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NSS Labs Introduces New AI Protection Test Framework

In an era of rapidly evolving cyber threats, NSS Labs is taking a substantial step toward enhancing the security of enterprise AI systems. On April 28, 2026, the company announced the release of its groundbreaking AI Protection Systems (AIPS) test methodology. This innovative framework is designed to provide organizations with a comprehensive and technical approach to evaluating the functionality of their AI security measures.

As artificial intelligence (AI) continues to gain traction within various sectors, the speed at which new vulnerabilities emerge presents a significant challenge to security teams. The speed and sophistication of attacks have outpaced the ability of organizations to respond effectively, making it imperative that enterprises not only assume their protection measures are effective but can also demonstrate their performance under real-world conditions.

The AIPS methodology scrutinizes AI protection systems through eight critical dimensions, executing hundreds of thousands of variations of attack simulations. These dimensions include prompt injection resistance, data exfiltration prevention, system resilience, policy enforcement accuracy, agentic AI security, and observability. This multifaceted approach reflects the complex nature of real-world threats, helping organizations identify and prioritize their defenses effectively.

The Need for Rigorous Testing

In response to the shifting landscape of AI security, NSS Labs recognizes that conventional validation methods are no longer adequate. Organizations require continuous, independent evaluations that clearly outline how well their AI security controls perform. The AIPS framework addresses this by employing a rigorous adversarial testing process that simulates actual attack scenarios, rather than relying on idealized configurations.

Vikram Phatak, CEO of NSS Labs, emphasizes that “AI security is fundamentally different from anything we've tested before.” With the dynamic and context-driven nature of AI systems, the AIPS methodology is crafted to provide a realistic reflection of how such systems operate and can be misused in enterprise environments.

A Model of Adversarial Testing

A distinctive feature of NSS Labs' approach lies in its adversarial testing framework, where vendors are not granted prior visibility into test cases. This methodology ensures that results are indicative of authentic conditions, providing a clearer picture of AI protection technology performance when faced with unpredictable threats. This independence is crucial to validating security claims made by various AI offerings in the market.

Keysight Technologies has partnered with NSS Labs as the lead collaborator in the AIPS initiative, leveraging its expertise in developing scalable and realistic test environments. Keysight’s involvement enhances NSS Labs' capability to model intricate attack scenarios, which are pivotal for measuring system performance amidst genuine operational conditions.

Seeking Feedback and Collaboration

As NSS Labs finalizes its AIPS methodology version, the organization is actively soliciting feedback from enterprises and security vendors until May 15, 2026. Interested parties are encouraged to contribute insights to refine the methodology and partake in the associated test program, demonstrating a commitment to advancing cybersecurity validation.

With growing dependence on AI, enterprises cannot afford to compromise on security. NSS Labs’ new testing methodology aims not only to help organizations understand the validity of their current protections but also to position them effectively within a layered AI security strategy. By clarifying the performance capabilities of differing technologies, enterprises can better identify complementary solutions that fill existing security gaps. In the shadow of increasing cyber risks, NSS Labs is committed to equipping organizations with the insights they need for robust, defensible security outcomes. For those interested in more information, visit nsslabs.com.