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.

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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.

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