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.
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Threat modeling, governance evidence, and what AI Committees actually need — written for security architects and AI governance leads. No cadence promises.
Data poisoning in RAG knowledge bases
A RAG knowledge base is only as trustworthy as the documents in it. Data poisoning attacks insert malicious content into the knowledge base — not to corrupt the index, but to influence model outputs when those documents are retrieved.
Building an EU AI Act system inventory
The EU AI Act requires organisations to know which AI systems they deploy and which tier each one falls into. Building that inventory is harder than it sounds when AI is embedded in SaaS, vendor products, and internal tooling.
Mapping the agentic AI attack surface
The agentic AI attack surface has five distinct layers: the prompt channel, the tool surface, the memory layer, the orchestration boundary, and the output channel. This piece maps each layer with its associated threats and controls.
Prompt injection, explained for security reviewers
Prompt injection is the most widely discussed LLM attack and the most widely misunderstood. This piece cuts through the confusion: what it is, what its variants are, how it differs from SQL injection, and what controls actually reduce the risk.
The AI security review checklist, by lifecycle gate
A checklist that follows the lifecycle of an AI system — intake, architecture, threat model, control plan, disposition, pilot, production. Each gate has different review questions and different evidence requirements.
The NIST AI RMF, explained for practitioners
The NIST AI Risk Management Framework gives organisations a structure for managing AI risk across four functions: Govern, Map, Measure, and Manage. This piece explains each function in terms a security practitioner can act on.
The AI section your vendor security questionnaire is missing
Standard vendor security questionnaires cover data processing agreements, SOC 2, and encryption. They do not cover model governance, re-assessment triggers, or incident notification for model updates. This piece fills the gap.
Writing an AI governance committee charter
An AI governance committee without a charter is a meeting with a name. The charter defines the committee's authority, composition, quorum, decision types, and escalation paths. This piece defines what a working charter requires.
MCP security — the four attack surfaces of a Model Context Protocol server
MCP servers extend a model's capabilities by exposing tools, resources, and prompts. Each extension point is an attack surface. This piece defines the four surfaces and the review questions for each.
ISO 42001, explained for security teams
ISO/IEC 42001 is the international standard for AI management systems. This piece explains what it requires, how it differs from ISO 27001, and what a security team needs to know to support an AIMS implementation or certification.
RAG security — the three boundaries that matter
Retrieval-augmented generation adds a retrieval layer between the user and the model. That layer has three security boundaries — the data boundary, the retrieval boundary, and the context boundary — and each has distinct failure modes.
EU AI Act risk tiers, explained for engineers
The EU AI Act classifies AI systems into four risk tiers: unacceptable, high, limited, and minimal. The classification determines the obligations. This piece explains how to classify a system and what each tier requires.
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.