The Cybersecurity and Infrastructure Security Agency (CISA) is spearheading the adoption of a “Secure by Design” approach to AI-based software, recognizing the potential safety and security concerns that come with the widespread implementation of Artificial Intelligence (AI) in various industries. At the forefront of this initiative is the integration of AI red teaming, a third-party assessment process, into the Testing, Evaluation, Verification, and Validation (TEVV) framework.
By incorporating AI evaluations into established software TEVV practices, stakeholders can leverage years of experience from traditional software security while adapting them to the unique challenges presented by AI systems. The main goal of this effort is to ensure rigorous safety and security testing to mitigate the risks of physical attacks, cyberattacks, and critical failures in AI systems.
One of the key reasons why AI red teaming is crucial is its systematic approach to testing AI systems for vulnerabilities and assessing their resilience. By simulating potential attacks or failure scenarios, this process helps developers identify weaknesses that could be exploited, allowing them to address these issues before deployment. CISA emphasizes that AI red teaming should not be a standalone activity but rather a part of the broader AI TEVV framework, ensuring that AI systems are thoroughly tested for reliability, safety, and security in line with the needs of critical infrastructure.
The initiative also underscores the importance of aligning AI TEVV with traditional software TEVV frameworks. While AI systems present unique challenges, they share fundamental similarities with traditional software systems in terms of safety risks, validity testing, reliability, and probabilistic behavior. By leveraging existing TEVV methodologies, AI evaluations can effectively address these challenges and ensure the robustness of AI systems.
CISA’s role in enhancing AI security evaluations extends across pre-deployment testing, post-deployment testing, standards development, and operational guidance. By collaborating with industry, academia, and government entities, CISA aims to develop AI evaluation benchmarks and methodologies that integrate cybersecurity considerations and ensure robust security in operational environments. The agency also contributes operational expertise to the development of AI security testing standards in partnership with NIST.
Treating AI TEVV as a subset of software TEVV offers several benefits, including efficiency, consistency, and scalability. By avoiding duplicative testing processes and applying proven methodologies, stakeholders can streamline the evaluation process and focus on addressing AI-specific challenges while building on the solid foundation of software TEVV. This approach encourages innovation at the tactical level and ensures that AI systems meet high cybersecurity benchmarks.
In conclusion, as AI continues to play a crucial role in critical infrastructure, it is essential to prioritize its safety and security. By integrating AI evaluations with established software testing frameworks and drawing on decades of expertise, stakeholders can effectively mitigate risks and ensure that AI systems are reliable and secure. With organizations like CISA and NIST leading the way, the future of AI security looks promising with a balanced blend of innovation and proven practices.