First-Order Risks

Mitigation strategy

1. Implement continuous data validation and integrity pipelines across the entire AI lifecycle, encompassing periodic auditing of training datasets for bias, representativeness, and drift. This proactive measure is essential to ensure foundational model alignment and mitigate the risk of skewed or discriminatory system outcomes. 2. Employ comprehensive adversarial stress-testing, often referred to as AI red teaming, prior to deployment. This rigorous exercise should be designed to expose vulnerabilities, edge cases, and potential for input/output manipulation, subsequently informing the integration of adversarial training methods to enhance model robustness and resilience. 3. Establish a formal, continuous AI Risk Management Framework, such as the NIST AI RMF, to integrate governance, measurement, and monitoring of system performance and safety. This includes assigning clear accountability to risk owners and designing human-in-the-loop mechanisms for high-stakes decisions and unexpected model behavior.