetry, so customer data is never retained or used to train public models.

What Happens When AI Meets Real Enterprise Architecture

We did not build this service on theoretical assumptions. We built and validated it internally  across Palo Alto Networks and across more than 100 customer engagements.

During our internal deployment, continuous Mythos-based scanning achieved over a year’s worth of traditional penetration testing results in just three weeks. The harness identified 3.2 times more high and critical vulnerabilities per product than legacy testing methods, and helped engineering teams cut mean time to remediate by 51%.

When we executed the service across customer environments, the frontline data delivered a clear warning:

• Hidden exposures: In third-party applications, two out of three validated exposures had no known CVE. Vulnerability-only scanners will never see them.
• High severity: 37% of identified exposures rated high or critical in severity.
• Chained attack paths: Flaws rarely existed in isolation. In one financial sector engagement, our harness identified a sequence of individually minor flaws: an application payment link that failed to re-verify identity, a skipped one-time password check, and a session routing flaw. Chained together, an attacker could achieve complete account takeover and payment fraud without any victim action.

Moving from Discovery to Validated Protection

Finding weaknesses without fixing them only creates noise. And, security teams do not lack findings; they already have more alerts than they can act on. The bigger challenge is knowing which weaknesses an attacker can actually exploit and chain into a viable path to compromise, and then remediating them.

Unit 42 Continuous Frontier AI Defense translates findings directly into risk reduction:

• Continuous Testing Engine: Provides a full-estate baseline scan followed by always-on testing as an organization’s environment changes.
• Multi-Model AI Harness: Unit 42 deploys proprietary multi-model harnesses, the software architecture to route work to the model best suited for the task, improving efficacy and coverage while managing the cost of frontier AI at scale.
• Leading Cyber Models: The harnesses are built around gated capability models, including Anthropic Claude Mythos 5 and OpenAI GPT-5.6-Cyber, as well as open weight models. 
• Advanced Adversary Simulation: Proves real-world exploitability by validating end-to-end attack paths across first- and third-party web apps, APIs, cloud infrastructure, source code repositories, and network assets.
• Accelerated Remediation: Delivers actionable and prioritized fixes, code-level guidance, and virtual patch recommendations. To implement virtual patches before vulnerabilities are publicly disclosed or official patches exist, organizations can pair this with Frontier Virtual Patching. 

Unit 42 Continuous Frontier AI Defense is available worldwide today on an annual subscription basis. Register for our upcoming virtual Threat Briefing to see how we apply Frontier AI, our proprietary multi-model harness, and offensive security expertise to protect Palo Alto Networks and our customers.

To learn more about Unit 42 Continuous Frontier AI Defense, register for the upcoming Virtual Threat Briefing here.

The post Introducing Unit 42 Continuous Frontier AI Defense appeared first on Palo Alto Networks Blog.

Palo Alto Networks Named a Market Shaper in 2026 September Gartner^® Emerging Market Quadrant for AI Application Security — Established Vendors.

When we set out to solve AI security, we knew it was an audacious challenge to take on. Securing AI Coding, which I wrote about last week, is one example of how we are tackling this multidimensional problem. 

Today marks yet another major milestone in that journey. I am so proud to share that Palo Alto Networks has been recognized as a ‘Market Shaper’ in the September 2026 Gartner^® Emerging Market Quadrant for AI Application Security – Established Vendors. 

 

To us, this recognition reflects our core commitment to help organizations address all emerging AI use cases: securing AI coding, enterprise AI applications and governing autonomous agents, through a unified approach. 

What Modern Enterprise AI Security Requires

Artificial intelligence is fundamentally changing how enterprise software is built, how digital experiences are delivered, and how work gets done.

In the report, Gartner defines ‘Market Shapers' as those that are building comprehensive platforms by acquiring innovative companies and introducing centralized management tools for AI workflows. They are also expanding traditional security capabilities into broader agent-based controls, using advanced access control and runtime monitoring to secure agentic actions.

We believe our position as a ‘Market Shaper’ reflects four core capabilities we provide through our AI Security Platform, Prisma AIRS for effective enterprise AI security today:

• Secure AI Coding: Gain visibility into coding agents, extensions, skills, models and MCP servers, assess the artifacts they rely on, and establish controls over data, identity and agent actions. 
• Secure Enterprise AI Applications: Reduce risk across the AI application lifecycle, from models and components to testing and runtime interactions. This helps organizations build and deploy AI faster while safeguarding sensitive data and business-critical applications.
• Govern Agent Actions: Maintain control and accountability as autonomous agents interact with enterprise data, tools and systems. Clear identities and permissions help reduce unauthorized activity and contain unintended behavior.
• Accelerate AI Adoption Without Losing Control: Scale AI with consistent governance across coding, enterprise applications and autonomous agents. Maintain centralized control over access, usage and spend, through an AI Gateway. 

Defending the AI Frontier Requires a Platform

AI coding agents, enterprise AI applications and autonomous agents may look like separate security problems. In practice, they increasingly depend on the same models, data, identities, tools and infrastructure. Attempting to secure each one with its own isolated point product only creates fragmentation at the exact point when enterprises need greater visibility and control

This is why we built Prisma AIRS, as the comprehensive AI security platform. It gives security teams a unified approach to discover AI, assess risk, protect runtime interactions, control agent identity and access, and govern connections to enterprise data and systems.

The goal is not to slow AI adoption. It is to give developers and business teams the freedom to use AI while giving security teams the visibility and control they need.

Read the research report to learn more: Gartner® Emerging Market Quadrant for AI Application Security – Established Vendors, September 2026.

Ready to take the next step: Schedule an AI Security Demo / Briefing 

 

Disclaimer

Source: Gartner, Emerging Market Quadrant for AI Application Security – Established Vendors, September 2026.

Gartner is a trademark of Gartner, Inc., and/or its affiliates.

Gartner does not endorse any company, vendor, product or service depicted in its publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner publications consist of the opinions of Gartner’s business and technology insights organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this publication, including any warranties of merchantability or fitness for a particular purpose.

The post Defining the Standard for AI Security appeared first on Palo Alto Networks Blog.

Palo Alto Networks has achieved FedRAMP Moderate authorization for Quantum-Safe Security (QSS), a turnkey Automated Cryptography Discovery and Inventory (ACDI) solution. With this certification, federal agencies can deploy QSS immediately.  The authorization demonstrates that QSS meets stringent security requirements, enabling federal agencies to adopt cutting-edge technologies while safeguarding sensitive, unclassified data. QSS removes operational roadblocks by making cryptographic discovery, risk assessment and transition both continuous and actionable.

Accelerating Mandates for PQC Transition

Executive Order 14412 and OMB Memorandum M-26-15 have moved federal transition milestones well ahead of the original 2035 target. Federal agencies must now transition High Value Assets (HVA) and  high  impact  systems to post-quantum cryptography (PQC) for:

• Key establishment: By December 31, 2030
• Digital signatures: By December 31, 2031

Meeting these compressed timelines demands upgrading from static, manual inventory methods to continuous, automated cryptographic visibility. Traditional, annual spreadsheet audits are point-in-time snapshots that become obsolete the moment they are completed.

As the largest standalone cybersecurity provider, Palo Alto Networks leverages existing federal firewalls as distributed sensors within an integrated platform turns the network into an automated, real-time cryptographic control point. Agencies using the QSS solution will achieve high-fidelity visibility into their network-wide cryptographic posture and active algorithms immediately on Day 0, without introducing operational friction or deployment delays. By utilizing the native cryptographic discovery capabilities built into already deployed firewalls, agencies can activate their PQC migration plans without demanding a net-new, capital-intensive hardware acquisition. This makes Palo Alto Networks solution a fiscally responsible and rapid path to PQC migration execution. 

Technical Architecture: Discovery to Remediation

QSS operationalizes the full PQC migration lifecycle through a unified control plane built on three technical pillars:

• Agentless Discovery: QSS transforms the existing Palo Alto Networks security fabric into a distributed sensor network, providing comprehensive  visibility into encrypted sessions, certificates, and tunnels. This approach identifies hidden cryptographic debt across diverse vendor ecosystems and IoT without forcing new agent rollouts or secondary hardware deployments.
• Prioritized Risk Intelligence: The platform fuses automated cryptographic discovery with rich enterprise context, correlating the mathematical strength of ciphers and certificates against asset criticality and data sensitivity categorization. This multidimensional risk engine enables teams to move beyond raw technical alerts and focus resources on protecting long-lived, high-value data against Harvest Now, Decrypt Later (HNDL) attacks and cryptographic debt.
• Active Inline Remediation: QSS moves beyond reporting to active risk reduction. For most legacy systems that cannot natively support PQC, Cipher Translation allows the firewall to translate weak classical encryption into quantum-safe standards at the network edge, shielding unpatchable assets without requiring application code changes.

Immediate Implementation

The Executive Order on Securing the Nation Against Advanced Cryptographic Attacks confirms that PQC is a national security imperative, not just a compliance requirement. For agencies, that means treating the shift to post-quantum cryptography as a strategic initiative that protects critical data today and safeguards it against tomorrow's threats. Our FedRAMP Moderate authorization reinforces that Palo Alto Networks is a proven partner in that effort, helping agencies build a cohesive posture that is "Secured in America" and made for the needs of the U.S. government. We're committed to helping the public sector modernize with confidence.

Get Ahead of the Mandate

Stop guessing which digital locks to change first. Quantum-Safe Security transforms your existing infrastructure into a high-fidelity sensor network to neutralize "harvest now, decrypt later" (HNDL) risks without new architectural complexity. Explore our FedRAMP Moderate authorized platform to see how you can achieve zero-downtime cryptographic resilience and bridge the gap for unpatchable legacy systems via in-line enforcement.

The post FedRAMP Moderate Authorization for Palo Alto Networks Quantum-Safe Security appeared first on Palo Alto Networks Blog.

Securing Every Environment at the Speed of Frontier AI

Recognized as a Leader for the second consecutive time and positioned furthest for Completeness of Vision. Palo Alto Networks is advancing hybrid mesh firewall with one integrated platform for preemptive protection, agentic operations, AI runtime security and quantum-safe security across the enterprise.

 

We are proud to share that Palo Alto Networks has been named a Leader in the 2026 Gartner® Magic Quadrant for Hybrid Mesh Firewall for the second consecutive time, and positioned furthest for Completeness of Vision.

We believe this recognition reflects the strength of our approach to network security: one natively integrated platform that delivers consistent protection, policy and operations across every environment.

It also comes at a pivotal moment for cybersecurity. Frontier AI is collapsing the time between vulnerability discovery and exploitation. AI applications and agents are creating new infrastructure and data risks. At the same time, the cryptographic reset is forcing enterprises to rethink how they establish and maintain digital trust.

Fragmented firewall architectures and manual security operations were never designed for this speed, scale or complexity, and can no longer keep pace.

One Platform Across Every Environment

Hybrid environments have traditionally required organizations to deploy and manage different security products across data centers, clouds, branches, campuses and remote users. The result is often fragmented policy, inconsistent protection and operational complexity.

Palo Alto Networks transforms hybrid mesh firewall from a collection of enforcement products into a unified, intelligent security platform, powered by Precision AI.

The  Strata Network Security Platform is our Hybrid Mesh Firewall, spanning hardware  (PA-Series), virtual (VM-Series), cloud-native (Cloud NGFW) and firewall-as-a-service (SASE) deployments with consistent policy, shared intelligence and threat prevention across environments. 

Strata Cloud Manager provides unified policy, visibility and AI-driven operations across these enforcement points. Beyond traditional firewall environments, the platform extends protection to the browser with Prisma Browser and to AI applications, models and agents with Prisma AIRS®.

Cloud-delivered security services, including Advanced Threat Prevention, Advanced WildFire, Advanced URL Filtering, and Advanced DNS Security deliver consistent, inline prevention across environments. Intelligence discovered in one part of the platform can strengthen protection everywhere, turning every enforcement point into a real-time prevention layer.

If you’re new to the concept of HMF, explore our primer on What Is a Hybrid Mesh Firewall and Why It Matters.

The Hybrid Mesh Firewall Market Is Evolving

Gartner’s 2026 Hybrid Mesh Firewall research describes a market moving toward more automated, intelligence-driven security platforms as organizations manage increasingly complex hybrid environments. 

The report identifies several trends shaping the evolution of hybrid mesh firewall, including the growing adoption of cloud-based management, AI-driven threat prevention, automation through LLM-based assistants, post-quantum cryptography and security capabilities for AI applications and workloads.

As these requirements evolve, organizations need a network security architecture that can deliver consistent protection and operations across hybrid environments while preparing for emerging AI and quantum-era risks.

Advancing Hybrid Mesh Firewall for the Frontier AI Era

Against this backdrop, Palo Alto Networks continues to advance the Strata Network Security Platform for the Frontier AI era.

Preempt attacks earlier: Advanced Threat Prevention Plus uses Frontier AI-powered vulnerability discovery and Frontier Virtual Patching to deliver preemptive protections, including before vulnerabilities are publicly disclosed or official patches are available.

Disrupt attacker infrastructure: Advanced IP Defense continuously identifies, validates and blocks attacker-controlled infrastructure in real time - even when adversaries use direct-to-IP communications, proprietary encryption, residential proxies or trusted cloud infrastructure to evade traditional security signals.

Operate at machine speed: Six purpose-built Network Security Agents in Strata Cloud Manager assist teams with deployment, troubleshooting, configuration management, posture optimization, threat investigation and data protection, with built-in guardrails and human oversight.

Secure AI and modernize digital trust: Prisma AIRS helps secure AI applications, models and agents across the AI lifecycle, while Quantum-Safe Security and Next-Generation Trust Security help organizations identify cryptographic risk and modernize digital trust. Read more about Palo Alto Networks’ approach to securing AI.

Together, these capabilities help organizations reduce exposure before attacks succeed, operate at machine speed, secure AI infrastructure and modernize digital trust for the post-quantum era.

Proven Security, Performance and Scale

A unified platform must deliver not only breadth, but also effective protection and reliable performance under demanding conditions.

Independent SecureIQLab testing demonstrated 99% security efficacy, with Palo Alto Networks scoring approximately 20 points higher than the next-best tested vendor. Miercom validation demonstrated up to 2–8 times higher AI traffic throughput, along with strong TLS inspection and stable performance under sustained load.

More than 75,000 organizations around the world rely on Palo Alto Networks to consolidate security, improve visibility and reduce risk across their hybrid environments.

This global scale provides intelligence that continuously strengthens the platform. Every attempted attack expands what the platform can learn, and every new detection can become protection for all customers.

Defining the Future of Hybrid Mesh Firewall

We believe being named a Leader for the second consecutive time reinforces our conviction in an integrated platform that delivers consistent prevention and operations everywhere an enterprise operates.

But we believe industry leadership is also about preparing customers for what comes next.

As Frontier AI accelerates attacks, autonomous systems reshape security operations and quantum risk changes the foundations of digital trust, enterprises need more than a collection of firewalls. They need an intelligent security platform that can transform shared intelligence into prevention across every environment.

Palo Alto Networks is building that future, helping organizations reduce exposure before attacks succeed, operate at machine speed, secure AI infrastructure and modernize digital trust for the post-quantum era.

Download the 2026 Gartner® Magic Quadrant for Hybrid Mesh Firewall to learn more about why Palo Alto Networks was named a Leader.

 

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Gartner Disclaimer

Gartner, Magic Quadrant for Hybrid Mesh Firewall, 7 September 2026

This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Garner document is available upon request from Palo Alto Networks.

Gartner does not endorse any company, vendor, product or service depicted in its publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner publications consist of the opinions of Gartner’s business and technology insights organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this publication, including any warranties of merchantability or fitness for a particular purpose.

Gartner and Magic Quadrant are a trademark of Gartner, Inc., and/or its affiliates.

The post Palo Alto Networks Named a Leader in the 2026 Gartner® Magic Quadrant™ for Hybrid Mesh Firewall appeared first on Palo Alto Networks Blog.

Enterprises are seeing an unprecedented surge in AI coding adoption with spend on AI coding tools projected to top $13 billion in this calendar year¹, compounding at more than 60% annually. For engineering teams, the benefits are unmistakable: accelerated output, supercharged productivity, and operational overheads dramatically reduced.

But these AI solutions are no longer just offering helpful coding suggestions. Today’s coding agents are able to assume a user’s identity, modify file systems, execute arbitrary terminal commands, and connect directly into core business systems.

Traditional cybersecurity architectures were engineered around three foundational assumptions: known software, human users, and human-speed actions. AI coding fundamentally dismantles all three.

The Breakdown of Traditional Controls for Secure Software Development.

As AI-assisted coding accelerates, traditional security controls are quickly falling behind, creating four volatile vectors of risk: 

• Shadow AI at Enterprise Scale: Coding agents, IDE (Integrated Developer Environment) extensions, skills, and MCP (Model Context Protocol) servers permeate the enterprise before security teams even know they exist. In 2026, 68% of organizations reported lacking visibility into the AI tools their developers were actually using, and 57% of employees reported using AI coding tools without formal IT approval². 
• Risky Behavior by Agents: AI coding agents have the capability to  change files, access company resources, and execute system commands in milliseconds. In one documented³ AI-enabled intrusion chain, an agent executed 17,600 actions in 4.5 days creating a timeline no human-enabled SOC can manually govern. 
• Blurred Identity and Attribution: Machines now outnumber human identities 109 to 1⁴. And, because agents frequently operate using human credentials, distinguishing developer action from autonomous agent action becomes nearly impossible. When anomalies occur, this obscured attribution further complicates root causing the issue. 
• Uncontrolled Connections, Data, and Cost: Agents connecting to external models, MCP servers, and third-party tools outside the company, presents another distinct but related issue. All these connections also quietly increase consumption of AI resources, and separately exacerbate the risk of sensitive IP leaks. A recent survey found⁵ that 93% of enterprises exceeded their AI budgets with 20-30% of AI spend being entirely unaccounted for.  

Resetting the Controls for AI-Native Development.

Securing AI coding inside an enterprise requires a proactive, platform-centric approach:

• Discover shadow AI and what it’s using: Gain complete visibility across every model, agents, plugin, skill, and MCP to neutralize risky or unsanctioned components before they perform harmful actions.
• Assess supply chain risks: Scan skills, models, and MCP to make sure they are trusted.
• Protect identity and prompt: Inspect prompts, model outputs, and autonomous interactions in real time. Provision verified, task-scoped identities for agents and eliminate standing privileges.
• Govern data, actions, and cost: Enforce zero-trust boundaries over external model connections, governing access to file systems, sensitive corporate data, and establishing hard policies over AI resource consumption and spend.

The Platformization Advantage for Superior Governance.

Today, endpoint visibility, LLM traffic, token consumption, agent identity and actions are fragmented across disparate solutions introducing visibility gaps and creating new risks for enterprises. Palo Alto Networks delivers AI coding security natively with an integrated platform to secure what gets installed, what runs, what data leaves the enterprise, and which identity is used.

AI coding tools will continue to evolve, as they should. But your builders don’t have to choose between speed and security. The goal is not to slow down developer momentum, but to provide the security controls that serve as scaffolding for your teams to innovate faster and with complete confidence.

Secure your AI. We’ve got next.

Visit our Secure AI Coding page or download our solution brief to learn more.

 

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1 - IdeaPlan
2 - The AI Velocity Gap
3 - Hugging Face
4 - Idira 2026 Identity Security Landscape - Chapter One
5 - McKinsey; Oabo

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