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.

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