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.

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.

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.

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.

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.

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.

The post A New Era of Security: Frontier AI Defense appeared first on Palo Alto Networks Blog.

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.

The post Nutanix and Palo Alto Networks Integrate for Robust Model Trust appeared first on Palo Alto Networks Blog.

As Frontier AI crosses new thresholds, the landscape for both attackers and defenders is shifting. At Palo Alto Networks, we are committed to ensuring defenders maintain the advantage.

To deliver this critical edge, our Unit 42 Frontier AI Defense will now leverage Anthropic’s Claude Security, powered by Opus 4.7. By integrating one of the world’s most advanced AI models, we are empowering our customers to outpace automated threats. Through Frontier AI Defense, organizations can rapidly assess their security posture, remediate vulnerabilities and harden their infrastructure against next-generation, AI-driven attacks.

We are utilizing Claude Security’s deep technical reasoning to enable our customers to find and fix vulnerabilities with unprecedented speed. This includes:

1. AI-Driven Exposure Analysis – Identifying complex exploit chains that turn minor findings into critical risks.
2. Scalable Application Analysis – Performing deep-stack code reviews at a scale and depth previously unavailable.
3. Agentic Defense – Powering autonomous workflows that detect and remediate threats at machine speed, backed by human oversight.

Palo Alto Networks is also participating in Anthropic's Cyber Verification Program, which credentials security teams for legitimate defensive use of frontier models.

The threat timeline is accelerating. Within months, AI-driven attack capabilities will become a standard fixture of the threat landscape. Palo Alto Networks is dedicated to ensuring our global customers are equipped with the modern frontier AI models necessary to stay secure both today and tomorrow.

The post Enhancing AI-Driven Defense with Anthropic’s Claude Opus 4.7 appeared first on Palo Alto Networks Blog.

Frontier AI is changing what is possible for attackers. To meet this escalating threat, Palo Alto Networks is teaming up with Armadin, the new offensive security company founded by Kevin Mandia. This partnership expands our newly introduced Unit 42 Frontier AI Defense service, scaling our ability to identify and remediate AI-driven exposures, and accelerating protection across the enterprise.

Over the past few weeks, we’ve spoken with hundreds of CISOs who universally feel the urgency on the frontlines. Security leaders need to know exactly where they stand against the AI-driven attacks happening right now, and the ones coming in the next six months.

Expanding Frontier AI Defense — The External AI Hyperattack Assessment

For organizations seeking to actively pressure-test their perimeter, this partnership introduces an autonomous, AI-driven offensive assessment of your external attack surface.

This added layer identifies real attack paths and proves exploitability across internet-facing assets. The platform begins with passive discovery, validating publicly exposed assets, cloud resources and secrets. Next, Armadin deploys a coordinated swarm of autonomous AI attack agents, operating at machine speed across your external footprint.

These agents execute active reconnaissance, launch attacks and exploit vulnerabilities in parallel, using over 50,000 templates. Upon initial access, the swarm simulates post-exploitation behavior to demonstrate impact, logging every attack chain as decision-grade evidence of exploitable risk.

Decision-Grade Proof of Exploitable Risk

With this added layer of autonomous simulation, Unit 42 Frontier AI Defense provides an even more rigorous, pressure-tested view of an organization's external attack surface. This allows our experts to accurately simulate the tradecraft of the most capable, AI-equipped threat actors, compressing complex attack lifecycles from days into minutes.

AI may change what is possible for attackers, but in the hands of defenders, it becomes a decisive advantage. This partnership is another important step in making sure that advantage stays with the defenders.

A member of Project Glasswing and OpenAI’s Trusted Access for Cyber (TAC) program, Palo Alto Networks remains the only company equipped to deliver this strategic level of partnership through Unit 42 Frontier AI Defense and the Frontier AI Alliance, driven to integrate cutting-edge technologies into our products and services.

Get started with Unit 42 Frontier AI Defense today.

The post Unit 42 Expands Frontier AI Defense with Armadin Partnership appeared first on Palo Alto Networks Blog.

Palo Alto Networks Completes Acquisition of Portkey

We are pleased to announce that Palo Alto Networks has officially completed the acquisition of Portkey. 

We are moving from vision to reality by integrating Portkey’s pioneering AI Gateway directly into the fabric of the Palo Alto Networks product portfolio. Prisma AIRS AI Gateway will provide a unified vantage point to secure and govern AI agents at scale, offering a mission-critical control plane to identify, authenticate and authorize every agentic interaction in real time.  

We are delivering the industry’s most comprehensive security and unified control framework for the agentic enterprise, enabling our customers to scale autonomous AI workloads with complete confidence.

The era of the AI Enterprise has arrived. Today, 81% of enterprises are piloting the use of AI agents or have fully implemented AI agent solutions. We aren't just talking about smart chatbots. We are talking about autonomous agents that execute.

By leveraging APIs and MCP servers, these agents navigate complex workflows, access sensitive data and make real-time, business-critical decisions. The question is no longer if companies will adopt AI agents, but how to securely operationalize them without putting the brakes on innovation. 

The Challenge: Expanding Attack Surfaces

AI agents are creating a new and largely invisible attack surface. The risk is not just their independence, but the lack of visibility and accountability. Without a centralized enforcement layer for operational and security controls, every team that deploys an agent may unintentionally expose the enterprise to unauthorized data access and heightened security risks.

To solve this, Palo Alto Networks is redefining security for the agentic era. We recently introduced Prisma AIRS 3.0, the industry’s first platform to secure the entire agentic AI lifecycle. Portkey's acquisition accelerates that momentum.

The Prisma AIRS AI Gateway: From Chaos to Control 

Portkey's AI Gateway will be integrated into Prisma AIRS to deliver the unified control plane that enterprises need to operationalise and secure AI apps and agents at scale.

Moving from “chaos to control” requires a centralized approach to governance. Currently, many AI initiatives are hindered by fragmented security and a lack of oversight. The AI Gateway solves this by providing a unified vantage point where organizations can enforce consistent policies across all models and agents, ensuring every interaction is identified, authenticated and authorized in real time within a single governing framework.

The Prisma AIRS AI Gateway will establish a mission-critical control plane for the agentic enterprise, enabling teams to move autonomous workloads from development into at-scale production with confidence. With operational features like a unified API to LLMs, an agent registry, semantic routing and caching, the AI Gateway equips enterprises with complete control in one platform. By serving as a centralized enforcement point at the center of Prisma AIRS for all agent traffic, the AI Gateway will provide critical security functions, including Agent Artifact scanning, automated Red Teaming and Runtime Security needed to monitor behavior, route requests and mitigate risks in real time. Crucially, the AI Gateway will reinforce Agent Identity Security via Idira (formerly CyberArk), applying strict protocols to ensure every autonomous action is authenticated and governed by least-privilege controls.

Our vision is for the Prisma AIRS AI Gateway to serve as the industry blueprint for enterprises in the agentic era. By making security a foundational component of the operational lifecycle, we are empowering enterprises to build and govern an AI ecosystem that is secure by design.

Why Portkey? The Pioneer in AI Gateways

• Battle-Tested: Portkey’s AI Gateway is already supporting the demands of the modern enterprise, at scale, with several Fortune 500 customers, processing trillions of tokens per month with the low latency that is required for agent-to-agent communication. This ensures that agentic security does not come at the cost of developer speed or application performance. 
• Architectural Simplicity: Portkey offers plug-and-play capabilities with just three lines of code required to implement the AI Gateway. The AI Gateway, powered by unified APIs, also provides secure access to over 3,000 LLMs, MCP servers and agents, giving enterprises a flying start to building and executing with AI agents.
• Better Together: Palo Alto Networks and Portkey’s joint vision is to make Prisma AIRS the most ubiquitous platform for AI security. With exceptional AI security by Palo Alto Networks combined with Portkey’s AI Gateway, we will offer a comprehensive AI Security platform.

What’s Next?

The era of AI Enterprises is here. We’re making sure it is secure by design. The complexity of managing agents and securing them has long created friction in enterprises. With the integration of Portkey into Prisma AIRS, we will remove the trade-off between agent autonomy and authority. We are ensuring that as businesses accelerate into the era of autonomous agents, the security architecture isn’t just keeping up, it is setting the pace. 

Learn more about Prisma AIRS - the world’s most comprehensive AI security platform.

Forward-Looking Statements

This blog contains forward-looking statements that involve risks, uncertainties, and assumptions, including, but not limited to, statements regarding the anticipated benefits and impact of the acquisition of Portkey on Palo Alto Networks, Portkey and their customers. There are a significant number of factors that could cause actual results to differ materially from statements made in this blog, including, but not limited to: risks related to disruption of management time from ongoing business operations due to the acquisition and the integration of Portkey and other recent acquisitions; our ability to effectively operate Portkey's operations and business, integrate Portkey’s business and products into our products, and realize the anticipated synergies in the transaction in a timely manner or at all; changes in the fair value of our contingent consideration liability associated with acquisitions; developments and changes in general market, political, economic and business conditions; failure of our platformization product offerings; risks associated with managing our growth; risks associated with new product, subscription and support offerings; shifts in priorities or delays in the development or release of new product or subscription or other offerings or the failure to timely develop and achieve market acceptance of new products and subscriptions, as well as existing products, subscriptions and support offerings; failure of our product offerings or business strategies in general; defects, errors, or vulnerabilities in our products, subscriptions or support offerings; our customers’ purchasing decisions and the length of sales cycles; our ability to attract and retain new customers; developments and changes in general market, political, economic, and business conditions; our competition; our ability to acquire and integrate other companies, products, or technologies in a successful manner; our debt repayment obligations; and our share repurchase program, which may not be fully consummated or enhance shareholder value, and any share repurchases which could affect the price of our common stock.

Additional risks and uncertainties that could affect our financial results are included under the captions "Risk Factors" and "Management's Discussion and Analysis of Financial Condition and Results of Operations" in our Quarterly Report on Form 10-Q filed with the SEC on February 18, 2026, which is available on our website at investors.paloaltonetworks.com and on the SEC's website at www.sec.gov. Additional information will also be set forth in other filings that we make with the SEC from time to time. 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 and Governing AI Agents At Scale Through A Unified AI Gateway appeared first on Palo Alto Networks Blog.

Expand Strategic Collaboration to Secure the AI Enterprise

The transition from generative AI to agentic AI represents one of the most significant shifts in the history of enterprise technology. As organizations move from simple chatbots to autonomous agents that can execute business processes, the attack surface isn't just changing, it's exploding.

At Google Cloud Next 2026 in Las Vegas, Palo Alto Networks is proud to announce a series of groundbreaking integrations with Google Cloud. These innovations are designed to do more than just monitor the new AI-driven landscape; they are built to secure it by design. AI deployment is currently outpacing AI governance. By embedding our security platform into Google Cloud’s infrastructure, we are giving today’s enterprises the foundation to become the autonomous organizations of tomorrow.

Here is a look at the four major milestones of our partnership being unveiled this week.

Secure AI Agents with Google Cloud + Prisma AIRS

As autonomous AI agents become the new enterprise standard, security can no longer be an afterthought; it must be architectural. By integrating Prisma AIRS™ natively with Google Cloud Gemini Enterprise Agent Platform, we provide the proactive defenses required to govern complex agentic workflows. This integration ensures that as you scale your autonomous workforce, your security scales with it, providing comprehensive operational integrity without hindering the speed of innovation.

We are delivering capabilities across three critical pillars:

• Protecting Agent-Specific Runtime Risks: In an agentic ecosystem, the primary risk is unauthorized or a destructive action taken by the AI agents themselves. Prisma AIRS secures the "agent-to-tool" interface, preventing poisoned context from triggering malicious scripts or destructive actions. The solution monitors agent execution in real-time, so agents cannot leak sensitive credentials or tool schemas, maintaining the boundary between agents and their access to enterprise data.
• Securing the GenAI Application Surface: Modern AI applications and agents require a secure-by-design approach. Prisma AIRS AI Runtime Security™ provides prevention of more than 30 adversarial prompt injection and jailbreak techniques, as well as malicious code and URLs within LLM outputs. Prisma AIRS utilizes over 1,000 predefined patterns out of the box and ML-powered Enterprise DLP to stop sensitive data leakage.
• Enforcing Enterprise AI Safety and Grounding: Trust in AI is built on the consistency and safety of its output. Prisma AIRS allows organizations to define safety policies in natural language and filter toxic content across eight distinct categories to protect brand reputation. Using contextual grounding, Prisma AIRS can prevent misleading outputs that contradict internal RAG data, keeping agents tied to real facts.

This integration ensures that as you scale your autonomous workforce, your security posture scales with it, providing operational integrity without hindering the speed of innovation.

Security-as-Code for Prisma AIRS Integration with Application Design Center (ADC)

The traditional bolt-on approach to security is no longer viable in a cloud-first world. Google Cloud’s Application Design Center (ADC) is revolutionizing how applications are built, using an intuitive canvas and natural language via Gemini Code Assist.

Palo Alto Networks is announcing that it will be published as a template within the Application Design Center, providing more capabilities to engineering teams:

• Drag-and-Drop Security – Visually "snap" VM-Series firewalls and Prisma AIRS AI protections directly into network flows.
• AI-Driven Architecture – Use natural language prompts to generate secure-by-default, multiregion architectures.
• Simultaneous Deployment – Deploy entire application stacks and security services in a single, unified workflow, ensuring protection is present from the very first minute of deployment.

Zero-Day Protection at Scale with Advanced Malware Sandboxing for Google Cloud NGFW Enterprise

The battle against malware has shifted to the cloud. Modern attacks are faster, more evasive and capable of bypassing traditional defenses.

That is why we are excited to announce Advanced WildFire^®, powered by Palo Alto Networks, natively integrated into Google Cloud NGFW Enterprise, delivering AI-driven malware prevention directly within Google Cloud environments.

This integration embeds inline sandboxing and real-time threat intelligence directly into Google Cloud’s distributed firewall to stop advanced and unknown threats before they impact workloads, enabling:

• Secure Detonation – Suspicious files are safely executed in a controlled sandbox environment to uncover hidden and unknown threats.
• Inline Traffic Inspection – Inbound and outbound traffic is analyzed in real time to prevent lateral movement of malicious payloads across cloud environments.
• AI-Driven Threat Prevention – Leverages global threat intelligence by Palo Alto Networks to block zero-day threats before they compromise workloads.

With Advanced WildFire embedded directly into Google Cloud NGFW Enterprise, organizations can extend consistent protection across their cloud infrastructure while maintaining operational simplicity.

Cloud NGFW Enterprise Advanced Malware Sandboxing will be available in Public Preview soon.

Defining the Future with the Google Cloud Marketplace

Palo Alto Networks has joined the Google Cloud Marketplace Agent-as-a-Service as a launch partner to introduce the Prisma AIRS Model Security agent. Operating as an Agent-as-a-Service, this solution scans AI models for vulnerabilities and policy noncompliance before they reach production.

Available in the Agent Gallery inside Gemini Enterprise, this marketplace offering runs entirely within the customer’s own Google Cloud environment, providing both new and existing Prisma AIRS users a seamless and simple deployment experience inside Gemini Enterprise.

Securing AI Innovation at Scale

The collaboration between Palo Alto Networks and Google Cloud is built on a shared vision: Security should be an accelerator for innovation, not a bottleneck. As we look toward the future of the AI-powered enterprise, our commitment remains to provide the most robust, platform-driven security for every workload, every agent and every interaction.

Want to see these integrations in action? Contact your Palo Alto Networks representative to learn more about how we are securing the future of the cloud together. If you’re attending Google Cloud Next 2026, join us at these sponsored sessions:

• Wednesday, April 22 @ 12:30 PM - 1:15 PM
  The AI-Ready Enterprise: Securing Access and Data in an AI-first World
• Thursday, April 23 @ 9:15 AM - 10:00 AM
  Securing and managing Agentic AI at scale: What Google and Palo Alto Networks are building together
  Munish Khetrapal, vice president of Cloud Engagement, GCP - Palo Alto Networks
  Mike Buratowski, deputy chief information security officer - The Home Depot
  
• Thursday, April 23 @ 9:15 AM - 10:00 AM
  The agentic era: Governing an invisible AI workforce
• Thursday, April 23 @ 12:00 PM - 12:45 PM
  The AI Attack Surface: Strategizing Defense for the modern AI-Powered Enterprise

The post Palo Alto Networks and Google Cloud appeared first on Palo Alto Networks Blog.

Acting swiftly with intent, together with Accenture, Deloitte, IBM, NTT DATA and PwC

With the imminent release of unbounded frontier models, the barrier to entry for sophisticated cyberattacks has vanished. Anthropic’s Mythos represents a 50% leap in coding capability over previous models. It’s a leap that, as Lee Klarich stated, translates into autonomous agents capable of both surfacing a massive surge of vulnerabilities and exploiting them faster than we’ve ever seen or imagined.

In this new era, business continuity requires more than just better tools; it requires a unified ecosystem of experts capable of orchestrating a defense that matches this new pace of attack.

As we drive the industry standard for addressing these emerging risks with our Unit 42^® Frontier AI Defense, we’ve united an alliance of global transformation leaders, starting with Accenture, Deloitte, IBM, NTT DATA and PwC, and will continue to scale these alliances to ensure every enterprise has a rapid path to AI resilience.

By combining the world’s most advanced AI security platform with deep industry expertise, we are delivering the security assessment and rapid protection needed to help customers stop emerging threats and keep their business resilient.

By engaging directly with Palo Alto Networks, or working with our partners through the Frontier AI Alliance, our customers can move past the complexity of building an AI-ready defense from scratch and gain:

• Accelerated Immunity: Go from a high-exposure state to a hardened posture using a prevalidated AI Defense Blueprint, delivering coverage in weeks, not years.
• On-Demand Expertise: Our partners provide the specialized prompting and verification required to make the latest AI Frontier models work for the defender.
• Operational Resilience: While Unit 42 provides the Frontier AI Exposure Analysis, our ecosystem partners provide the boots on the ground to remediate those findings and leverage our product portfolio to deliver AI-readiness to your enterprise.

The threat of Mythos-class models is imminent, but the path to resilience is clear. Whether you are looking for an immediate strategic assessment or a deep operational overhaul, the Frontier AI Alliance is ready to move at the speed of your business.

The post The AI Ecosystem Edge — Introducing Our Frontier AI Alliance appeared first on Palo Alto Networks Blog.

The release of the newest frontier AI models marks a turning point for cybersecurity. Palo Alto Networks has conducted early testing of the latest frontier AI models, including Anthropic’s Mythos model as part of Project Glasswing and OpenAI’s latest models as part of Trusted Access for Cyber program. The conclusion is clear: They are extraordinarily capable at finding vulnerabilities and generating corresponding exploits.

This generational improvement in coding ability directly translates to a significant advance in vulnerability discovery and exploit generation. These capabilities, however guardrailed, will not stay contained. Similar advances will appear across other major AI labs, Chinese models, and open source models. Attackers will find the seams in those guardrails. They will use advanced AI to discover zero-day vulnerabilities at scale, generate exploits in near real time, and develop autonomous attack agents unlike anything the industry has faced.

Within six months, advanced AI models with deep cybersecurity capabilities will become commonplace. Organizations that have not put appropriate safeguards in place will face an entirely new class of risk across their enterprise and critical infrastructure.

Frontier AI: A Quantum Leap in Code Fluency

As you have probably already seen, the latest unbounded models like Mythos represent roughly a 50% improvement in coding efficiency over Anthropic’s previous leading model. Palo Alto Networks has had early access to unbounded models and we’ve been able to leverage this vast improvement in coding to a quantum leap in scanning and offensive capability.

Hundreds of our best security engineers have been assessing these capabilities and developing best practices for using it effectively. The results revealed several core truths:

• Vulnerability discovery at scale: Frontier AI is exceptionally effective at identifying vulnerabilities in code. In less than three weeks, it accomplished the equivalent of a full year’s worth of penetration testing effort.
• Attack path determination: Perhaps more impressive than finding individual vulnerabilities, Frontier AI excels at vulnerability chaining, combining multiple lower-severity issues into critical-level exploit paths. For example, linking two medium-severity and one low-severity vulnerability into a single critical exploit.
• Full-stack logic analysis: Frontier AI can analyze the full exposure surface of applications, including SaaS and public-facing platforms, identifying logic-based vulnerabilities that traditional tools miss.

Impacts on the Cyber Landscape

Attackers have been using LLMs for years, but based on our testing of frontier AI models, there are three key areas where they will have a significant impact on the cybersecurity landscape:

1. The Vulnerability Deluge: Frontier AI models will dramatically accelerate the rate at which vulnerabilities are discovered, by defenders and attackers alike. This will be particularly acute in open source and critically, the flood of patches that follows will itself create risk. Every patch that is not applied immediately becomes a known, targetable vulnerability. Organizations will need to accelerate and automate their patching programs, rethink how they prioritize and apply patches, and ensure best-in-class protections are in place to mitigate vulnerability until they can be remediated.
2. Rise of Inside-Out Attacks: Recent supply chain attacks on tools like LiteLLM and Trivy demonstrate a growing pattern where attacks land adversaries inside an organization’s infrastructure, bypassing multiple conventional attack steps and reducing the number of prevention opportunities available to defenders. The rapid deployment of AI infrastructure has made this problem more acute as the AI supply chain, including runtime environments, communication infrastructure, and model dependencies, is often insufficiently protected. While open source usage and patching practices must become significantly more robust, organizations will need structural containment of potential attacks through zero trust, identity modernization, outbound connection restrictions and lateral movement protections.
3. Faster AI-Assisted Attack Cycles: I expect the most consequential shift with frontier AI models is the move from AI-assisted to AI-driven attacks. Attackers will build autonomous attack agents that dramatically compress attack cycle times. What once took days or weeks of skilled manual effort will soon be executed in minutes. This democratization of advanced attack capabilities means that defenders must match that speed with near-real-time detection and response, which is only possible with extensive AI and automation throughout security operations. Organizations whose Mean Time to Detection and Mean Time to Response are not measured in low single-digit minutes will be outpaced.

The Defenders Guide: Assessment, Protection, Platformization

The framework for defending against AI-driven threats is not completely new, but the standard for execution must be absolute. Organizations that are “mostly protected” are effectively unprotected. What follows is a phased approach – assessment, protection and platformization – that organizations should pursue in parallel to close gaps before attackers exploit them.

Assessment: Every organization should use the latest AI models to assess its entire code and application landscape and build a comprehensive asset and exposure inventory.

Key priorities:

• Leverage AI models to identify vulnerabilities across your codebase, applications and infrastructure before attackers do.
• Evaluate exposure with full context, including how vulnerabilities chain together to form critical exploit paths.
• Audit your open source supply chain, including AI infrastructure, runtime environments and model dependencies.
• Map your current sensor coverage. Detection, prevention and telemetry gaps represent critical blind spots.

Protect & Remediation: Remediating and reducing exposure is table-stakes. What in the past may have been difficult due to cross-organizational friction of finding and fixing at pace should now be accelerated with the c-suite attention of these new AI models. But this must go further and extend to comprehensive deployment of best-in-class attack prevention capabilities where the new standard is 100% coverage and optimization.

• XDR everywhere, with emphasis on real-time ML-based detection and prevention of attacks; all hosts on prem and cloud included.
• 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 is now a necessity for securing the agentic endpoint).
• With an average of 85% of work now happening in the browser, secure enterprise browsers with real-time security become a must-have for attack prevention.
• Zero trust and identity security are foundational to securing every user and every connection.

Real-Time Security Operations: With attack cycle times shrinking rapidly, the legacy approach to security operations simply doesn’t work. Disparate tools analyzing data in silos overlaid with manual processes must be replaced with AI and automation throughout. Cortex XSIAM, our AI-driven SOC platform, is what I consider to be the gold standard for how to take a next-generation approach to deliver MTTD and MTTR in single digit minutes.

• 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 1st party and 3rd 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 the seams and gaps between point solutions.

We’re Here to Help

Achieving this level of resilience requires the right platforms and the right expertise.

To help you navigate this shift, we are introducing Unit 42 Frontier AI Defense. This new offering is designed to discover and remediate your current exposure before attackers do, strengthen controls that reduce exposure and contain impact and modernize operations so teams can detect and respond at machine speed.

This is the moment we’ve been preparing for. The threat has never been more sophisticated, but the path forward has never been clearer, and we’re here to partner with you on what comes next.

The post Defender's Guide to the Frontier AI Impact on Cybersecurity appeared first on Palo Alto Networks Blog.

Frontier AI models have given the security industry a preview of what comes next. As they become weaponized, attackers will automate the discovery and chaining of vulnerabilities in near real-time – compressing timelines, increasing scale and outpacing human-led defense.

Until now, defenders have had time to detect activity, investigate signals and contain threats before exposures were chained into full attacks. AI is quickly closing this window.

Defending against AI-driven threats means engineering a resilient architecture that limits how easily attackers can exploit discovered weaknesses, that contains the blast radius when they do, and enables faster response at scale. It also means using AI to accelerate the security program itself, from vulnerability discovery and code review to triage, remediation and incident response.

The transition should cover three areas. First, discover and remediate your current exposure before attackers do. Second, strengthen controls that reduce exposure and contain impact. Third, modernize operations so teams can detect and respond in real-time.

To help organizations make this shift, Palo Alto Networks is launching Unit 42^® Frontier AI Defense.

Powered by the latest AI models, Unit 42 Frontier AI Defense helps organizations answer a critical question: Are your defenses ready for AI-powered attacks?

Unit 42 Frontier AI Defense combines three core components delivered by expert consultants, coupled with 6 months of complimentary access to Cortex^® XDR, Cortex Xpanse^® and Koi Agentic Security.

Frontier AI Exposure Analysis: Identify and validate the exposures most likely to be chained into real attacks before attackers weaponize them.

Actions

• • Use the latest frontier models, Unit 42 offensive security expertise, threat telemetry and Unit 42 Threat Intelligence to assess your environment.
  • Identify the vulnerabilities, misconfigurations and posture gaps most likely to be exploited across infrastructure, applications, code, identity and cloud.
  • Validate the attack paths most likely to matter in real-world attacks.

Outputs

• • A prioritized view of vulnerabilities and attack paths that matter most
  • Clear actions to fix the exposures that matter first

Autonomous Security Blueprint: Benchmark current capabilities and define the changes required for machine-speed defense.

Actions

• • Assess current-state capabilities across attack surface, identity, software supply chain, zero trust containment, as well as real-time detection and response.
  • Identify where AI-powered threats create the greatest exposure and where current controls are most likely to fail.
  • Define the technical and operational changes required to close those gaps.

Outputs

• • A clear blueprint for immediate action
  • A prioritized roadmap to reduce exposure, strengthen containment and modernize security for the AI era

Agentic Defense Transformation: Implement the prioritized architecture, control and operating changes needed to modernize defenses for AI-driven threats.

Actions

• • Implement the architectural, operational and control changes required to defend against AI-driven threats.
  • Modernize exposure management, harden the software supply chain, and advance zero trust architecture.
  • Build response capabilities that can keep pace with autonomous attacks.

Outputs

• • Accelerated implementation of the changes that matter most
  • A more modern security architecture, built to reduce exposure and improve containment

The Window Is Still Open, for Now

AI is the biggest security inflection point since enterprises moved to the cloud. Organizations that act now will be the ones that are ready. Those that wait will be forced to respond under maximum pressure on the worst possible day.

Frontier AI is changing what is possible for attackers. In the hands of defenders, it can become a decisive advantage.

Human-speed security is no longer enough. A modern security approach is required. Get started with Unit 42 Frontier AI Defense today.

*The complimentary offer is not available to public sector customers or current Cortex XDR, Cortex Xpanse or Koi customers.

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Securing the AI-Powered Hybrid Multicloud

At the core of every modern enterprise is a fundamental need: The ability to innovate across hybrid environments without sacrificing security. For over five years, Palo Alto Networks and Nutanix have partnered to meet this need, building a collaborative ecosystem where industry-leading infrastructure meets the world’s most comprehensive AI-powered security.

As we look toward the future of the enterprise at Nutanix .NEXT 2026, our focus remains on a shared vision for the "Secure Nutanix Cloud."

2026 Global Security Partner of the Year

We are deeply honored to be named the Nutanix 2026 Global Security Partner of the Year. This recognition reflects our commitment to delivering integrated, automated security that feels like a native part of the Nutanix experience. Together, we have helped thousands of joint customers move from reactive security to a proactive, zero trust posture that spans the data center, the edge and the public cloud.

The Existing Partnership Is the Foundation of Trust

Our partnership is built on the belief that security should be invisible, automated and inseparable from the infrastructure it protects. We’ve worked alongside Nutanix to enable enterprises to scale their hybrid multicloud, and their security posture scales with it. Current integrations provide zero trust protection across the Nutanix environment:

• VM-Series Virtual Firewalls – Seamlessly integrated with Nutanix AHV and Flow Network Security, our virtual firewalls provide Layer 7 visibility and advanced threat prevention for east-west traffic. This integration leverages Nutanix Flow service chaining to automatically steer traffic through VM-Series firewalls for deep packet inspection without manual reconfiguration. It delivers full functional parity and operational continuity for Nutanix AHV environments, allowing security teams to maintain high-performance standards using familiar Panorama® management and persistent, tag-based policies that migrate with workloads across clusters.
• Hybrid Cloud Security – We provide consistent security for Nutanix Cloud Clusters (NC2) on both AWS and Azure, enabling your policies to follow your workloads wherever they reside.
• Automation & Orchestration – Leveraging the Panorama® plugin for Nutanix, teams can automate security provisioning and use Dynamic Address Groups to sync application attributes instantly.

New Integration Secures Nutanix Enterprise AI (NAI)

Building on this foundation, the highlight of this year’s show is our groundbreaking integration designed to accelerate Enterprise AI adoption. NAI provides a simplified, cloud-native stack that allows organizations to deploy and scale large language models (LLMs) across their choice of infrastructure with push-button simplicity. We are collaborating on a first-of-its-kind, end-to-end security solution for NAI.

This integration, launching soon, will bring AI Model Security and AI Red Teaming directly into the NAI, creating a seamless experience where security is built in, not bolted on. By allowing only Prisma AIRS™ validated models to reach production, we eliminate security friction at the start of the AI lifecycle. Every model will undergo rigorous scans for known vulnerabilities before deployment, providing a "clean room" environment for AI development. Providing a proactive test of AI defenses, Prisma AIRS AI Red Teaming will be available within NAI as an autonomous solution that integrates seamlessly into the development pipeline, utilizing a combination of a profiler and an attacker agent to perform contextual iterative simulations that mirror real-world attacker behavior. By providing detailed reports that map vulnerabilities directly to the OWASP Top 10 for LLMs and NIST AI RMF, it equips teams with the precise context needed to secure AI applications continuously and effectively.

By proactively identifying and neutralizing emerging threats, we will give organizations the confidence to deploy AI bravely, knowing their innovation is anchored in the world’s most robust security platform.

Powered by Prisma AIRS, this integration will bring a "security-first" approach to your AI deployments:

• AI Model Security – Scans AI models during the download phase to block malicious code and hidden backdoors before they reach your environment.
• AI Red Teaming – Provides continuous, autonomous vulnerability hunting on models, application and agent endpoints, testing your AI behavior against more than 750 real-world attack scenarios and contextual agentic risk assessment.
• Unified Visibility – Provides a complete overview of your AI risk posture and scan summaries directly within your NAI dashboards.

Benefits:

Seamless and Frictionless Deployment

We will prioritize a fast and frictionless deployment experience, ensuring that robust AI security does not come at the cost of development speed. By integrating these controls directly into the NAI workflow, organizations will be able to deploy and scale their AI initiatives with "push-button" simplicity, removing the traditional complexity and friction associated with securing large language models.

Proactive Protection Against Emerging Threats

Leveraging our deep expertise in threat prevention, this solution will proactively identify and block vulnerable or malicious models before they can impact the enterprise environment. By scanning models for hidden backdoors and malicious code during the initial download phase, we will stop threats at the perimeter, allowing only validated, secure models to ever reach your production environment.

A Comprehensive Enterprise Cloud AI Solution

This integration will deliver a comprehensive enterprise cloud AI solution, merging Nutanix’s industry-leading infrastructure with our next-generation security controls. The result will be a unified, cloud-native stack where security is built in rather than bolted on after the workload deployment, providing a secured deployment environment, which is consistent across the data center, the edge and public cloud.

Evolving Insights and Real-Time Remediation

The vulnerability insights from AI Red Teaming are coupled with remediation insights. The platform will provide a prioritized list of remediation steps that are tailor-made for the endpoint. This allows organizations to battle-test their inference endpoints before deploying them at scale.

Key Takeaways

• A Proven, Award-Winning Partnership: Celebrating five years of collaboration, Palo Alto Networks has been named the Nutanix 2026 Global Security Partner of the Year, highlighting a shared commitment to delivering native, automated zero trust security for hybrid multicloud environments.
• Seamless Zero Trust for Hybrid Workloads: Through deep integrations with VM-Series virtual firewalls and Nutanix Cloud Clusters (NC2), organizations can maintain consistent Layer 7 visibility and tag-based security policies that automatically follow workloads across on-premises data centers and public clouds.
• Securing the AI Lifecycle with Prisma AIRS: The new integration with NAI, launching soon, will bring a security-first approach to AI adoption, utilizing Prisma AIRS to scan LLMs for vulnerabilities during download, and perform autonomous Red Teaming to neutralize emerging threats before they reach production.

Don’t Miss Our Speaking Session

Want to see the integration in action? Join our experts, Shrikant Brahmbhatt (Palo Alto Networks) and Ashwini Vasanth (Nutanix), on Tuesday April 7 3:30-4pm for an exclusive look at how we are securing the "Challenge of Hybrid AI." We’ll dive into the architecture that allows you to discover, assess and protect your entire AI ecosystem (apps, agents and models alike).

Visit Us at Booth #G2

Stop by the Palo Alto Networks booth (#G2) to meet our team of over 19,000 active threat researchers and see live demos of our joint solutions. Whether you are building the next generation of agentic AI or securing your virtual desktop infrastructure (VDI), we have the tools to help you innovate at machine speed.

Ready to secure your journey? Visit the Palo Alto Networks partner directory or learn more about Prisma AIRS.

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, including, without limitation: developments and changes in general market, political, economic, and business conditions; risks associated with managing our growth; risks associated with new products and subscription and support offerings; shifts in priorities or delays in the development or release of new offerings, or the failure to timely develop, release and achieve market acceptance of new products and subscriptions as well as existing products and subscription and support offerings; failure of our business strategies; rapidly evolving technological developments in the market for security products and subscription and support offerings; our customers’ purchasing decisions and the length of sales cycles; our competition; our ability to attract and retain new customers; and our ability to acquire and integrate other companies, products, or technologies. 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.

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Forging Secure AI Threat Protection for Singapore

Singapore is currently undergoing a decisive transition toward an AI-enabled economy. National initiatives are focused on driving large-scale transformation through the National AI Missions and integrating advanced technologies, including generative AI and autonomous agents across key sectors. This rapid technological evolution, however, also introduces a sophisticated threat landscape characterized by AI-specific risks, like prompt injection, model manipulation and sensitive data leakage. As enterprises scale AI adoption, the need for robust, AI-native and locally hosted cybersecurity solutions becomes essential to ensure data residency, regulatory alignment and operational resilience.

Strategic Imperatives for an Emerging AI Security Landscape

Singapore’s highly integrated digital ecosystem presents both significant opportunities for leadership as well as distinct security challenges. As the nation executes its National AI Strategy 2.0, the focus has shifted from high-level experimentation to the pervasive deployment of AI across the economy. This evolution requires a security posture that is not only AI-native but locally grounded to satisfy the data residency expectations of a global financial and innovation hub.

Palo Alto Networks is pleased to announce a strategic investment designed to enhance Singapore’s cyber resilience – the establishment of our new cloud landing for Prisma^® AIRS. This launch demonstrates a commitment to providing organizations in the region with an AI-powered cybersecurity platform that aligns with the National AI Council’s whole-of-government mission. This initiative optimizes operational efficiency and facilitates the secure adoption of advanced digital transformation projects, allowing organizations to Deploy Bravely.

Comprehensive AI Security Platform

The new regional expansion in Singapore now hosts Prisma AIRS, our most comprehensive AI security platform, specifically engineered to deliver robust security across the entire AI lifecycle. This localized landing provides Singaporean organizations with domestic, high-performance access to critical AI security capabilities:

AI Model Security
Enable the safe adoption of third-party AI models by scanning them for vulnerabilities and secure your AI ecosystem against risks, such as model tampering, malicious scripts and deserialization attacks.

AI Red Teaming
Uncover potential exposure and lurking risks before bad actors do. Perform automated penetration tests on your AI apps and models using our Red Teaming agent that stress tests your AI deployments. Our agent learns and adapts like a real attacker.

AI Runtime Security
Protect your LLM-powered AI apps, models and data against runtime threats, such as prompt injection, malicious code, toxic content, sensitive data leaks, resource overload, hallucinations and more.

AI Agent SSPM (SaaS Security Posture Management)
Secure AI agents (including those built on no-code/low-code platforms) against new agentic threats, such as identity impersonation, memory manipulation and tool misuse.

Commitment to Singapore's AI Future

Our new region expansion into Singapore signifies the long-term commitment of Palo Alto Networks to the nation’s digital transformation journey and its cybersecurity resilience. By bringing advanced, AI-native platforms closer to regional organizations, Palo Alto Networks helps enterprises achieve data residency and national data sovereignty needs, enhance performance and strengthen security posture. This localized presence simplifies operations and accelerates the safe adoption of generative AI and agentic workflows.

As Singapore continues its trajectory toward an AI-driven and secure future, Palo Alto Networks stands as a trusted partner, empowering organizations to innovate and thrive securely within an evolving threat landscape. The establishment of this new cloud landing reinforces the ongoing promise to deliver the best-in-class cybersecurity platforms that the country requires to lead on the global stage.

Please visit the regional cloud locations of Palo Alto Networks for more information.

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A Pivotal Moment for National Security

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

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

Shape Adversary Behavior (Pillar 1)

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

Promote Common Sense Regulation (Pillar 2)

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

Modernize and Secure Federal Government Networks (Pillar 3)

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

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

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

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

Secure Critical Infrastructure (Pillar 4)

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

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

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

Sustain Superiority in Critical and Emerging Technologies (Pillar 5)

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

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

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

Build Talent and Capacity (Pillar 6)

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

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

Turning Strategic Vision Into Action

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

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

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