Classification
AI Risk Management & Controls
Overview
Security and safety in AI governance refer to the comprehensive set of policies, controls, technical safeguards, and organizational processes designed to prevent, detect, and respond to risks associated with AI systems. This includes ensuring the confidentiality, integrity, and availability of AI models and data, as well as minimizing harm from system failures or adversarial attacks. Key elements involve incident response (IR) planning, robust failure handling mechanisms, adversarial resilience (e.g., robustness against data poisoning or model inversion), and continuous monitoring. While security focuses on protecting systems from intentional harm (e.g., cyberattacks), safety emphasizes preventing unintended consequences (e.g., model drift or unsafe outputs). A limitation is the evolving nature of threats, which can outpace existing controls, and the challenge of aligning security and safety practices across complex supply chains or third-party providers.
Governance Context
Security and safety obligations are embedded in major AI governance frameworks. For example, the NIST AI Risk Management Framework (AI RMF) requires organizations to implement continuous monitoring and incident response plans for AI systems. The EU AI Act mandates providers of high-risk AI systems to establish risk management systems, including post-market monitoring and mandatory incident reporting to authorities. ISO/IEC 27001:2022, while not AI-specific, sets out controls for information security management that are often adapted for AI contexts, such as access control, data encryption, and vulnerability management. Organizations must ensure contractual clauses mandate timely incident reporting (e.g., within 72 hours), conduct regular adversarial testing, and maintain up-to-date documentation of failure modes and mitigation strategies. Two concrete obligations include: (1) implementing and regularly testing incident response plans for AI systems, and (2) requiring third-party vendors to report AI-related security incidents within a specified timeframe (such as 24 or 72 hours).
Ethical & Societal Implications
Robust security and safety in AI systems are critical to protecting individuals and society from harms such as privacy breaches, physical injury, or systemic bias. Failure to implement effective controls can erode public trust, amplify vulnerabilities for marginalized groups, and result in regulatory sanctions. Moreover, overemphasis on security may stifle innovation or create burdensome compliance costs, while insufficient safety measures can lead to catastrophic failures. Ethical governance requires balancing these considerations, ensuring transparency, accountability, and proportionality in risk mitigation strategies.
Key Takeaways
Security and safety are distinct but complementary pillars in AI governance.; Incident response planning and adversarial resilience are essential controls.; Frameworks like NIST AI RMF and the EU AI Act impose concrete obligations.; Real-world failures often stem from gaps in monitoring or reporting.; Ethical AI governance requires balancing risk mitigation with innovation.; Contractual clauses should mandate timely incident reporting and risk controls.; Continuous monitoring and documentation of failure modes are critical for compliance.