The thinking behind AI security review.
Threat models, governance evidence, and the decisions that hold up under scrutiny. Written for security architects and the committees they report to.
New posts in your inbox,
when they publish.
Threat modeling, governance evidence, and what AI Committees actually need — written for security architects and AI governance leads. No cadence promises.
Preserving evidence after an AI incident
AI incident evidence degrades in ways that traditional IT incident evidence does not. Model context windows are ephemeral, log retention is often incomplete, and model weights change. This piece defines the evidence-preservation steps to take immediately after an AI incident.
The anatomy of an AI evidence pack
An AI evidence pack is the complete set of artefacts a governance committee needs to make a defensible decision. This piece defines what goes into one — and why the order and labelling of the artefacts matters as much as the content.
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.
Building an AI management system (AIMS) from scratch
An AI management system is the governance infrastructure for AI: the policies, procedures, roles, and records that allow an organisation to make defensible AI decisions at scale. This piece defines what it takes to build one.
Vetting a third-party MCP server before you connect it
Every third-party MCP server connected to an agent extends that agent's attack surface. This piece defines the vetting process: source review, tool manifest audit, permission scope, and the evidence required for a security review.
PII leakage through RAG retrieval
RAG pipelines built over internal document corpora frequently contain personal data that was never intended to be queryable by the model. PII leakage through retrieval is the most common data-protection issue we encounter in RAG security reviews.
Security review for multi-agent systems
When agents orchestrate other agents, every trust assumption in the single-agent model multiplies. This piece defines the additional review surface for multi-agent systems: inter-agent trust, capability delegation, and blast-radius containment.
EU AI Act vs GDPR — where they overlap for AI systems
The EU AI Act and GDPR overlap significantly for AI systems that process personal data. This piece maps the overlap, explains where the obligations are additive rather than duplicative, and identifies the review artefacts that satisfy both.
LLM supply-chain risk — models, weights, and dependencies
LLM applications have a supply chain that extends to pre-trained models, fine-tuning datasets, inference providers, and plugin ecosystems. This piece maps the supply-chain attack surface and the review questions for each layer.
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.
Classifying AI incidents — a framework for security teams
AI incidents do not fit cleanly into standard incident classification schemes. A model behaving unexpectedly is not the same as a data breach, but it may become one. This piece defines an AI-specific incident classification framework.
The restricted-pilot pattern for risky AI systems
A restricted pilot is a formal disposition state: the AI system may operate within a defined scope, on the condition that named controls are in place and named triggers will initiate a re-review. This piece defines how to write a restricted-pilot disposition.
Free resources
Practical templates for every framework covered here.
AI Security Review Template
Full review pack with threat model, controls, and evidence grading.
OWASP Agentic Top 10 Controls
Each risk mapped to required controls and lifecycle gates.
AI Risk Disposition Memo
Clearance decision template with rationale and sign-off log.
AI Go-Live Security Checklist
Production gate checklist for security architects and CISOs.