Palo Alto Networks is proud to enter a strategic partnership with the RAF Association.

For over 90 years, the Royal Air Forces Association (RAFA) has championed a simple yet profound belief: No member of the RAF community should ever be left without the help they need. Serving personnel, veterans and their families, the RAF Association provides crucial welfare support, responding to increasingly complex needs in an era of operational changes and challenges, including persistent global deployment.

Delivering on their mission today requires not only compassion and expertise but also resilient digital foundations. To strengthen and future-proof its operations, RAFA has entered into a strategic partnership with Palo Alto Networks. Together, we are modernising the Association's cyber security posture through a secure-by-design, zero trust architecture to enhance organisational resilience, secure sensitive beneficiary data, and improve operational agility. This helps ensure they can focus on their mission of support, not security management.

As Nick Bunting OBE, Secretary General at the RAF Association, puts it:

Cybersecurity is essential to safeguarding the trust people place in our organisation. This transformation will give us greater protection for our data and systems, ensuring that our services remain dependable and that our organisation is secure, resilient and ready for the future. Strong digital security is not just a technical requirement, it is a fundamental part of how we uphold our duty of care to every individual who relies on us.

Securing the Mission

The RAF Association operates in a distributed environment comprising headquarters’ functions, remote caseworkers, and more than 20 RAFAKidz nursery sites, supported by a growing portfolio of cloud-based services. In this context, cybersecurity is not simply an IT concern. It is a safeguarding imperative.

Disruption to systems or a compromise of sensitive beneficiary data could directly impact RAFA’s ability to deliver services and maintain the trust of the communities it supports. By consolidating fragmented legacy tools into a unified platform, this partnership ensures the Association’s digital evolution aligns security controls with GDPR obligations and safeguarding requirements.

Digital Resilience with a Unified Platform for Visibility and Control

To support RAFA's lean IT operational model, this transformation will move them away from fragmented legacy tools toward a unified platform approach. The deployment of Prisma^® SASE (secure access service edge) and Cortex XDR^® will provide RAFA with consistent visibility and control across users, devices, applications and data, regardless of location. This consolidation replaces complexity with clarity, allowing the organisation to inspect traffic for threats in real-time. Security policies are now enforced continuously, threats are detected and contained faster, and access to critical systems is governed by zero trust principles without compromising the user experience.

As Phil Sherwin, Chief Information Officer, at the RAF Association states:

Our data is one of our most valuable assets and the protection of that data, as we continue to provide life-changing support to members of the RAF community, is our most important priority. This partnership will move us into the next generation of security tools that adopt zero trust principles and is a crucial step on our journey to providing a layered approach to data protection.

One of the most critical aspects of this modernisation is supporting RAFA’s diverse workforce, particularly within the RAFAKidz nursery sites. These environments rely on nondesk-based staff using iPads and mobile devices to get their critical work done.

Using zero touch provisioning and the Prisma Browser™, we are enabling secure, seamless connectivity for unmanaged devices. This ensures that nursery staff can access necessary SaaS applications safely without complex login hurdles or manual configuration, improving their agility and allowing them to focus on caring for children rather than troubleshooting technology.

Creating Operational Advantage by Scaling Operations with AI and Automation

As a charity, RAFA has a responsibility to ensure resources are used efficiently. A critical goal of this partnership is to improve productivity and allow the organisation to scale its services without increasing the IT burden.

By adopting Strata™ Cloud Manager with AIOps (artificial intelligence for IT operations), RAFA is shifting from reactive security operations to proactive, automated management. Machine learning helps identify configuration risks and performance issues before they affect users, while standardized policies enable the secure, consistent onboarding of new sites. This shift is projected to significantly reduce operational overhead, enabling RAFA to scale its support network cost-effectively. This shift is projected to reduce operational overhead by 40–50%.

A Resilient Future

This partnership is about more than deploying technology. It is about ensuring RAFA remains resilient, trusted and capable of supporting the RAF community for decades to come.

As Darren Bisbey, Head of Group Information Security for the RAF Association, puts it:

We live in an era where digital threats are accelerating in both scale and sophistication, creating unprecedented challenges for organisations. Our partnership with Palo Alto is a statement of intent, reflecting our unwavering commitment to building the most secure environments possible for our data.

At Palo Alto Networks, we are honored to support RAFA in this journey, providing the digital armour and operational advantage necessary to protect those who serve and have served.

As Alistair Wildman, Palo Alto Networks CEO for Northern Europe states:

For over 90 years, RAFA has been a lifeline for the RAF community; it is our privilege to ensure that legacy endures in a digital-first world. By embracing a unified, AI-driven platform, RAFA is moving beyond complex, fragmented security to a posture that is Secure by Design. This partnership allows them to navigate today’s threat landscape with confidence, ensuring their resources remain focused where they belong: on the families who need them.

Key Takeaways

1. Digital Resilience – Strategic Shift to Zero Trust Architecture: RAFA is modernizing its cybersecurity posture by implementing a comprehensive zero trust architecture. This transition involves moving from fragmented legacy tools to a unified platform approach, deploying Prisma^® SASE and Cortex XDR for 360-degree visibility and complete control over access and traffic.
2. Interoperability – Secure, Seamless Access for Diverse Workforce: The partnership ensures operational agility by simplifying security for nondesk-based staff, particularly at the RAFAKidz nursery sites. Solutions like Zero-Touch Provisioning and the Prisma Access Browser enable secure, seamless connectivity for unmanaged devices, allowing nursery staff to focus on their critical work without complex login or configuration issues.
3. Creating Operational Advantage – Efficiency and Scalability through AI and Automation: RAFA is leveraging technology to scale services efficiently and reduce operational overhead. By using Strata Cloud Manager with AIOps (Artificial Intelligence for IT Operations), the organization can shift to proactive management and automating remediation, which is projected to reduce operational overhead by 40–50%.

The post Empowering the RAF Association with Next-Generation Cyber Resilience appeared first on Palo Alto Networks Blog.

The early weeks of 2026 have already made one thing clear: Government cybersecurity is in a new phase, shaped not by incremental change, but by the rapid integration of AI into core public-sector missions. AI systems are now embedded in critical infrastructure, federal service delivery, research environments, as well as state and local operations. At the same time, nation-state adversaries are leveraging AI to accelerate intrusion, scale deception and manipulate trusted systems in ways not possible even a year ago.

As Senior Vice President of Public Sector at Palo Alto Networks, I see a decisive shift underway. Defending the public sector in 2026 means navigating a world where security depends on verifying identity, securing data and governing AI-driven systems that act without human intervention. Success now hinges on architectures that assume automation, operations that prioritize coordination, and governance frameworks capable of managing AI at mission scale.

Here are the developments that will define the year ahead.

Federal Government

1. AI-Native Security Must Become Integral to Federal Operations

AI in federal environments is no longer an experiment. Agencies are now designing workflows, SOC missions and cloud architectures around AI-driven detection and response. The emphasis is shifting from supplementing human analysts to building systems that maintain visibility, correlate threats, and respond autonomously when human capacity is limited. This builds on what we forecasted last year, when federal cybersecurity teams began using AI to replace manual workflows and drive down detection and response times.

The shift will be practical. Federal teams must plan to deploy AI systems that correlate logs, identify behavioral anomalies, prioritize threats, and suppress noise before analysts ever see an alert. Manual, ticket-based workflows will no longer meet federal timelines for investigation or reporting, particularly as adversaries automate more phases of attack.

2. Identity Emerges as the Central Federal Security Challenge

The biggest shift in 2026 will be the collapse between “identity” and “attack surface.” Deepfake technologies now operate in real time. AI-generated voices and video can impersonate senior leaders at a level undetectable by traditional controls. Machine identities continue to proliferate; they will outnumber human identities this year. And autonomous agents can initiate high-impact actions without human oversight. This reflects a broader crisis of authenticity now reshaping how enterprises defend identity itself.

Identity abuse will no longer be limited to credential theft. This turns identity into a systemic risk. One compromised identity (human, machine or agent) can cascade through automated systems with little friction. Federal programs will need to prioritize continuous identity verification, stronger proofing and governance frameworks that validate the legitimacy of both human and AI-driven activity.

3. AI Systems Must Be Secure-by-Design

Stemming from the clear mandate in the AI Action Plan (and subsequent work by NIST to develop an AI/Cyber Profile on top of the existing Cybersecurity Framework) agencies will steadily integrate AI security into their deployment of AI technologies.

This imperative is critical as AI systems are susceptible to novel threats. Data poisoning of training sets, manipulated inputs and hidden instructions in untrusted datasets compromise the intelligence that agencies rely on for analysis, planning and mission support. To support the security of this AI-first moment, Palo Alto Networks was proud to make its AI security platform, Prisma^® AIRS™, available through the GSA OneGov initiative.

4. Nation-State Operations Expand Through AI Automation

Adversaries will use AI to compress the time between reconnaissance, exploitation and lateral movement. We expect rapidly increasing the use of AI to chain vulnerabilities, tailor social engineering campaigns, and generated malware variants that adapt in real time.

The focus will broaden beyond IT networks. AI will be used to disrupt OT systems and target sensitive research environments. Foreign intelligence services will weaponize AI to blur the line between intrusion and information operations, producing hybrid campaigns that attack both systems and the legitimacy of institutions.

5. Autonomous SOC Capabilities Become Essential

Federal SOCs will evolve from human-centered command centers to hybrid operations where autonomous agents run major components of the detection and response mission. These agents will triage alerts, enforce containment, and initiate predefined responses.

This evolution comes with risk. AI agents with broad authority can be misused or manipulated if not properly governed. Agencies will need safeguards to track agent behavior, enforce least privilege on agents, and prevent misuse through runtime monitoring and “AI firewall” controls designed to stop malicious prompts and unauthorized actions. The same pressures are shaping enterprise security, where controls like AI firewalls and circuit breaker mechanisms are becoming standard practice. Automation will only strengthen federal security if paired with rigorous oversight and continuous validation of agent activity.

6. Shared and Federated SOC Structures Gain Momentum

As threats scale, agencies will increasingly operate through shared or federated security structures. Instead of isolated SOCs, agencies will adopt analytics layers capable of correlating activity across departments and exchanging findings in real time.

This shift will reduce redundancy and provide faster insight into nation-state campaigns that cross federal boundaries. Early adopters will establish shared analytic and response frameworks that allow agencies to coordinate without sacrificing mission-specific control. Civilian agencies will lead early adoption with broader participation across defense and national security stakeholders expected later in the year.

7. The Post-Quantum Deadline Becomes Immediate

In 2026, post-quantum cryptography planning will move to implementation. Accelerated advances in quantum computing and AI-based cryptanalysis will push agencies to transition from pilot efforts to mandated modernization.

Agencies will focus on discovering where vulnerable algorithms are used, replacing outdated libraries, and implementing crypto-agility so systems can evolve without major redesigns. Systems with unpatchable cryptographic components will be flagged for full replacement, forcing agencies to reconcile years of accumulated “crypto debt.”

8. Data Trust and Cloud Workload Protection Become Priority Missions

The rise of AI workloads will force agencies to rethink how they protect data. Infrastructure controls alone cannot detect when training data has been manipulated or when model outputs no longer reflect real-world conditions.

Agencies will unify developer and security workflows and use tools like Data Security Posture Management and AI security posture management (AI-SPM) to track data lineage and enforce protections at runtime. Enterprises are addressing the same issue by bringing development and security teams together under shared data governance models. Ensuring model trustworthiness will become a mission-support requirement, not just a security objective.

9. Platform Consolidation Becomes Necessary

Fragmented tools cannot support the visibility and oversight required for AI governance. Executives will push for platform consolidation to unify network, identity, cloud, endpoint and AI security. Integrated platforms will gain favor because they enable consistent policy enforcement and a single operational picture across increasingly automated environments.

State, Local and Educational Institutions

1. AI Adoption Splits SLED into Distinct Tiers

In 2026, disparities in funding and technical capacity will widen. Some states will deploy AI across security operations, citizen services and identity verification. Others will struggle to maintain legacy systems.

Well-resourced jurisdictions will reduce response times and improve resilience. Underfunded ones will remain exposed to ransomware and disruption. Without targeted modernization efforts, a national divide in SLED cybersecurity maturity will deepen.

2. Regional Models Become the Practical Path Forward

Silos are no longer sustainable. SLED organizations will rely on shared SOCs, regional threat intelligence hubs and coordinated incident response agreements. States will formalize partnerships to share expertise, reduce costs and defend interconnected systems. This evolution represents the maturation of the “team sport” mentality we predicted in 2025. These models reflect operational reality: Compromised data or infrastructure in one jurisdiction often creates immediate risk for its neighbors.

3. Higher Education Redesigns Its Security Baseline

Universities will classify cybersecurity alongside energy, research infrastructure and physical security as essential institutional functions. Secure browser adoption, stronger vendor oversight and centralized identity governance will become the norm.

AI research environments will receive increased scrutiny, and universities participating in federally funded research will face stricter compliance requirements to prevent data poisoning and model manipulation. Institutions with large research portfolios will prioritize securing lab environments where AI models are trained and evaluated.

4. K–12 Systems Enter a New Phase of Security Oversight

States will introduce new security mandates for K–12 environments, covering MFA, network segmentation, secure browsers, identity verification and foundational zero trust principles. AI-enabled ransomware will remain a threat. Smaller districts will adopt managed services or regional support structures as they confront growing operational and compliance demands. Districts that modernize identity controls and browser security will significantly reduce their exposure compared to those reliant on legacy tools. Building on the regulatory momentum we predicted in 2025, K–12 institutions will continue moving from defensive posture to proactive security adoption.

5. Local Governments Face Escalating AI-Driven Ransomware

Municipal governments remain high-value targets due to limited staffing and aging infrastructure. AI gives threat actors the ability to automate reconnaissance, craft targeted phishing messages, and identify vulnerabilities with little effort.

Attacks timed to public safety incidents or weather emergencies will increase, meaning local governments will need stronger identity controls, automated endpoint protection and access to managed detection and response. Operational continuity will depend on reducing time-to-detect and time-to-contain, capabilities that smaller municipalities cannot achieve without external support.

6. Managed Services and Platform Consolidation Become Standard

As technical demands grow, SLED organizations will move toward managed SOC models and consolidated vendor ecosystems. Platforms that integrate data protection, threat detection, identity governance and AI oversight will gain traction. Point tools without interoperability will decline. Budget-constrained environments will favor comprehensive platforms that reduce operational burden and simplify compliance.

7. Identity and Data Trust Become Central SLED Priorities

SLED organizations manage sensitive student records, election data and social services information. These environments are increasingly strained by the rapid growth of machine identities and AI-driven applications.

Synthetic identities and AI-generated credentials will be used to infiltrate systems with limited oversight. Continuous identity verification, data lineage tracking and posture management will become essential to prevent fraud, service disruption and data manipulation. Identity assurance and data integrity will become the foundation of public trust at the state and local level.

The post 2026 Public Sector Cyber Outlook: Identity, AI and the Fight for Trust appeared first on Palo Alto Networks Blog.

Why the GSA OneGov Agreement Is a Game-Changer for Federal Cybersecurity

The mission to modernize government IT is accelerating at lightning speed, largely thanks to the transformative power of artificial intelligence (AI). Federal agencies are strategically leveraging AI to boost efficiency, enhance citizen services, and strengthen national security – a vision fully supported by the administration’s AI Action Plan.

At Palo Alto Networks, we are all-in on helping agencies deploy AI bravely and securely. Because the challenge isn't just about using AI for cyberdefense, but also about defending AI itself. We appreciate the U.S. General Services Administration (GSA) recognizing the critical need for scalable, efficient solutions.

That is precisely why the GSA OneGov Initiative is a massive, game-changing step forward. We are proud to be the first pure-play cybersecurity vendor to secure a OneGov agreement with the GSA. This strategic alliance simplifies and standardizes the process for agencies to access our world-class, AI-powered security platform, ensuring security is foundational to this crucial modernization mission.

The Wake-Up Call: The Silent Threat of AI Agent Corruption

If you needed a clear sign that AI has fundamentally shifted the cybersecurity landscape, our own Unit 42 research provides it. The new reality isn't just about hackers using AI in their attacks; it’s also about how internal AI provides another attack surface for threat actors.

The most insidious new threat we've observed is AI Agent Smuggling, where malicious attackers use AI agents to exploit other agents. Our Unit 42 research highlights two major vectors:

• Indirect Prompt Injection: A security risk in LLMs where a user crafts input containing deceptive instructions to manipulate the model’s behavior, which can lead to unauthorized data access or unintended actions.
• Agent Session Smuggling: Exploit vulnerabilities in agent-to-agent communication, injecting malicious instructions into a conversation, hiding them among otherwise benign client requests and server responses.

This confirms our core belief as stated in a recent secure AI by Design blog: The AI ecosystem (the models, data and infrastructure) is now a complex, expanding attack surface that traditional perimeter defenses were simply not designed to protect.

As I’ve said before, “If you’re deploying AI, you must deploy AI security.”

Secure AI by Design: A Strategic Alliance with GSA

The GSA’s OneGov Initiative aims to streamline procurement and drive down costs by leveraging the purchasing power of the entire federal government. This is more than an agreement; it’s a direct response to the call for a "secure-by-design" approach to federal AI adoption. This agreement simplifies and standardizes the process for agencies to access our world-class, AI-powered security platform, ensuring that security is foundational, not an afterthought. It provides industry leading AI security tools into the hands of our cyber defenders today.

Under the Hood: Technical Capabilities for the AI Ecosystem

To counter the autonomous threats we’re seeing, we provide a platform that protects the entire AI lifecycle, from the developer's keyboard to the data center.

1. Runtime Protection for AI Workloads

Securing the AI supply chain requires visibility across every stage, especially during runtime when models are processing sensitive data.

• Prisma^® AIRS delivers comprehensive security for the entire AI lifecycle, in one unified platform. It allows organizations to deploy traditional apps as well as AI applications, models and agents with confidence by reducing risk from misuse, data loss and sophisticated AI-driven threats. Prisma AIRS provides a clear, connected view of assets in multicloud environments, so teams can eliminate silos, accelerate responses, as well as scale cloud and AI apps securely.
• Our Cloud-Native Application Protection Platform (CNAPP) has achieved the FedRAMP High designation, making it the preferred Code to Cloud solution to secure the entire application lifecycle from development to runtime. Our industry-leading CNAPP eliminates silos to deliver comprehensive visibility and best-in-class protection across multicloud environments.

2. Protecting Users and Data at the Edge

Even the most advanced AI defenses are undermined if users accessing applications and data are left vulnerable outside corporate security boundaries. The explosive growth of generative AI tools and the unseen behavior of AI agents are amplifying data exposure risks.

• Prisma SASE (secure access service edge) secures all users, apps, devices and data, no matter where they are and no matter where applications reside.
  • Prisma Access (FedRAMP High Authorized) and Prisma Browser (FedRAMP-Moderate Authorized) integrate security capabilities, like zero trust network access (ZTNA), secure web gateway (SWG) and cloud access security broker (CASB), to provide a unified policy framework and a consistent user experience.
• This approach helps agencies outpace the speed of AI-driven threats, safeguarding critical data and simplifying operations for a frictionless user experience. It ensures that the human element interacting with the AI is protected by the most stringent security controls available.

Deploy AI Bravely

The GSA OneGov agreement is a pivotal moment that provides federal agencies with the cost-effective, streamlined access they need to deploy AI with confidence. By leveraging our unified, AI-powered platform, government organizations can stop reacting to threats and start building secure-by-design AI environments. We are committed to remaining a key partner in this strategic initiative and helping the government achieve its mission outcomes safely.

For more information and access to promotional offers for new contracts signed on or before January 31, 2028, federal agencies can visit the GSA OneGov website.

The post Securing the AI Frontier appeared first on Palo Alto Networks Blog.

Modernizing Vulnerability Sharing for a New Class of Threats

In cybersecurity, vulnerability information sharing frameworks have long assumed that conventional threats exploit flaws in software or systems, and they can be resolved with patches or configuration updates. AI and machine learning (ML) models upend that premise as adversarial attacks, like poisoning and evasion, target the unique way AI models process information. Consequently, the risks for AI systems include tactics like model poisoning (from evasion attacks) in datasets and training, which are not conventional software vulnerabilities. These new vulnerabilities fall outside the scope of traditional cybersecurity taxonomies like the Common Vulnerabilities and Exposures (CVE) Program.

There is a need to bridge the gap between the existing cybersecurity vulnerability sharing structure and burgeoning efforts to catalog security risks to AI systems. Provisions in the White House AI Action Plan, which Palo Alto Networks supports, call for the creation of an AI Information Sharing and Analysis Center (AI-ISAC), reinforcing the importance of addressing that disconnect. This integration is essential, as leveraging the existing, widely adopted cybersecurity infrastructure will be the fastest path to ensuring these new standards are accepted and operationalized.

Established Construct for Vulnerability Management and Disclosure

The global cybersecurity community relies on a mature infrastructure for sharing standardized vulnerability intelligence. Central to this ecosystem is the CVE List, established in 1999 as the authoritative catalog of cybersecurity vulnerabilities. Through CVE IDs and a network of CVE Numbering Authorities (CNAs), this framework enables consistent vulnerability documentation and disclosure.

Similarly, the Common Vulnerability Scoring System (CVSS) provides standardized severity assessments, allowing security teams to prioritize responses. Together with resources like the National Vulnerability Database (NVD) and CISA’s KEV Catalog catalog, these tools form the backbone of global vulnerability management, information sharing and coordinated disclosure.

Why AI Breaks the Traditional Model

While this infrastructure has served the cybersecurity community effectively for over two decades, it was designed around traditional threat models that AI systems substantially upend. Attacks on AI systems represent a critical departure from traditional cybersecurity threats as they operate insidiously, subtly corrupting core reasoning processes, causing persistent, systemic failures, some of which only become evident over time. Most traditional cybersecurity tools are not equipped to recognize those breakdowns because they assume deterministic behavior and rules-based logic. AI systems defy those assumptions because AI is probabilistic, not deterministic. Consequently, attacks on AI models may remain hidden for extended periods.

Unlike traditional cybersecurity threats that target code, adversarial AI attacks target the underlying data and algorithms that govern how AI systems learn, reason and make decisions. Consider the following predominant adversarial attack methodologies on machine learning:

• Poisoning attacks inject malicious data into training datasets, corrupting the model's learning process and creating deliberate vulnerabilities or degraded performance.
• Inference-related attacks exploit model outputs to extract sensitive information or learn about its training data. This includes model inversion, which reconstructs sensitive data from the model's outputs, as well as membership inference, which identifies whether specific data points were used in training.

The expansion of existing security frameworks and programs is necessary to cover the enumeration, disclosure and downstream management of security risks to AI systems.

Advancing AI Security Through the AI Action Plan

In July, the Administration unveiled the AI Action Plan, an innovation-first framework balancing AI advancement with security imperatives. The Plan prioritizes Secure-by-Design AI technologies and applications, strengthened critical infrastructure cybersecurity and protection of commercial and government AI innovations.

Notably, it recommends establishing an AI Information Sharing and Analysis Center (AI-ISAC) to facilitate threat intelligence sharing across U.S. critical infrastructure sectors and encourages sharing known AI vulnerabilities, “tak[ing] advantage of existing cyber vulnerability sharing mechanisms.” These provisions affirm that AI security underpins American leadership in the field and, where possible, should be built upon existing frameworks.

Redefining Boundaries for AI Threats

To position the CVE Program for the AI-driven future, Palo Alto Networks is engaging directly with industry and program stakeholders to chart the path forward. Traditionally, the CVE Program serves as an ecosystem-wide central warning system. It provides a unified source of truths for security risks. A security risk catalog and identification system are needed for AI systems, as they currently fall outside the traditional scope of the CVE Program that has focused exclusively on vulnerabilities rather than on malicious components. The historical aperture of the current CVE Program excludes harmful artifacts, such as backdoored AI models or poisoned datasets, which represent fundamentally different attack vectors, in turn creating security blind spots.

Securing AI’s Promise

The United States leads in AI innovation and must equally lead in securing it. As momentum builds behind the AI Action Plan and the establishment of the AI-ISAC, we have a critical window to shape information sharing frameworks of the future. The goal is to ensure that cybersecurity and AI security infrastructure advance in unison with the technology itself. Integrating new AI vulnerability standards into trusted frameworks like the CVE Program aligns with industry focus and needs. Through proactive, coordinated action, we can unlock AI’s full promise while safeguarding the models that are embedded in the critical systems on which our nation depends.

The post Bridging Cybersecurity and AI appeared first on Palo Alto Networks Blog.

The transformative potential of artificial intelligence (AI) across industries is undeniable. But realizing AI's true value hinges on three cybersecurity imperatives: Understanding the AI-cybersecurity nexus, harnessing AI to supercharge cyber defense, and embedding security into AI tools from the ground up through Secure AI by Design.

Nowhere is this convergence more urgent than in financial services. Sitting at the center of our global economy, financial institutions face a dual mandate: Embrace AI for cybersecurity and cybersecurity for AI.

I was honored to cover these key principals in my testimony before the House Committee on Financial Services, led by Chairman French Hill. The hearing, entitled “From Principles to Policy: Enabling 21st Century AI Innovation in Financial Services” convened witnesses from Palo Alto Networks, Google, NASDAQ, Zillow and Public Citizen. Together, we examined AI use cases in the financial services and housing sectors, including those specific to cybersecurity. We assessed how existing laws and frameworks apply in the age of AI.

The Defense Advantage Is AI-Powered Security Operations

Attacks have become faster, with the time from compromise to data exfiltration now 100 times faster than four years ago. The financial sector bears disproportionate risk, given the value of its data and interconnected systems, while firms contend with evolving regulatory expectations, talent shortages and the persistent tendency to elevate cybersecurity only after an incident.

Generative and agentic AI intensify these pressures by accelerating every phase of the attack chain, from deepfake-driven fraud to tailored spear phishing campaigns. Our researchers at Unit 42^® have found that agentic AI, autonomous systems that can reason and act without human intervention, can compress what was once a multiday ransomware campaign into roughly 25 minutes.

To keep pace, financial institutions must pivot to AI-driven defenses that operate at machine speed.

Security operations centers (SOC) have long been overwhelmed by traditional alerts and fragmented data. Security teams, forced into manual triage across dozens of disparate tools, face an inefficient model that leaves vulnerabilities exposed, burns out analysts and makes it impossible to operate at the speed necessary to outpace modern attacks.

The average enterprise SOC ingests data from 83 security solutions across 29 vendors. In 75% of breaches, logging existed that should have flagged anomalous behavior, but critical signals were buried. With 90% of SOCs still relying on manual processes, adversaries have the clear advantage.

AI-driven SOCs flip this paradigm, acting as a force multiplier to substantially reduce detection and response times. To illustrate the scale of this necessity, consider our own security operations. Palo Alto Networks SOC analyzes over 90 billion events daily. Without AI, this would be an impossible task for human analysts. But by applying AI, we distill that down to a single actionable incident.

Financial institutions migrating to AI-driven SOC platforms are seeing transformative results:

• One customer reduced the Mean Time to Respond (MTTR) from one day to 14 minutes.
• Another prevented 22,831 threats and processed 113,271 threat indicators in less than 5 seconds.
• A large bank saved 180 hours per year by automating security information and event management reporting; 500 hours through automated data collection; 360 hours by automating four Chief Technology Officer playbooks; and 240 hours with automated threat intelligence enrichment.

These improvements are critical to stopping threat actors. But none of this would be possible without AI.

Securing the New AI Attack Surface

As AI adoption grows, it will further expand the attack surface, creating new vectors targeting training data and model environments. AI's rapid growth is outpacing the adoption of security measures designed to protect it. Nearly three-quarters of S&P 500 companies now flag AI as a material risk in their public disclosures, up from just 12% in 2023.

Traditional security tools rely on static rules that miss advanced attacks, like multistep prompt injections or adversarial manipulations. Autonomous AI agents can take unpredictable actions that are difficult to monitor with legacy methods.

Rapid AI adoption has exposed organizations' infrastructure, data, models, applications and agents to unique threats. Unlike traditional cyber exploits that target software vulnerabilities, AI-specific attacks can manipulate the foundation of how an AI system learns and operates.

A Secure AI by Design

Even with an understanding of the risks, many organizations struggle with the lack of clarity on what effective AI security looks like in practice. Recognizing the gap between intent and execution, Palo Alto Networks developed a Secure AI by Design policy roadmap that provides organizations with a comprehensive roadmap that integrates security throughout the entire AI lifecycle.

A proactive stance ensures security is a feature, not an afterthought, crucial for building trust, maintaining compliance and mitigating risks. The approach addresses four imperatives organizations most pressingly face in AI adoption:

1. Secure the use of external AI tools.

2. Secure the underlying AI infrastructure and data.

3. Safely build and deploy AI applications.

4. Monitor and control AI agents.

The Path Forward

For financial institutions, Secure AI by Design must be anchored in enterprise governance. Institutions should maintain risk-tiered AI inventories, enforce strict access controls and implement testing commensurate with risk. Governance structures should enable board oversight and align with established model risk practices.

Policymakers also have a critical role to play in promoting AI-driven security operations, championing voluntary Secure AI by Design frameworks, ensuring policies safeguard innovation, enabling controlled experimentation and strengthening public-private collaboration.

Ultimately, the financial institutions that will thrive will recognize cybersecurity as the foundation that makes innovation possible. By embracing AI-driven defenses and securing AI systems from the ground up, the sector can confidently unlock AI's transformative potential while safeguarding the trust and stability that underpin the global economy.

Read the full testimony to learn more about how cybersecurity can enable AI innovation in financial services.

The post From the Hill: The AI-Cybersecurity Imperative in Financial Services appeared first on Palo Alto Networks Blog.

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