You've watched the demos. Whether it's Claude Cowork, ChatGPT Enterprise, GitHub Copilot, Cursor, or internally developed agents, AI systems are no longer answering questions. They are connecting to enterprise data, invoking tools, making decisions, and executing multi-step workflows across applications without human intervention. The capability is real, and organizations are rapidly moving from experimentation to deployment.

Teams are no longer asking if they should use this, they have accepted agentic tools as the reality. But the board and the infosec team are asking a different question: can this capability be secured and controlled at enterprise scale? Can security teams prevent sensitive company data from being exchanged without oversight?

Anthropic built meaningful access controls into Cowork — role-based permissions, group spend limits, usage analytics and connector restrictions — so the answer is a qualified yes. Those controls handle who can use the tool and what they can connect to, but they don't answer whether a specific action inside a given session is safe. That gap is the one standing between a successful pilot and a successful org-wide rollout.

The Gap That a Demo Doesn’t Expose

The organization’s admin assigns roles, sets spend ceilings per user group and restricts which connectors have access to write to your database. Anthropic's OpenTelemetry support even lets your team pipe session events into your SIEM. These controls cover real ground, but they operate at the permissions level — answering whether a person is authorized to use the tool rather than whether what's happening inside a session is safe.

Consider what that gap looks like in practice. Let’s consider two scenarios. Your finance analyst has full Cowork access and uploads a quarterly forecast containing unannounced acquisition figures. The access controls confirm she is authorized to use the tool, but nothing evaluates whether that information should be exposed to a model. That's an AI data loss prevention risk, and access controls are blind to it.

The risk becomes greater when agents move beyond information retrieval and begin taking actions. Let’s say a scheduled Cowork automation is set up to pull weekly competitor pricing from the web. A target site embeds hidden instructions in its page content. The agent, running unattended, reads them as legitimate commands and begins modifying local files and triggering actions your team never authorized. By the time anyone notices, the agent has already acted.

The first scenario exposes a governance problem because your security team has no visibility into what data is flowing through AI tools across the organization. The second is a runtime security problem as there is nothing evaluating whether an action in progress is safe, regardless of whether the user was authorized to start it. Neither gap is addressed with the predefined controls in Cowork; both need to be solved before you can say yes to Cowork adoption in the whole organization.

Why Traditional Controls Break Down

Traditional enterprise software behaves predictably. Access controls work because administrators can reasonably anticipate what an authorized user or application will do once access is granted. 

AI systems operate differently. Agents combine models, tools, data sources, and reasoning paths dynamically at runtime. An authorized user may start with a simple request, but the resulting chain of actions may evolve in ways that were never explicitly programmed or anticipated. The challenge is no longer controlling who can access a system. The challenge is securing and governing what happens after access has been granted.

The Missing Layer is Runtime Security 

Anthropic's access controls establish who can use Cowork and what they can connect to. But as the examples above show, they don't protect against what happens inside a session: a finance analyst uploading sensitive acquisition data to the model, or a scheduled automation being hijacked by a malicious instruction embedded in a webpage it was directed to visit. What organizations working with Cowork need is a layer that enforces data and security controls and gives complete visibility at runtime across all Cowork agents in the enterprise every interaction boundary.

An AI runtime security layer that sits between your teams and the model providers such as  Anthropic, AWS Bedrock, Google Vertex or any combination, and evaluates risk in every interaction. It inspects every request, every tool call and detects sensitive data like client names, financial projections, internal pricing and contract terms.  It enforces agent identity controls, so every automated action is traceable to a specific workflow and owner. 

Your CISO gets the audit trail and your Infosec team gets the evidence.

The AI Enterprise Needs a Control Plane

The CIO needs the observability for all Cowork activity and costs. An AI control plane allows the CIO to set spending limits per team and use case across every AI tool from a single console. Procurement asks for a quarterly forecast across all AI spend, and you pull it from one place instead of aggregating reports from four different vendor dashboards. If you need to move providers for cost or compliance, the gateway reroutes traffic without disrupting your teams or breaking your workflows.

Claude Cowork may be where organizations begin scaling their AI journey, but it won't be the only AI tool your teams use. Developers will use coding assistants,  business teams will leverage the AI built into SaaS applications and data science teams will deploy custom agents for their workflows. New models, new providers and new workflows will continue to appear.

The challenge isn't just governing one AI application; it’s governing AI activity across the entire AI enterprise.

Everyone looks to secure each tool individually: configure Cowork's controls, configure your coding assistant's controls, configure your internal agents separately. But this approach doesn't scale. This is the sole purpose of the control plane. It sits above individual tools, applications and models and enforces  security policies,  across every AI interaction. 

Prisma AIRS AI Gateway provides that centralised control plane. Organizations that deploy Cowork behind our gateway get runtime security, data protection, agent identity controls, and full visibility, applied consistently, without changing how teams use the tool. The same gateway secures every other AI tool in your environment on the same terms.

Cowork may be where the journey begins, the gateway is what allows it to scale and secure the AI Enterprise.

The post What It Takes to Secure Claude Cowork Across the AI Enterprise appeared first on Palo Alto Networks Blog.

As artificial intelligence (AI) adoption surges and organisations move from the ‘should we?’ phase to the ‘how do we?’ phase, it’s natural to evaluate the likelihood of positive returns on AI investments. That’s always been the case with the onset of each new technology paradigm: C-suite executives, guided by their boards and aided by technical and business teams, remain keenly focused on traditional metrics such as return on investment, shareholder equity, developing and extending competitive advantage, and ensuring superior customer relationships.

This time is different, however. I recently experienced that firsthand when I went to visit a major customer. My contact, a senior decision maker, gave me a pointed piece of advice about how to talk about AI with his boss, the CEO: “Please don’t say anything negative about AI.” The subtext was clear: The company was fully committed to AI and didn’t want any cognitive dissonance to dissuade them from their mission.

It's hard to imagine a CEO taking such an absolutist stance on previous technology waves, such as cloud, bring your own device, or the internet of things. CEOs, board members, and technical leaders would be pragmatic in evaluating the benefits of investments and put mileposts in place to gauge progress – and to determine if and how to proceed.

AI is certainly a different kind of paradigm, though. While no one is casting aside careful evaluation and monitoring of AI investments, the underlying assumption is that we’re stepping on the accelerator. We’re all enthused not only by its potential for transformation and innovation, but also by how this technology can be leveraged for remarkable societal good.

However, while the accelerating momentum toward AI and agentic systems is undeniable, it is vitally important to set aside the fervour around AI and take a sober look at how to deliver safe, secure, and tightly governed systems at enterprise scale. 

Many organisations are underestimating the challenges of AI governance, in large part because they think they’ve been here before. They already have many experiences of ensuring robust cybersecurity and strict governance for new technologies, as they’ve done for remote systems, cloud computing, the internet of things, and more. They already have a corporate commitment to doing governance correctly and a sound governance model. 

But this new era of AI and agentic systems is different. New challenges abound, and AI strategy, build-out, and governance must be in alignment from the start to ensure proper operational, ethical, and regulatory outcomes. 

Our intention with this Peer Insights guide is to raise what we believe are existential issues around governance for this powerful, complex, and unprecedented technology wave. Few technologies have merited the often overused phrase ‘inflection point’ more than AI. The speed of AI adoption is nothing short of breathtaking; however, today’s runaway embrace of AI is far stronger than our current ability to govern it. That’s because AI represents a fundamental shift in how organisations do their business, interact with customers, make vital decisions, and execute their plans. This isn’t just a technology play: It’s a strategy for success and survival for entire industries and our global economy. The stakes have never been higher.

CEOs care so passionately about AI because they see it changing nearly everything we’ve learned and believed to be true about organisational success and failure. CEOs are in their positions for one purpose: to grow the business. AI can do that by transforming their processes and sparking new ideas. When that customer representative forewarned me, I really wasn’t surprised to hear his CEO felt so strongly about AI: Research from BCG indicates that more than 94% of CEOs say they still plan to deploy AI irrespective of demonstrated business value, even if there is a lack of tangible ROI or financial benefits from the start. 

Which brings us to the central role of AI governance. As we all know, there are many fundamental elements to any governance strategy, starting with robust, scalable, and intelligent cybersecurity. Cybersecurity - the foundation of governance - also includes the twin imperatives of accountability (‘rogue AI’ being a real thing, after all) and regulatory compliance.

But good AI governance has to go even further. Operational integrity is key to good governance because so much sensitive and even proprietary data is poured into AI models and accessed through powerful agentic AI systems. Now more than ever, organisations have to be transparent with customers and trading partners about how their AI systems operate, what kind of data is accessed, and how it is protected. And that doesn’t just mean being upfront with customers by telling them when they are interacting with an AI agent. Let’s take a typical retail use case: Imagine you’re on a website looking at clothing, and the agent recommends specific styles of clothing in specific colours. True operational integrity would allow you to discover why and when the agent made those recommendations. Was it based on your prior purchasing history, or on your browsing patterns on a recent web session? AI and agentic governance take the guesswork out of the equation for those interacting with the system and help breed greater confidence and trust.

It's critically important for decision makers to view AI governance holistically, rather than through a series of narrow lenses. For instance, even though cybersecurity is the foundation of good AI governance, it’s a mistake to treat AI governance primarily as a cybersecurity problem. If asked about ownership of AI governance, CEOs cannot and should not reply, “Oh yeah, the CISO has that covered.”

AI governance is fundamentally an enterprise risk problem, which means everyone must be involved in creating, deploying, managing, evaluating, and adjusting AI governance guardrails on a real-time basis. Again, AI is a different kind of risk environment than any we’ve previously encountered. For the most part, organisations are simply not adequately prepared to apply the right level and right type of governance to AI and agentic systems. I’ve spent much of the past 15 years of my career building governance frameworks, and while it has never been easy, we have had the advantage of being able to control many of the variables – such as infrastructure and network access – impacting governance decisions. With AI and agentic, we no longer have that advantage.

To explore the critical and complex issues of AI governance, we’ve enlisted five leading voices to bring their real-world experience to the discussion. Together, our five authors help lay out the new rules of the road for governing AI and agentic systems at scale.

Just as my customer gave me a heads up about the realities of speaking with his boss about AI, I’d like to offer you a heads up about the realities of AI governance challenges before you read this Peer Insights guide. 

1. Visibility is paramount for successful AI governance. As we learned during the growth of trends such as cloud, bring your own device, and remote work, our employees will push the envelope with a do-it-yourself mindset. These tech-savvy and resourceful users are already making rogue AI a reality, so organisations need more visibility than ever into where AI ‘science projects’ and sandboxes are operating without anyone’s knowledge.
2. AI governance must reflect the stunning velocity of change in AI development and deployment. Not only does AI have its own never-imagined rate of change, but the technology is changing everything else faster – product development, supply chains, marketing programmes, and more. AI governance has to evolve just as rapidly. Governance in the AI world must be a living system, constantly evolving with new technology use cases.
3. Trust boundaries are incredibly different and difficult to manage in AI governance. AI represents a new class of identity that simply didn’t exist before. That means AI doesn’t fit neatly into your existing identity management framework, making things like application whitelists and zero trust network access less effective.

Unfortunately, many CEOs, board members, and business executives simply don’t understand the profound importance and complexity of these issues. They may have been heartened by how they integrated generative AI into their technology frameworks and their business processes, but GenAI was pretty familiar territory for CIOs, CTOs, and CISOs. Agentic AI is different for several reasons, including its automation and self-learning capabilities. Don’t be lulled into a false sense of security: Agentic AI is not simply a refresh of GenAI.

As you get ready to dive into the following chapters, rethink how you define governance when applying it to AI systems and agentic AI. Most traditional governance models are imagined, constructed, and deployed as gates, preventing people from doing things or going places they shouldn’t. Instead, think of AI governance as a guardrail to guide and direct people to get the most out of AI without creating problems. With so much excitement and investment around AI, organisations – and their employees – want to get the most out of their AI and agentic systems. We all know people don’t want to hear “no, you can’t do that”, so an effective governance system should use guardrails to drive proper, responsible, and safe usage of the technology.

Finally, as complex as AI and agentic governance are and will continue to be, don’t overthink things in hopes of creating the perfect model – it doesn’t exist. My advice is to start now, even if the model and framework are imperfect, and then bring the business along with you.

We at Palo Alto Networks are excited to give you insights, ideas, and actions you can take away from the chapters of this guide. We encourage you to share what you learn with your colleagues, peers, and team members – and to take prudent steps to build an AI governance model that rewards innovation without allowing your organisation to drift into dangerous waters.

 

Haider Pasha is VP & Chief Security Officer, EMEA, Palo Alto Networks

The post It Might Feel Like We’ve Been Here Before, But We Haven’t appeared first on Palo Alto Networks Blog.

Anyone who has seen the impressive frame of Stonehenge against the morning’s sunrise cannot help but be struck by its resilience, how it has withstood time and the unpredictable impact of nature and humans. And partly because of this, a recent conversation I had with the CIO of a large healthcare technology company made me realize that it was a fitting metaphor for cybersecurity.

As our conversation wove through familiar topics — the challenges and breakthroughs in enterprise IT architecture — we recognised and discussed a recurring pattern throughout most EMEA and multinational enterprises. Those organisations have gradually but surely evolved into a mosaic of vendor fragmentation, ‘micro-platforms’ across vendor-specific technologies, and rapidly developing data silos that no single IT architecture can solve on its own. 

The increased heterogeneity of hardware, operating systems, and cloud architectures now comes with a dizzying mix of cybersecurity tools and services, often optimised for Vendor X’s platform. This has led to the situation that a large organisation typically has more than 30 cybersecurity point solutions in place to protect their digital assets. And now that we have thrown AI into that mix, designing the right cybersecurity solution is as confusing as it is imperative.

That’s when I was reminded of Stonehenge. Its lintel-and-joinery design is strikingly simple and elegant, and it stands as a brilliant monument to long-term resilience. Just as Stonehenge has endured against natural and human threats, so organisations must build a cybersecurity architecture that endures a revolutionary rate of change and threat diversity, including geopolitical turbulence and AI entering the value chain. 

For CISOs, CIOs, board members, C-suite executives and line-of-business leaders concerned with operational resilience, cybersecurity architecture matters—deeply. 

And we should not forget that cybersecurity is a data problem. The more telemetry data you have, the more effectively you can execute security algorithms and protect your digital essentials across all your enterprise IT pillars, i.e., IT, OT, Clouds, Networks, Workplace, Endpoints, etc. We at Palo Alto Networks are able to combine relevant telemetry data from networks, firewalls, clouds, browsers, endpoints and the internet. 

Stonehenge was built from massive, self-reinforcing pillars and platforms of stone. The lintels and joinery help hold together the overall structure as a cohesive unit, and they have striking similarities to how IT architects are now thinking about cybersecurity. In today’s technology architecture, Stonehenge’s vertical pillars are an IT organisation’s specialised, vendor-specific IT domains—sometimes with its own security tools and capabilities rather than as a strategically integrated zero-trust cybersecurity framework across your enterprise IT pillars.

Now, Stonehenge’s with its unique resilience, can also serve in its own construction as a model for modern cybersecurity architecture. Like our evolution towards modular platformisation evolved deliberately and assuredly over time and it spans all key domains of cybersecurity, ie network, cloud, AI,  identity security and all key building blocks for an AI-driven SOC, the last line of defense that has to be real-time. In other words, it is the linchpin of our strategy for enterprise security built upon such key areas as Identity, the Autonomous SOC, and Network Security. 

Stonehenge’s lintel is analogous to cybersecurity platformization, a growing trend rapidly replacing the now-outdated best-of-breed point solution mindset. This employs a modular approach that gives flexibility and control to the security architect looking to add security domain capabilities as needs evolve. The mortise-and-tenon joinery of Stonehenge works because the parts fit together rather than being stacked as an afterthought, in much the same way modern cybersecurity frameworks are built upon the concept of embedded functionality rather than being bolted on. 

An important example here is Palo Alto Networks’ decision to power the cybersecurity platform core with Precision AI, rather than its technology being added as a separate tool. This approach enables Precision AI to power data, analytics, and workflows, making it an omnipresent resource for smarter and faster prevention, detection and response.

Another important element of any enduring architecture is its ability to provide stability to the overall framework. In cybersecurity architecture, this is the all-important cyber data layer across an integrated zero trust framework. As organisations continue to struggle with data silos across networks, cloud environments, security operations centres, and edge systems, the cybersecurity data lake takes on a heightened role of importance for the resilience of the entire cyber framework. Again, let’s not forget, cybersecurity is a data problem, a domain in its own right across all vertical IT pillars.

Now, Stonehenge with its unique resilience, can also serve in its own construction as a model for modern cybersecurity architecture. Like our evolution towards modular platformization evolved deliberately and assuredly over time and it spans all key domains of cybersecurity, i.e.  network, cloud, AI, endpoints, identity security and all key building blocks for an AI-driven SOC, the last line of defense that has to be real-time. In other words, it is the linchpin of our strategy for enterprise security built upon such key areas as Identity, the Autonomous SOC, and Network Security/SASE. 

Another critical element of the cyber platform is something even Stonehenge hasn't had to face: securing AI itself, especially the opportunity and threat represented by agentic AI. AI security must become part of the platform design and implementation, as we have done with our Prisma AIRS (AI Runtime Security) platform for enabling an organisation's growing AI portfolio to remain a vital asset and not an inviting attack vector. Agents now are not just another non-human identity; they are an entirely new class of identity, with a striking mismatch in speed between agent decision-making and human governance. The inside-out attack paths taken by hackers' ill-intentioned agents represent a major threat to under-protected AI supply chains. The same pressure now also comes from geopolitics and from AI moving into the value chain itself, such as in the case of the Factory of the Future.

Similarly, our recent acquisition of CyberArk gives us what we believe is the industry’s strongest identity security platform, Idira, positioning it as yet another vertical pillar connected to the overall cybersecurity platform lintel. Cortex XSIAM and its security data lake are deliberately open — ingesting and correlating third-party telemetry alongside our own, over 17 petabytes of telemetry data each day — to form a secure data layer that is accessible to users based on policy management and credentials validation. Palo Alto Networks leverages this mountain of data, along with around-the-clock scanning of more than 5 billion daily security events, to feed Precision AI in order to detect and block potentially devastating attacks. Currently, we detect about 9,6m new attacks per day that have not been there the day before. The use of automated AI in attack vectors has been accelerating the time of exfiltration of data from the compromise of an organization. This delay was 9 days about 3 years ago, now data is exfiltrated in most cases in less than a day, sometimes already within less than one hour!

In this context, it's also important to highlight the importance of an Autonomous SOC pillar, particularly since compliance reporting windows are continuously contracting from days to mere hours calling for real-time, highly automated defence. Today, mean-time-to-detect and mean-time-to-respond are board-level imperatives commanding more conversation and attention at an organisation’s highest levels. The Autonomous SOC pillar is a vital element in helping enterprises achieve even faster detection and remediation, ideally down into single minutes. If it also integrates the historic enterprise SIEM you can further simplify your SOC operations and gain solid financial benefits by platformization of your security relevant data.

Finally, keep in mind the use of supply chains to build the actual platform. For Stonehenge, that was an impressive physical supply chain: The bluestones used in the structure were hauled about 250 kilometers from Wales without the benefit of air, rail, or truck transport. For Palo Alto Networks’ cybersecurity platform, the supply chain was no less impressive, but more virtual than physical, often faced with attacks on third-party interdependencies such as SaaS applications, APIs and in times of Frontier AI models, the Open Source components. 

Like the pyramids, the Great Wall of China, and the Roman road system, the most remarkable aspect to Stonehenge isn’t just its engineering elegance, but its ability to withstand changing conditions and threats over time. Whether you’re a CEO, board member, CIO, CISO or security engineer, the decisions you make about cybersecurity carry significant impact and implications. In order to achieve Stonehenge-like resiliency, technical and business leaders should commit to an architectural model designed not only for today’s needs, but for what those needs are likely to be over the long term. 

Therefore, cybersecurity should be architected as a horizontal, dedicated platform across all your IT domains and businesses. With this you are able to provide real-time and platformized cybersecurity for tomorrow. And tomorrow is going to be a more and more AI-driven business world. 

 

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

The post Built to Last: What Stonehenge Teaches us About IT Architecture & Cyber Resilience appeared first on Palo Alto Networks Blog.

The rise of Agentic AI is rapidly reshaping the enterprise, yet its deployment opens a complex new frontier for cyber threats.  As organizations race to harness the power of enterprise agents, the "Data Estate" has become the new perimeter. CISOs today face a high-stakes trade-off: enabling developers to build at the speed of AI while keeping proprietary data visible, governed, and secure across the entire AI lifecycle. This requires meticulously checking user inputs, agent outputs, and tool calls for threats like prompt injections, sensitive data loss, and malicious code, while simultaneously preventing autonomous agents from performing destructive actions.

Securing the AI-driven enterprise requires a fundamental shift from reactive measures to proactive runtime protection. Palo Alto Networks and Databricks are delivering on that vision. Our partnership will integrate the Prisma AIRS API with Databricks Unity AI Gateway, embedding seamless security at runtime. This collaboration will enable organizations to innovate with AI agents, applications, models and MCP Servers at scale while maintaining a robust, policy-driven security posture. By combining the centralized AI governance and control capabilities of the Databricks platform with the runtime security protections of Palo Alto Networks, organizations can scale AI innovation without sacrificing visibility, compliance, or security.

 

The Context: Why AI Security is Different

AI security represents a fundamental departure from traditional defense. Legacy tools are designed for structured threats, leaving them incapable of parsing the intent behind complex, conversational attacks. Furthermore, the integration of Retrieval-Augmented Generation (RAG) and autonomous workflows creates a dynamic attack surface that goes far beyond traditional data loss. Without AI-native oversight, organizations can face severe risks from prompt injections, custom topics, and toxic content manipulating model logic, to tool misuse, malware execution, and malicious URLs hijacking agent actions.

Modern AI development requires more than just a perimeter; it requires contextual intelligence. By integrating Prisma AIRS directly into Databricks Unity AI Gateway, we will evolve security from a reactive layer into a native pillar of the AI architecture.

 

The Joint Solution: Centralized Security at the Gateway

The most effective way to secure an entire AI environment is at the governance layer. Our integration focuses on Databricks Unity AI Gateway, which serves as the centralized interface for all AI activity within the Databricks environment. Unity AI Gateway is designed for managing, governing, and monitoring access to all models, agents and MCP Servers—whether they are open-source models deployed within Databricks or external proprietary models. As organizations deploy more agents, applications, and models, centralized governance becomes critical. Unity AI Gateway provides a single control plane for AI usage, enabling teams to apply consistent policies, monitor activity, and manage access across AI workloads.

Through this integration, Unity AI Gateway will make real-time calls to the Prisma AIRS Runtime Security API for security inspection. Instead of managing fragmented security policies across dozens of individual applications, SecOps teams will be able to enforce consistent guardrails across the entire Agentic AI estate from one location, providing a single, unified enforcement point for all AI workloads.

Figure 1: Centralized AIRS guardrail configuration delivers instant protection across all applications, agents and MCP Servers without requiring client-side code refactoring

 

Mechanism: API Intercept for AI Runtime Security

Prisma AIRS operates as an advanced inspection layer, leveraging its API Intercept capability to provide real-time security embedded directly into the application flow. By embedding Prisma AIRS directly into the workflow, we offer a seamless 'Security-as-Code' experience that unifies development and defense. Prisma AIRS intercepts AI prompts, responses, and MCP calls—inspecting them in real time to enforce security policies with an immediate Go/No-Go verdict or by sanitizing the data in transit. Prisma AIRS uses deep learning classifiers to detect data exfiltration risks, such as the presence of PII (Personally Identifiable Information), PHI, or PCI data. If sensitive data is found, it can be dynamically redacted or blocked based on corporate policy.

 

Key Benefits for the Enterprise

This integration isn't just about blocking threats—it’s about accelerating your AI roadmap. By removing the "security friction" that often slows down production deployments, we enable teams to move faster with confidence. Key benefits include:

• Zero-Friction Governance: Developers continue working within their familiar Databricks environment. Security is enforced via the Unity AI Gateway API, meaning there are no bulky agents to install and no complex architectural re-wiring required.
• Prevention of Data Leakage: Leverage Prisma AIRS’s data classifiers to automatically protect sensitive intellectual property, preventing data leaks to public models and unauthorized users.
• Resilience Against AI-Specific Attacks: Protect your Unity AI Gateway deployments from emerging threats that standard network security tools cannot see, including prompt injection, toxic content, custom topics, malware detection and malicious URL detection.

 

Key Takeaway

• Ease of use and unified Policy Management: Enable runtime security through the Unity AI Gateway to gain centralized control over security enforcement.
• Audit-Ready Compliance: Every transaction mediated by the Unity AI Gateway is logged with detailed security metadata, delivering enriched insights in Strata Cloud Manager. This provides the forensic trail required for regulatory compliance in highly governed industries like finance and healthcare.
• Protection for Agentic Workflows: Future-proof your multi-step AI agents against sophisticated Agentic Threats by inspecting function and tool calls within the runtime.

 

Looking Ahead

As agentic workflows and multi-step model interactions become the standard, a 'fail-closed' runtime security posture is no longer optional; it is foundational. The integration of Prisma AIRS API and Databricks Unity AI Gateway marks a definitive shift toward a future where enterprise AI is secure by default.  By integrating Prisma AIRS API with the Databricks platform through Unity AI Gateway, organizations can centrally govern AI across models, agents, applications, and MCP servers while enforcing consistent runtime security policies. Together, Databricks and Palo Alto Networks are helping customers scale AI innovation with the control, visibility, and protection required for the agentic era.

Are you ready to secure your AI workloads and agentic applications?
check out the latest Databricks blog and stay tuned for technical deep-dive sessions coming soon.

 

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 Securing the Agentic AI Frontier: Palo Alto Networks and Databricks Deliver a New Standard for AI Security appeared first on Palo Alto Networks Blog.

AI security is getting attention because AI has stopped being a side experiment.  It is now part of how work gets done. Employees use copilots to write, research, code, and analyze. Product teams are adding AI into customer experiences. Developers are building applications on top of foundation models. Business teams are experimenting with agents that can read email, summarize documents, query data, and trigger workflows.  That is a very different world from the one many AI review processes were designed for.  An AI system can pass a benchmark and still fail in production. It can behave safely in a clean test environment and then encounter real […]

The post AI Red Teaming Makes the Unknowns Known appeared first on Check Point Blog.

Shadow AI is no longer a theoretical risk. Employees are adopting AI tools faster than security teams can track them, often without IT’s knowledge, and frequently on devices and surfaces that traditional security tools simply can’t see. If you asked your security team right now how many AI tools are active across your organization, on which surfaces, and what’s being shared, could they answer? For most organizations, the honest answer is no. And that gap, between what your employees are doing with AI and what your security team can actually see, is where enterprise risk lives today.  AI adoption in the enterprise didn’t slow down and wait for governance to catch […]

The post The AI Your Security Team Can’t See Is the One You Should Worry About appeared first on Check Point Blog.

The model behind a security workflow shapes how fast a threat is caught, how accurately an incident is investigated, and how much a defender can trust the result. We treat that choice with care. Today we’re taking a clear step forward: Check Point has joined OpenAI’s Daybreak initiative through its Trusted Access for Cyber (TAC) program. These are real steps in how we bring AI into our defensive operations, and in the security we deliver to our customers. What Trusted Access for Cyber Gives Us Trusted Access for Cyber is OpenAI’s program for vetted security organizations that need its most […]

The post Check Point Joins OpenAI’s Trusted Access for Cyber Program and Daybreak Initiative appeared first on Check Point Blog.

Key Findings:   Check Point Research identified a critical vulnerability chain in LangGraph, an open-source framework from the creators of LangChain that enables developers to build complex, stateful, and controllable AI agent workflows using LLMs; they have approximately 46.5 million monthly downloads, making it one of the most widely adopted AI agent platforms in the world An SQL injection in LangGraph’s function could allow attackers to gain full control via remote code execution of a server by exploiting weaknesses in how the system processes and handles data. A compromised LangGraph server exposes everything the agent touches, including LLM API keys, customer data, CRM credentials, conversation history, and internal network […]

The post When Your AI Agent’s Memory Becomes a Security Liability appeared first on Check Point Blog.

Frontier AI is moving faster than most governance and response systems were designed to handle.

The corporate landscape across the Japan and Asia-Pacific (JAPAC) region is facing an unprecedented regulatory and operational reckoning. The rise of hyper-autonomous ‘frontier’ AI models is pushing cyber security out of human hands and into a real-time war of machine against machine. This shift has triggered a highly coordinated enforcement wave cascading through JAPAC’s premier digital hubs, where regulators and enterprises are moving in lockstep to address machine-speed threats. 

With corporate watchdogs Australian Prudential Regulation Authority (APRA) and Australian Securities and Investments Commission (ASIC) firing warning shots via urgent market letters, and neighbouring authorities like the Monetary Authority of Singapore and South Korea’s central government enacting strict new AI safety rules, organisations are being forced to completely overhaul their defensive architecture. Decades of relying on slower, committee-based governance are being shattered by new threat intelligence showing that autonomous AI agents can now exploit vulnerabilities and exfiltrate critical data within minutes—turning traditional 72-hour regulatory reporting windows into mere post-mortems.

The warning comes as the gap between corporate readiness and technological reality widens right across the JAPAC corridor. Much of the region’s current governance and cyber risk architecture still reflects a legacy system engineered for predictable, slower-paced environments. We have spent years building risk models where vulnerability discovery, incident escalation, and defensive response unfold gradually enough for traditional executive oversight and committee structures to remain effective. But that comfortable pace has officially vanished.

The Machine-Speed Reality

The sheer velocity of this shift was highlighted during restricted testing of Anthropic’s advanced frontier model, Claude Mythos, under an initiative known as Project Glasswing. Palo Alto Networks was among a select group of technology and cyber security organisations chosen to evaluate the implications of the model before its broader release. Mythos demonstrated an unprecedented capability to identify and exploit vulnerabilities across major operating systems at a level matching or exceeding advanced human experts.

During combined testing involving Mythos, Claude Opus 4.7, and OpenAI’s GPT-5.5-Cyber, the real-world impact of machine speed became starkly visible. In a single month, Palo Alto Networks disclosed 26 Common Vulnerabilities and Exposures (CVEs) representing 75 distinct issues, a massive surge compared to a typical monthly volume of fewer than five CVEs.

While discovering flaws at that scale would historically have raised uncomfortable questions around software quality, the landscape has fundamentally shifted. In this new era, radical transparency, paired with the ability to reflect and act instantly, has emerged as a critical corporate superpower. Frontier AI is accelerating both sides of the digital chessboard simultaneously: while attackers are gaining unprecedented speed, defenders are gaining a level of visibility that simply did not exist a few years ago. Real-time warfare between AI defenders and AI attackers is rapidly becoming the standard operating model.

AI Agents: The New Corporate ‘Insiders’

This shift introduces a profound dilemma for corporate leadership. Recent regulatory guidance repeatedly emphasises the necessity of human supervision, and for good reason—ultimate accountability must always remain with people. Boards must still set risk appetite, Chief Information Security Officers (CISOs) must determine operational thresholds, and security teams must decide how much authority autonomous systems should hold inside critical environments.

However, organisations must now look a step further. Autonomous AI agents—operating on behalf of employees, suppliers, or automated workflows—are quickly becoming the new corporate ‘insiders’. If not managed with extreme care, they represent massive, systemic blind spots.

Current identity and access frameworks are starting to buckle under the strain because they were never built to distinguish between human users and autonomous agents acting on their behalf. Traditional identity systems assume a predictable human pattern: a user authenticates, requests access, and operates within set boundaries. Autonomous agents, by contrast, interact continuously with APIs, generate code on the fly, move fluidly across workflows, and operate with delegated authority from trusted users.

When these agents begin operating deep inside critical infrastructure, financial services, or government workflows, the risk profile changes entirely. Security teams are no longer just dealing with stolen passwords or human misuse; they are managing autonomous systems capable of acting at machine speed across highly interconnected environments, with potentially devastating consequences if control is lost.

The Failure of the 72-Hour Window

This acceleration has effectively broken traditional regulatory reporting timelines. Recent threat observations from Unit 42 reveal that in approximately 20 percent of modern breaches, attackers successfully exfiltrate data within the very first hour of a compromise.

When data theft occurs inside 60 minutes, a 72-hour reporting window ceases to function as an effective defense mechanism. Instead, it becomes a post-mortem.

For example Australia’s current reporting obligations—including those under the SOCI Act, CPS 234, and the Privacy Act—were largely designed for static environments where defenders had sufficient time to investigate, escalate internally, and coordinate remediation before damage spread. Today, many CISOs quietly acknowledge the immense operational strain created by overlapping reporting frameworks during a live crisis. In the chaotic early stages of a compromise, security teams frequently find themselves managing compulsory reporting requirements from different regulators while their engineering teams are still actively trying to contain a fast-moving incident.

A Region-Wide Regulatory Reckoning

Australia is far from alone in this challenge. The regulatory anxiety echoing through the halls of APRA and ASIC is part of a highly coordinated, region-wide crackdown across the Japan and Asia-Pacific (JAPAC) tech corridor. As frontier models shrink the ‘time-to-exploit’ to near zero, neighbouring digital economies are rapidly realising that their legacy frameworks are equally vulnerable.

In Singapore, the regulatory response has been immediate. The Cyber Security Agency (CSA) recently issued a stark advisory warning that advanced frontier models can examine complex codebases and automate attacks faster than human developers can write patches. In lockstep, MAS finalised its Guidelines on AI Risk Management. Under these new rules, financial institutions are now mandated to perform continuous ‘AI Cyber Stress Testing’— requiring boards to prove that complex, autonomous AI-to-AI interactions within their systems won't trigger an unmanageable domino effect.

Meanwhile, South Korea has shifted from guidelines to hard law. The nation's landmark AI Basic Act (Framework Act on Artificial Intelligence) has officially entered into force, creating strict compliance mandates, mandatory data audits, and extraterritorial penalties for any enterprise deploying high-impact AI systems without ironclad human guardrails.

Across JAPAC, a uniform regulatory shift is underway: voluntary AI ethics frameworks are being replaced by proactive, real-time enforcement measures. 

Moving with Discipline

Organisations broadly acknowledge that AI demands a distinct approach, yet implementation gaps remain. Businesses must move away from managing AI like standard software and instead commit the significant defensive resources needed to protect complex AI supply chains. 

The language coming from regulators reflects these exact challenges. ASIC Commissioner Simone Constant warned that frontier AI capability could expose vulnerabilities at unprecedented speed and scale, creating systemic consequences across entire sectors. Her message to corporate Australia was direct: do not wait for perfect clarity to address the threat posed by new AI models. Instead, organisations must act now, and act with discipline, to strengthen the cyber resilience fundamentals that underpin their businesses.

The testing conducted within Project Glasswing ultimately proved that while frontier models can expose weaknesses at terrifying speed, that exact same capability can be weaponised defensively. By deploying AI to reduce exposure and identify vulnerabilities before adversaries can operationalise them, organisations can effectively level the playing field.

The most resilient organisations over the next few years will be those that combine real-time frontier AI defensive capabilities with disciplined human supervision, rather than treating the two as separate priorities. In the era of machine-speed warfare, you cannot successfully have one without the other.

To learn more about how we are securing the frontier of technology, visit the Palo Alto Networks Trust Center and explore the latest threat insights from Unit 42.

The post Beyond Human Oversight: Adapting to the Frontier AI Era appeared first on Palo Alto Networks Blog.

Enterprise security has always had a comforting assumption baked into it: systems do what they were built to do. Sometimes badly. Sometimes insecurely. Sometimes in ways that make auditors develop a nervous twitch. But still, the basic shape was understandable. Applications processed requests. Databases stored data. APIs connected systems. Users clicked things they probably should not have clicked. Then AI arrived and made the whole thing a little weird. AI did not introduce one neat new risk category. Security teams are very good at turning new risk categories into taxonomies, dashboards, and meetings with names like “working group.” The real […]

The post The AI Defense Plane: Securing the New Enterprise Execution Layer appeared first on Check Point Blog.

When people hear about hackers “asking an AI chatbot” to help them take over Instagram accounts, the instinctive reaction is to file it under prompt injection, jailbreaks, or “the model got tricked.”  That may be the wrong lesson.  According to reporting from 404 Media, hackers claimed they used Meta’s AI support chatbot to gain access to high-profile Instagram accounts by asking it to change the email address associated with the target account. The reported incidents coincided with several high-profile account takeovers, including accounts linked to the Obama White House, Sephora, and the Chief Master Sergeant of the Space Force.    […]

The post The Meta AI Account Recovery Incident Wasn’t Just a Chatbot Problem appeared first on Check Point Blog.

At GTC Taipei during COMPUTEX 2026, NVIDIA is highlighting the growing adoption of its NVIDIA Vera BlueField-4 STX architecture and introducing new NVIDIA DOCA-powered innovations designed to secure the next generation of enterprise AI infrastructure. As organizations continue scaling AI factories, private LLM environments, distributed inference systems, and increasingly autonomous AI operations, enterprise infrastructure requirements are rapidly evolving. Modern AI environments combine high-performance compute, distributed storage systems, inference pipelines, Kubernetes clusters, APIs, GPU server farms, and sensitive enterprise data operating continuously at enormous scale. At the same time, AI-driven environments are introducing increasingly dynamic machine-to-machine interactions across infrastructure, applications, and […]

The post Check Point Lays the Groundwork for the Future of AI Factory Security with NVIDIA appeared first on Check Point Blog.

At Check Point we don’t wait for threats to evolve; we evolve ahead of them. This is why we’ve been running our Frontier AI Models Readiness Program: a proactive, structured initiative designed to ensure that our products remain resilient as AI models grow increasingly capable of understanding complex software systems and assisting adversaries in attacking them. As part of this program, we conducted large-scale AI-driven code scanning across our products, performed extensive security reviews, hardened components where needed, refined our time-to-patch procedures, and accelerated our protection development processes to meet the pace of emerging AI-driven threats. Today’s Jumbo Security Release […]

The post Check Point Frontier AI Models Readiness Program – Security Update appeared first on Check Point Blog.

As AI rapidly reshapes industries, the role of the cloud has become even more critical. From automated customer experiences to intelligent cyber security and predictive analytics, AI transformations are increasingly being built on a cloud-first foundation. Over the past two years, AI has swiftly moved from an experimental state to an operational reality, with every leading organization embedding AI into the core of how they build, operate, and compete. However, security architectures have not kept pace with the AI transformation. Closing that gap requires more than incremental fixes. It demands a rethinking of how security is designed, deployed, and enforced […]

The post 2026 Cloud Security Report: Why Traditional Network, Cloud, and Security Architecture Are Lagging Behind the AI Transformation appeared first on Check Point Blog.

Learn how AI transforms cybersecurity through enhanced threat detection, new attack methods, model vulnerabilities, and the evolving skills teams need in 2026.

The post Top AI Risks Every Security Team Should Be Testing For appeared first on OffSec.

By now, you’ve heard about the latest frontier AI models that are remarkably good at finding vulnerabilities in code and creating potential exploits. So good, in fact, that these models have been significantly limited from general use in an attempt to give defenders time to find and fix vulnerabilities before attackers find and exploit them.

For context, on April 7, 2026, we began testing Anthropic’s Claude Mythos model as a launch partner for Project Glasswing. Our conclusion was clear: The latest models are extraordinarily capable at finding vulnerabilities and changing them into critical exploit paths in near-real-time. In Defender's Guide to the Frontier AI Impact on Cybersecurity, I shared our early findings and recommendations.

Since then, we’ve continued testing the latest frontier AI models, including Anthropic’s Mythos and Claude Opus 4.7 and OpenAI’s GPT-5.5-Cyber as part of the Trusted Access for Cyber program. The big question just a few weeks ago was: “Are we overstating the model capabilities?” With more testing, I can confidently say we weren’t. In fact, these models are likely even better at finding vulnerabilities than we initially realized. Today, we’re providing an update on our ongoing research, our learnings uncovered in the process, and the approach we’re taking to protect our customers.

Find and Fix Before Attackers Find and Exploit

Today, we released our May “Patch Wednesday” security advisories, our monthly cadence of transparent vulnerability disclosure and remediation. This is the first time where the majority of findings were the result of frontier AI models scanning our code.

• These are the results of the full, initial scan of over 130 products across all three platforms.
• As of today, we’ve patched all important vulnerabilities in our SaaS delivered products, and all customer-operated products now have patches available.
• Today’s advisory covers 26 CVEs (representing 75 issues) versus our usual volume (typically less than 5 CVEs in a month); none of which are being exploited in the wild. Note, this excludes CyberArk vulnerabilities, which are disclosed in their normal process.

It's important to understand this isn’t a one-and-done situation. We’re now rescanning, applying all our learnings about how to provide the right context and threat intelligence to the models. We intend to fix every vulnerability we find before advanced AI capabilities become widely available to adversaries.

While incredibly powerful, AI models aren’t simply magic. To achieve high-fidelity results, you need to build AI scanning harnesses, leverage context, guardrails and threat intelligence. We’ve also discovered a variance across models, due to variations in their training. A multimodel approach is required to identify the superset of vulnerabilities. And finally, while the immediate priority is finding and fixing the vulnerabilities that organizations currently have, the longer-term shift is incorporating these models directly into the software development lifecycle. This is the light at the end of the tunnel: A future where software is secure by design.

Four Steps Every Organization Needs to Take Immediately

Regardless of the current restricted access, we believe these capabilities will flow more broadly to other models. We now estimate a narrow three-to-five-month window for organizations to outpace the adversary before AI-driven exploits start to become the new norm. This impending vulnerability deluge demands urgency. Organizations that haven’t put appropriate safeguards in place will face an entirely new class of risk. Here’s what we recommend:

1. Find and Fix Vulnerabilities In Your Applications, Products and Code
   Find and fix before attackers find and exploit.
   • Leverage AI models to identify vulnerabilities across all codebase.
   • Apply the same AI scanning to your open-source supply chain, and remediate or mitigate findings.
   • Run accelerated patching tightly coordinated with product and development teams.
2. Assess, Reduce and Remediate Your Exposure
   Reduce what is reachable by attackers, secure what must be accessible, such as customer-facing applications.
   • Attack surface management products, like Cortex Xpanse^®, have never been more critical for finding and reducing exposure.
   • The latest frontier AI models are very adept (with the right AI scanning harness) at evaluating exposures, understanding security misconfigurations and prioritizing attack-path reachability.
   • Audit your supply chain, including AI infrastructure, runtime environments and model dependencies.
3. Ensure Attack Protections
   Vulnerability exploits are typically just one step of a multi-step attack lifecycle. Ensuring best-in-class protections is now even more important for preventing breaches.
   • Map current sensor coverage to identify critical blind spots in detection, prevention and telemetry.
   • Deploy best-in-class XDR everywhere with an emphasis on real-time ML-based detection and prevention of attacks with all hosts on-premises and cloud included.
   • Deploy Agentic Endpoint Security to secure wide-scale adoption of vibe coding and AI security across the enterprise (e.g. Prisma AIRS^® and our recent acquisition of Koi are now a necessity for securing the agentic endpoint).
   • Secure enterprise browsers with AI-based security are a must have for securing where users now do their work.
   • Zero trust and Identity Security are foundational to securing every user and connection, extending to internal segmentation and outbound application connections.
4. 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).
   • Attack detections must be AI/ML-driven to detect even frequently changing and novel attacks at scale.
   • These AI detections must operate against a wide range of first party and third party data sources. A best in class AI SOC must operate on ALL relevant data sources.
   • Automation, both natively integrated and throughout the SOC lifecycle, is necessary to achieve single-digit MTTR. This automation will increasingly be agentic.
   • This must be delivered as a platform to remove seams and gaps created by point solutions.
   • Assess and act as quickly as possible.

Fighting AI with AI — AI Frontier Security Innovations Coming Soon

So far, frontier AI models only find new attacks, not new attack techniques. This means that with the right innovations, we can expand our use of AI to solve the security challenges that organizations are facing, and deliver what our customers need to stay ahead of the ever-evolving threat landscape, including:

• Reimagining virtual patching with proactive, high-fidelity content updates across network, endpoint and cloud security – We expect that across open source and technology suppliers there will be a deluge of patches, and virtual patching will provide a mitigation layer necessary to give your teams time to update. We expect to roll out the first phase of capabilities very soon.
• Enhanced attack preventions, including cyber-LLM trained ML and small language models (SML) and behavior protections – Early testing with Cortex XDR^® and our network security security services, such as WildFire^® malware prevention, indicate high protection coverage from the types of attacks created using these new frontier AI models.
• Using these models to scan our code, applications and even security configurations – Our intention is to productize these capabilities and incorporate them into our platforms.

Unit 42 — We’re Here to Help

We recognize that not everyone has the capacity and/or expertise to action all of the recommendations to effectively counter frontier AI-driven risks in the short timeframe mandated by AI innovation. Our Unit 42 Frontier AI Defense service is designed to discover and remediate your current exposure before attackers do, strengthen controls that reduce exposure and contain impact and modernize security operations so teams can detect and respond at machine speed.

This is a pivotal moment for our industry. While the scale of the challenge presented is real, I’m confident in our ability to solve it. We’re here to help our customers navigate this transition and ensure that as the landscape continues to evolve, the advantage remains with the defender.

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 Defender's Guide to the Frontier AI Impact on Cybersecurity: May 2026 Update appeared first on Palo Alto Networks Blog.

How Nations Hack, Why Attribution Fails, and What AI Changes

Executive Summary:
Code War author Allie Mellen, argues that cyberwarfare must be understood through a human and geopolitical lens to close the knowledge gap between the security community and the public.

Disclaimer:
This post reflects the perspectives shared in the book Code War: How Nations Hack, Spy, and Shape the Digital Battlefield, and does not represent the views of the publisher of this blog.

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The summer of 1983, President Reagan watched WarGames at Camp David and couldn't get it out of his head. A week later, he walked into a White House meeting with cabinet members and Congress and launched into a detailed plot summary of a Matthew Broderick movie about a teenager who nearly hacks the world into nuclear war. The room full of defense experts sat uncomfortably, suppressing smirks. Then Reagan turned to General John Vessey, Chairman of the Joint Chiefs, and asked if something like that could actually happen.

Vessey came back a week later with an answer: "Mr. President, the problem is much worse than you think."

Fifteen months after that, Reagan signed a classified presidential directive titled "National Policy on Telecommunications and Automated Information Systems Security" – the first federal policy of its kind. A movie had done what years of expert warnings hadn't: It made the most powerful person in the world stop and ask the right question.

Allie Mellen, author of Code War: How Nations Hack, Spy, and Shape the Digital Battlefield, loves to tell this story, and it captures exactly why she wrote the book. In a conversation recorded at RSA 2025, Mellen joined Threat Vector host, David Moulton, to talk about nation-state threats, attribution pitfalls, and why the security industry's biggest problem isn't technical.

"They're human stories, and if we can communicate them that way to the general public, then we'll get more people interested in cybersecurity, invested in cybersecurity, and invested in protecting their data."

That gap, between what the security community understands and what everyone else grasps, is the core problem Mellen set out to solve. And in today's geopolitical moment, closing it has never been more urgent.

Every Nation Hacks Differently

One of the central arguments in Code War is that you can't understand a nation's cyber behavior without understanding its history, doctrine and social contract. China, Russia, Iran, North Korea and the U.S. each approach offensive and defensive cyber operations from completely different starting points, and those differences matter enormously to defenders.

China operates with patience. Its attacks tend to be low and slow, focused on long-term espionage rather than loud disruption. But that changes sharply in its own region, where operations targeting Taiwan are aggressive and relentless. Russia, by contrast, is bombastic; they want you to know it was Russia. Its influence operations have been some of the most effective in modern history, studied and imitated by Iran and others.

Interestingly, the very system China built to protect itself has become a liability in one specific domain. Because Chinese operators live behind the Great Firewall, without access to western social media, they lack the cultural fluency that makes Russian disinformation so effective. "They try to use memes, but it's like ‘uncanny valley’," Mellen explains. "They just slightly miss every time and so it doesn't go viral." The walled garden that gives China control over its own population makes it harder to manipulate everyone else's.

Attribution Is a Geopolitical Tool, Not Just a Technical One

Mellen is careful about attribution, and she wants defenders to be too. The standard technical signals (coding language, infrastructure patterns, operational hours) are necessary but not sufficient. Nation-states, especially the U.S., have developed tools specifically designed to mimic other actors' signatures. AI will make that problem significantly worse.

But the bigger issue is motivation. Mellen walks through a case from the Olympics where an attack was initially attributed to North Korea, even though North Korea was actively trying to normalize relations at the time by sending Kim Jong Un's sister to the games. The actual perpetrator was Russian, using a false flag to obscure its involvement. The lesson: Attribution requires asking not just "who has the technical capability?" but "who has the motive right now, given everything happening geopolitically?"

The pitfalls are real:

• Tools once used exclusively by intelligence agencies are now publicly available, making code signatures unreliable.
• Working-hours analysis is easy to spoof, especially for sophisticated actors.
• Government-controlled research in adversarial nations can deliberately skew attribution findings.
• False flag operations are increasingly sophisticated and harder to disentangle.

Why Your Data Is a Geopolitical Asset

One of the more powerful sections of the conversation centers on a question Mellen hears constantly: why would China care about my data?

Her answer cuts through the dismissiveness. These nations aren't collecting data out of idle curiosity. They're willing to constrain companies for it, invest billions in infrastructure for it, and in some cases, far worse. "Whether you wanna be involved in that system or not, you are involved in that system," she says. "And so you can either choose to take control of your information in that environment, or you can just pretend like it's not your problem."

The historical context she offers is striking. One of the driving forces behind GDPR in the EU was the collective memory of how Nazi Germany used data to target Jewish people during the Holocaust. Europe built privacy protections into law because it had seen what happens when governments gain unrestricted access to population data. That's not an abstract concern. It's a lesson written in history that the rest of the world is still catching up to.

AI Makes Everything Harder

Mellen isn't optimistic about the trajectory. Attribution is about to get much harder. Attacks are about to get much more dynamic. And AI is the reason for both.

She points to research on Chinese state-sponsored actors using AI to orchestrate attacks across the full kill chain, with only a couple of human checkpoints in the loop. The implication isn't just faster attacks. It's more adaptive malware that can adjust to different operating environments, more convincing disinformation that clears the cultural context bar, and reconnaissance-to-exploitation cycles that move faster than most defenders can process.

The constraints that have always slowed sophisticated attackers – understanding the operating system, identifying vulnerabilities, crafting exploits, mimicking attribution – all get easier with AI. All of that becomes more dynamic. And most enterprises, Mellen acknowledges, are not yet equipped to respond effectively.

The investment required is in the basics the industry has always struggled to get right, executed now at a pace and scale that demands automation and AI on the defensive side. Fighting AI with AI isn't a vendor talking point. It's the only math that works.

More to Explore

The nation-state threats Mellen describes aren't theoretical. Unit 42 responded to more than 750 major incidents in 2025. See what they found. Download the 2026 Global Incident Response Report.

Listen to the full conversation with Allie Mellen, author of Code War, on the Threat Vector podcast

The post From WarGames to Cyberwar appeared first on Palo Alto Networks Blog.

Key Takeaways

• Built on the Pioneers of PAM (privileged access management): Idira is Palo Alto Networks next-generation identity security platform, extending privileged access controls to every human, machine and AI agent identity in the AI enterprise.
• Zero Standing Privilege by Default: Idira replaces static, always-on access with dynamic privilege, granted just-in-time on a single control plane.
• AI-Driven Identity: AI runs natively inside Idira to surface hidden entitlements, unmanaged accounts, recommend least privilege, and remediate to close the gap between attackers who move in 72 minutes and defenders who historically took days.

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Since Palo Alto Networks and CyberArk came together in February, customers have been asking me the same question: What does the future of identity security actually look like?

At IMPACT, I got to answer that question.

I am proud to introduce Idira, the next-generation identity security platform from Palo Alto Networks. Idira secures every identity in the AI enterprise (human, machine, AI agent) on a single control plane that discovers risk, applies privilege dynamically, and governs the full lifecycle from first access to last session.

Idira begins with a belief shaped by more than 20 years of working on this problem. Privilege is the most challenging aspect of identity security. For a generation, the industry learned how to manage it well for a small population – administrators inside the most security-sensitive organizations in the world. That was necessary. But it is no longer enough.

The moment has come to extend that same rigor to every identity, because every identity today carries the power to move the business, or enable an attacker. That is the journey Idira takes us on. From privilege controls for administrators, to privilege controls for every identity.

Attackers Are Not Breaking In. They Are Logging In.

For most of the last two decades, identity security was built on a comfortable assumption: One can maintain a firm divide between a small number of powerful administrators and a much larger number of ordinary users; that is enough to secure the organization. That assumption no longer holds.

Our Chairman and CEO, Nikesh Arora, calls it the “IAM fallacy,” and the data in the 2026 Identity Security Landscape Report makes clear why it is time to retire this assumption.

Based on responses from 2,930 cybersecurity decision-makers worldwide:

• Machine identities now outnumber humans by 109 to 1. Of those, 79 are AI agents.
• 91% of organizations already run autonomous agents in production.
• 90% of organizations suffered an identity-related breach in the past 12 months. 83% of organizations suffered two or more incidents.

The old model is not failing because identity became less important. It is failing because identity and privilege became universal and ubiquitous.

Every major breach I have studied over the last two years follows the same pattern. An attacker steals a credential. They move laterally using standing access that should have expired. They escalate privilege. They reach the data, the infrastructure or the business systems they came for: Okta, MGM, Microsoft. Different industries. Different scales. The same pattern.

One overprivileged identity unlocks the entire enterprise.

And when defenders have a chance to respond, they are already behind and disadvantaged. 97% of practitioners tell us that fragmented tools add 12 hours to every identity incident response time. All while Unit 42^® has observed the fastest attackers move from a first foothold to exfiltration in as little as 72 minutes.

Identity is now the enterprise perimeter. And the perimeter was built for a threat model that no longer exists.

Every Identity Is Privileged — Idira’s First Fundamental Principle

The premise of Idira is simple. Every identity in your organization is privileged.

Every login, every token, every service account, every workload, every AI agent can trigger a workflow, call an API, or reach sensitive data. Some can create and destroy infrastructures, direct organizational spend, or create new identities. Privilege is no longer reserved for a small class of administrators. It is distributed across the enterprise, quietly and continuously, every second of the day.

The controls that protect privilege cannot be reserved for the few, either.

Idira changes three things from day one.

First, We Discover

Idira continuously finds every identity, every entitlement and every access path across your entire environment: humans, machines, workloads, secrets, certificates and AI agents everywhere – on the network, in the cloud, on servers and endpoints, in the browser. If someone or something can authenticate, Idira knows it is there, knows what it can reach, and evaluates how much of that access is actually necessary.

Second, We Control

Idira replaces static, always-on accounts attackers rely on with dynamic privileges that exist only in the moment of use. Zero standing privilege moves from aspiration to default, and it applies equally to the administrator logging into production, the developer deploying code, and the AI agent calling a tool. This is the shift to identity-centric active security.

Third, We Govern

Idira automates the identity lifecycle end-to-end. Governance stops being a quarterly compliance exercise and becomes a continuous enforcement loop. The 12-hour fragmentation tax closes.

This is what I mean when I say we are democratizing privilege controls. We are not loosening them. We are extending the strongest privilege controls the industry has ever built to every identity that now carries the weight of the business, without penalizing these identities for the powers they carry.

Already Better Together

Idira is not launching into an empty runway. We have been executing against this roadmap since the day we joined Palo Alto Networks, and the early results give us real confidence in what comes next.

Earlier this year at the RSA Conference, we launched Next-Generation Trust Security (NGTS), the first network-native platform to automate certificate lifecycle management and accelerate post-quantum readiness. That matters because 71% of organizations have not yet automated certificate renewal. As public TLS lifetimes compress to 47 days and manual workloads multiply, that gap becomes more than an operational burden. It becomes a business continuity risk.

NGTS closes it in the network itself.

As one of the core platforms of Palo Alto Networks along with Strata^® and Cortex^®, Idira is providing deep identity integrations across the entire portfolio to enhance platform value for customers. Prisma^® Browser delivers privileged access directly in the place where enterprise users work. Prisma AIRS 3.0 natively integrates with Idira to extend deep identity security and privilege controls to AI agents. Cortex will receive first-party identity signals to sharpen detection and take automatic identity- and privilege-driven response actions when indicators of compromise are detected.

Customers are already seeing the impact. Northern Trust improved password compliance by 137 percent. Panasonic Information Systems rebuilt its security operations around identity. Healthfirst grounded its zero trust program in identity-first controls. PDS Health secured clinical access for more than 900 practices. They had different problems with the same answer.

Different challenges. One answer. One platform. Consistent privilege controls applied to every identity that matters.

AI Makes This Urgent. AI Makes This Possible.

AI has changed the speed, scale and economics of identity risk.

Frontier models have crossed a threshold. Anthropic's Claude Mythos Preview has already identified thousands of zero-day vulnerabilities across the operating systems and browsers that businesses rely on every day. Every exposed secret, every standing admin path, every forgotten service account can now be discovered, validated and weaponized faster than most security teams can respond. 55% of the decision-makers in our 2026 survey named AI-enabled threats as their top identity concern.

Our answer is clear: We fight AI with AI.

If frontier models are rewriting the economics of attack, the only credible response is to rewrite the economics of defense with the same technology.

Idira is how we do that in identity. AI is built into the platform to surface hidden entitlements, identify risky access combinations, recommend the least privilege automatically, and drive surgical remediation. That same intelligence lets attackers find the weakest link in 72 minutes and helps defenders close it in seconds.

When code cannot be patched fast enough, identity becomes the control plane that can still adapt at machine speed.

Same Mission, Stronger Together

For more than two decades, the pioneers of privileged access have management-built controls trusted to safeguard the world's most critical environments. That mission created a category and earned the trust that made today possible.

Idira carries that mission forward and expands it to match the scale of the problem we now face.

This is the first wave, not the last. The roadmap extends privilege controls to workforce identity, advances machine and agentic identity security, and unifies a fragmented market into one platform. We are building it in the open, shaped by the customers in the room with us at IMPACT and by the realities they face every day.

The future of identity security will not be defined by access alone. It will be defined by control. See what Idira is built to deliver.

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, integrations 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 Idira — Our Journey to Democratize Privilege Controls appeared first on Palo Alto Networks Blog.

For the last several months, we have had early, unbounded access to the latest frontier AI models. What we’ve seen from that vantage point has made it clear that the window for organizations to get ahead of what’s coming is shorter than most leaders realize.

We have moved past the era of incremental AI improvements into a threat landscape shift. Our testing has revealed a step-change in capability that demonstrates an intuitive understanding of software vulnerabilities. This is more than faster code generation, it is a shift from AI as an assistant to AI as an autonomous agent capable of discovering and chaining flaws at a scale that most defenders aren’t prepared for.

These capabilities will not stay confined to controlled environments for long. When Mythos first launched, we predicted a six-month window before attackers gained access. We now believe that timeline has accelerated significantly.

To meet this inflection point, defense must operate at the speed of the adversary. That is why Palo Alto Networks has introduced Frontier AI Defense. This initiative unites our AI-native security platforms with Unit 42^® consulting and threat expertise with strategic partners to deliver continuous protection, prioritized risk mitigation and autonomous remediation.

What the Threat Looks Like Now

The latest frontier models, including OpenAI’s GPT-5.5-Cyber, Anthropic’s Mythos and Claude Opus 4.7, and the specialized variants emerging across major labs, represent roughly a 50% improvement in coding efficiency over their predecessors. That number sounds incremental, but in practice, it’s the threshold at which AI crosses from a helpful assistant into an autonomous operator.

Based on our testing and review, we found four key developments that, taken together, redefine the modern threat landscape:

• Vulnerability Discovery at Scale: Frontier AI is exceptionally effective at identifying vulnerabilities across massive, complex codebases. In our testing, three weeks of model-assisted analysis matched a full year of manual penetration testing, with broader coverage.
• Exploit Chaining & Synthesis: What is more consequential than individual discovery is the models’ ability to think like an attacker. They link multiple lower-severity issues into single, critical exploit paths, seeing full-stack logic, including SaaS and public-facing surfaces, in ways traditional scanners cannot.
• Attack Cycle Compression: In AI-assisted scenarios, the time from initial access to exfiltration has collapsed to as little as 25 minutes. Detection and response measured in hours is no longer a viable standard; single-digit MTTR (Mean Time to Respond) is the new floor.
• The Unsupervised Attack Surface: Rapid AI development and decentralized innovation are creating a massive, unsupervised attack surface in real-time. As local AI agents become commonplace, every desktop is now effectively a server, yet most organizations lack visibility into the code their own employees are generating and deploying.

Our Approach

These emerging threats form the foundation of how we have architected our platform response for the agentic era – Frontier AI Defense. Our approach moves beyond traditional, reactive defense to provide a comprehensive framework built to outpace frontier-AI-enabled attackers. This initiative is defined by:

• Advanced Access: We leverage early access to frontier AI models to harden defenses and simulate attacks before they reach the mainstream.
• Intelligence-Led Resilience: Unit 42 experts leverage frontier AI to fast-track discovery and remediation of exposures at machine speed through our Unit 42 Frontier AI Defense service.
• Unified Global Ecosystem: We provide the scale required for global protection through our Frontier AI Alliance of elite partners, including Accenture, Armadin, Deloitte, IBM, NTT DATA, and PwC.
• Machine Speed Security: By natively integrating Frontier AI across our platforms, we deliver the automated, real-time defense necessary to counter autonomous threats.

The Window Is Open. It Won’t Be for Long.

The capabilities we tested under early-access conditions are expected to become widely available over the next several months. Success in this new environment requires adapting your cybersecurity stack before these tools are in the hands of every adversary.

The threat has never been more sophisticated. The window to prepare for this shift is closing. And we're here to help secure your future at the edge of the frontier.

Visit Palo Alto Networks Frontier AI Defense to learn more.

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Elevating AI Security

Every AI system you deploy is a potential attack surface. Models and agents can carry embedded backdoors, malicious operators or compromised dependencies. Once running, these artifacts can exfiltrate sensitive data or execute unauthorized code, creating persistent vulnerabilities within the enterprise perimeter. Organizations running AI workloads on Nutanix need security that catches these threats before they reach production.

Nutanix and Palo Alto Networks are excited to announce a purpose-built integration between the Nutanix Enterprise AI and Palo Alto Networks Prisma AIRS^® advanced security capabilities, specifically focusing on AI Model Security and AI Red Teaming. This partnership directly addresses the critical need for a secure-by-design approach to AI development, giving customers the confidence to accelerate their AI journey.

Seamless Security Integration on the Nutanix Enterprise AI Platform

The Nutanix Enterprise AI platform provides a unified, scalable and secure foundation for the entire AI lifecycle: from data preparation and model fine-tuning to deployment and management. By integrating cutting-edge AI security tools by Palo Alto Networks directly into this workflow, we enable security checks to become an intrinsic part of the AIOps pipeline.

Scanning AI Models for Comprehensive Vulnerability Detection

The Prisma AIRS AI Model Security solution introduces sophisticated model scanning capabilities that are essential for preemptively identifying and mitigating risks.

• Prisma AIRS Model Security Integration: Automatically scans AI models (e.g., during check-in to a model registry on the Nutanix Enterprise AI platform) for inherent vulnerabilities, policy violations and malicious code. This provides Proactive Risk Mitigation by detecting malicious or vulnerable model artifacts before deployment, helping prevent zero-day exploits and potential data leakage caused by compromised models.
• Dependency Analysis: Examines all open-source libraries and dependencies used in the model environment for known vulnerabilities and license compliance issues. This enables Supply Chain Security, eliminating risks introduced by third-party components throughout the entire AI deployment lifecycle.
• Model Supply Chain Threats: The system addresses malicious model artifacts, including deserialization exploits, embedded backdoors, unsafe file formats, unauthorized code execution, untrusted sources and noncompliant licenses. This enables Model Integrity and Governance by validating model safety, provenance, approved formats, license compliance and detecting hidden execution paths before deployment.

AI Red Teaming Your AI Systems for Adversarial Resilience

AI Model Security addresses known issues, but the malicious actors of tomorrow are developing new ways to exploit AI systems. This is where the power of Prisma AIRS AI Red Teaming by Palo Alto Networks comes into play, creating a crucial layer of proactive testing against adversarial attempts. AI Red Teaming involves simulating sophisticated attacks against the AI application’s behavior to test its resilience under attack.

• Continuous AI assessment: Onboard an LLM model, application and agent, then start scanning in less than 10 minutes. Use documented APIs to integrate into CI/CD pipelines to trigger automated red teaming whenever versions are updated. Connect AI endpoints securely via an outbound web socket channel to eliminate the need for routing changes, while maintaining the option for IP allowlisting, if preferred. Your team controls access. This reduces technical setup overheads and empowers you to keep your assessment current.
• Contextual Vulnerability Insights: Prisma AIRS profiles your LLM model, application or agent and informs the Red Teaming Agent to design relevant attack objectives. The Red Teaming Agent is trained on over 50 techniques and simulates attack prompts to achieve those objectives. This reduces noise and lets you focus on actual business relevant risk.
• Comprehensive Threat Coverage: Prisma AIRS uses a library of over 750 attacks to evaluate your defensibility. Both the library and the red teaming agent are updated and trained on a constant basis to keep up with the AI threat landscape. This stress tests your AI system thoroughly, so your system is defensible to known and unknown threats.

Securing the Future of Enterprise AI — The Nutanix and Palo Alto Networks Integration

This integration between the scalable, high-performing Nutanix Enterprise AI platform and the advanced security intelligence of Palo Alto Networks offers measurable value to AI-driven organizations:

1. Accelerated Time-to-Trust – By automating critical security checks as part of the MLOps process on the Nutanix Enterprise AI platform, teams can deploy models faster, knowing they have been rigorously vetted by a leading security partner.
2. Simplified Compliance and Governance – The joint solution provides a verifiable record of security testing (scanning and red teaming), making it simpler to demonstrate adherence to internal governance standards and external regulatory mandates.
3. End-to-End AI Security Posture – Customers gain a holistic view of security, from the unified AI infrastructure layer managed by Nutanix, to the network security enforced by Palo Alto Networks. This visibility now extends critically into the AI models themselves, completing the security posture by unlocking controlled access to vendor models, so protection is enforced seamlessly.
4. Cost and Resource Efficiency – Integrating security tools within the existing AI platform streamlines workflows. Data Scientists and ML Engineers can trigger red teaming simulations and scanning directly within their familiar Nutanix environments, reducing the need for dedicated, siloed security teams to manually test every model.

The partnership between Nutanix and Palo Alto Networks is a commitment to building a more secure future for enterprise AI. With this integration, you can bring LLM models into your environment without fear. Malicious code and hidden backdoors are blocked before they ever reach you. Your endpoints stay continuously protected, with coverage across over 50 attack techniques and the contextual risks that come with agentic AI. When you're evaluating a model or an endpoint, the risk picture is right there inside NAI – no context-switching, no guesswork. And a custom security dashboard gives you a single place to see where you stand. The result is AI you can actually trust at the core of your lifecycle, so your teams can build faster without trading off security for speed.

Key Takeaways

A "Secure-by-Design" AI Pipeline: The partnership between Nutanix and Palo Alto Networks is a commitment to building a more secure future for enterprise AI. The integration enables advanced level AI security in AIOps workflow. By embedding Prisma AIRS directly into the Nutanix Enterprise AI platform, organizations can automate model scanning and vulnerability detection during the initial check-in phase, authorizing only validated, secure models to reach production.

Proactive Defense via AI Model Security and AI Red Teaming: The solution provides a dual-layer defense: AI Model Security preemptively blocks hidden backdoors, malicious code and supply chain threats in third-party artifacts, while AI Red Teaming uses autonomous agents for contextual discovery to generate new attack scenarios and have over 750 sophisticated adversarial attack scenarios. This enables resilience against both known vulnerabilities and emerging zero-day AI exploits.

Unified Governance and Operational Efficiency: The partnership consolidates security and visibility into a single custom dashboard within the Nutanix environment. This unified view allows Security and AI teams to manage risk while having continuous assessments and compliance records significantly accelerating the time to trust.

Next Steps

For more information, visit the Palo Alto Networks partner directory or contact your local sales representatives to learn more about a trial run.

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