Distinguish between verification and real-world validation in deployed AI systems.
Analyze how system components—including agents, APIs, logs, and plugins—can introduce hidden risks even when models behave correctly.
Evaluate real-world case studies to identify where privacy breaches, decision failures, or uncontainable outputs originated.
Apply design strategies such as rollback triggers, human intervention layers, and permission scoping to contain harm during AI deployment.
Critically assess the role of governance in post-deployment control, including the assignment of authority (e.g., Trustworthy AI Reviewer) to stop or reverse harmful system behavior.
Propose safeguards that support regulatory compliance (e.g., EU AI Act) and ethical deployment practices in high-stakes environments.
