AI Testing

AI testing refers to the processes used to evaluate whether an AI system behaves reliably, safely, and as intended before and after it is deployed. It covers activities such as checking accuracy and bias, probing for harmful or unexpected outputs, and running red-team exercises where testers deliberately try to make a system fail. For compliance and risk leaders, testing records are often the evidence regulators and auditors ask for when they want to see that an organization has done due diligence on an AI system it relies on.

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ResearchSingapore2026-10-21

Singapore's IMDA Publishes Practical Testing Guidance for Agentic AI Assurance

Singapore's Infocomm Media Development Authority (IMDA) released new AI assurance exchange initiatives on October 14, 2026, providing structured guidance on testing agentic AI systems using realistic scenarios. The guidance covers test execution, log analysis, and technical controls for evaluating agent autonomy, tool use, monitoring, and accountability. It gives compliance teams a concrete methodology to assess whether deployed AI agents behave within authorized boundaries.

Singapore IMDA agentic AI AI assurance AI testing audit logs agent governance