Insights
Analysis for risk and security executives, and regulatory briefings for business leadership. Each piece is labelled so you can read to the depth the question requires.
Patterns from a Geneva workshop on trust management for agentic AI, feeding into the AI for Good Summit and ITU Study Group 17's work on AI security standardization.
Read article →GovTech Singapore's Agentic Risk and Capability Framework is the most structured agentic governance framework available in Singapore today. This article reads it seriously, uses its own vocabulary, and names the architectural step that follows it.
Read article →Every board is asking whether there is a kill switch for their AI agents. The honest answer is: probably not in the way the question assumes. A kill switch is not a button. It is an architecture. Here is what it actually requires.
Read article →Gartner's four-level autonomy framework correctly diagnoses the classification problem. But classifying an agent's risk level does not constrain its behaviour. Governance becomes real only when execution can be interrupted, escalated, or denied.
Read article →Most AI governance programmes cannot enforce a single rule they have written down. The policies exist. The governance committees have signed off. And then the systems get deployed, governed by a paragraph of natural language hoping the model complies. Prompts are not governance. This is what governance actually requires.
Read article →Singapore's updated Model AI Governance Framework includes over ten case studies. One of them, Terminal 3's payroll agent, shows what enforcement-layer governance actually requires technically. Here's what separates it from the others.
Read article →Gartner has a term for the gap between what AI agent vendors are selling and what organisations are actually buying: agentwashing. Here is what it means for enterprises in Singapore and Southeast Asia, and the three questions that cut through it.
Read article →Infrastructure governance tells you whether a system was secure. Agentic governance asks whether the authority exercised was legitimate. Two scenarios that show why the gap between them matters.
Read article →APRA named Anthropic Mythos as an example of frontier AI cyber risk in its April 2026 letter to regulated entities. The four findings translate directly into questions Southeast Asian boards should already be asking, regardless of jurisdiction.
Read article →AI governance has moved beyond model safety. The critical frontier is now the execution layer, where agents stop generating text and start taking actions. Here is what Anthropic, IBM, Microsoft, Salesforce, Google, and OpenAI are doing about it.
Read article →Most enterprise AI governance stops at the policy document. Agentic systems running on real data, across real users, in regulated industries need governance at the runtime layer. Here is where the gap is, and why it matters now.
Read article →PM Lawrence Wong chairs a National AI Council with a confirmed mandate to set clear rules for AI development. Budget 2026 removed any remaining ambiguity. Here is what the shift from voluntary guidance to enforceable national policy means for mid-market companies.
Read article →Most AI governance failures are not systems that misbehave. They are systems that proceed. The model produces output, the pipeline accepts it, the system executes, and no one ever explicitly granted that authority. Here is what governance looks like when it is designed rather than assumed.
Read article →Singapore's Model AI Governance Framework is principles-based, updated for generative AI, and extended in 2026 to cover agentic AI. This article explains what genuine compliance looks like in practice, not just what the framework says.
Read article →The EU AI Act is in force as a legal instrument, but enforcement of its most significant obligations has been pushed to late 2027 and beyond. Singapore businesses with EU exposure still need to prepare. Here is the updated picture.
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