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.
LLM output validation — the controls that actually work
Prompt injection and hallucination are symptoms of missing output validation. The controls that close the gap at each gate.
What goes in an AI risk register — and what does not
What belongs in an AI risk register: five required fields, common items that do not belong, and how to avoid a generic IT risk list with 'AI' added.
Threat modeling a RAG pipeline — retrieval, context, and generation risks
RAG introduces three attack surfaces standard LLM threat models miss: retrieval boundary, context window, generation gate.
DPAs and AI systems — what DPOs actually need to document
Data Protection Authorities are asking about AI in DPA reviews. What DPOs must document and the gaps regulators find first.
Attack path analysis for AI systems — beyond CVE scoring
CVE scores show a vulnerability exists. Attack path analysis shows if it is reachable and connected to meaningful blast radius.
AI incident response — what the playbook needs that IT playbooks miss
AI incidents have non-deterministic reproduction, model-level root cause, and degrading evidence. What the playbook must add.
Clearance vs approval — why the distinction matters for AI governance
Security clearance and business approval are separate gates. Conflating them produces systems that are approved but not cleared.
Is your AI system high-risk under the EU AI Act? How to find out
Determine whether your AI system is high-risk under the EU AI Act's Annex III categories and what that classification requires.
An AI Risk Disposition that holds up in regulator review
Most risk dispositions are written for internal approval. What must change so they survive external regulator scrutiny.
Security review for fine-tuned models — what changes from base model assessment
Fine-tuned models inherit base-model risk and add training data provenance, alignment drift, and capability overhang concerns.
Why your existing threat modeling tool doesn't model agents
STRIDE and commercial tools model deterministic software. Agents have five properties they can't represent -- each one an attack surface.
Assessing third-party AI vendors — the questions procurement skips
AI vendor risk assessment beyond SOC 2: model governance, incident notification, and the evidence to re-assess when vendors change models.
Free resources
Practical templates for every framework covered here.
AI Security Review Template
Full evidence pack with threat model, controls, and evidence grading.
OWASP Agentic Top 10 Controls
Each risk mapped to required controls and lifecycle gates.
Decision Memo
Clearance decision template with rationale and sign-off log.
AI Go-Live Security Checklist
Production gate checklist for security architects and CISOs.