Clearance vs approval — why the distinction matters for AI governance
Most organisations conflate security clearance with business approval for AI systems. The distinction matters: clearance is a security gate, approval is a business decision. Conflating them produces systems that are approved but not cleared — or cleared but not governed.
What an AI Risk Disposition actually contains
AI Committees keep approving systems they can't defend later. The Risk Disposition memo is the artifact that fixes this — here is what goes into one, section by section, with examples from a real assessed system.
Five mistakes that make an AI security review undefensible
Most AI security reviews fail not because they miss threats, but because they miss the structure that makes a decision defensible. These five mistakes appear in almost every review we have examined.
A lightweight AI security review for fast-moving teams
Large-enterprise review processes do not scale to a 10-person team shipping an AI feature next sprint. This piece defines the minimum-viable AI security review: three questions, three artefacts, one decision record.
What makes an AI decision record defensible
A defensible AI decision record is one that a regulator, auditor, or procurement officer can read — without access to the people who made the decision — and understand what was decided, why, and what commitments were made. This piece defines the standard.
Why SOC 2 is not AI assurance
SOC 2 tells you that a vendor's infrastructure and processes meet a defined set of trust service criteria. It does not tell you how the vendor's model behaves, what data it was trained on, or how it handles edge cases. AI assurance requires different evidence.
Scoping an AI security review without boiling the ocean
The most common failure mode in AI security reviews is scope so wide nothing gets finished. This piece walks through how to scope a review to the decision you actually need to make: the system, the deployment context, and the threshold.
AI security review vs penetration testing — different questions
A penetration test asks: can this system be exploited? An AI security review asks: should this system go to production, and under what conditions? The questions are related but not the same. Running only a pentest leaves most AI risk unaddressed.
When to run an AI security review — the four trigger points
Not every change to an AI system warrants a full review, but some changes that seem minor do. This piece defines the four trigger points that should initiate a review: initial deployment, model change, scope expansion, and incident.
What an AI security review actually is (and what it is not)
AI security review is not a pentest, not a compliance audit, and not continuous monitoring. This piece defines what it is — a design-time assessment that produces a defensible record of how an AI system was evaluated, what risks were identified, and what controls were required.