AI Assurance blog

Assurance: Why defence risks turning it into governance theatre

dallen@synoptix.co.uk (Dan Allen) — Mon, 01 Jun 2026 15:19:23 GMT

Why AI impact assessments validate delivery instead of shaping design

AI impact assessments are intended to influence decisions. In practice, they often arrive too late, ask the wrong questions and ultimately reinforce delivery pathways that are already fixed. Rather than shaping design, autonomy boundaries or operating concepts, they validate them.

This is not because impact assessment are inherently flawed. It is because of how, when and why it is applied. This isn't about just doing a better assurance process; it’s about having the right mindset, understanding and knowledge, centred around engineering judgement, to make the appropriate decisions for the appropriate measures. The table below provides two examples of patterns as a result of incorrectly conducted impact assessments.

Accountability is a design property: Assurance and liability in high-stakes AI

Callum Cockburn — Fri, 01 May 2026 09:45:00 GMT

Policy debates about AI often start where the damage ends: with liability after harm. Liability matters, but it is not enough on its own. When AI systems shape access to healthcare, liberty, employment, or essential public services, accountability is not only a governance preference but also a prerequisite for protecting rights and enabling effective remedy. Otherwise, people’s experience of AI systems ends up as denial without explanation, bias without recourse, and appeals processes that cannot surface what actually happened.

Why AI assurance will continue to fail unless we start to think holistically

Sam Farrow — Tue, 31 Mar 2026 10:30:00 GMT

AI Assurance can significantly benefit enterprises and society by helping to unlock AI's potential. However, current approaches often lack a holistic view, failing to develop justified trust.