AI Safety

AI safety is concerned with ensuring that AI systems behave as intended, avoid causing harm, and remain under meaningful human control. For organizations deploying AI today, it has a practical near-term dimension: outputs should be accurate, systems should resist misuse and manipulation, humans should remain in the loop for consequential decisions, and there should be clear accountability when something goes wrong. It also has a longer-term dimension as AI systems become more autonomous — questions about what actions they can take, how they can be interrupted, and who remains responsible for their behavior.

AI safety is increasingly a regulatory requirement, not just an ethical one. Jurisdictions across the EU, US, and Asia are introducing obligations for safety documentation, impact assessments, and human oversight of high-risk AI systems. Organizations that treat safety as a launch checklist rather than an ongoing practice will find themselves exposed — both to regulatory scrutiny and to the operational failures that safety measures are designed to prevent. Agentic AI systems, which take real-world actions, raise the stakes further: a system that can send emails, execute transactions, or modify data needs an interrupt mechanism before it goes anywhere near production.