ISO 42001 and AI security: where pentesting fits

ISO 42001 focuses on AI management systems, not just technical vulnerabilities. But technical testing still matters because governance claims are stronger when the organization can show how AI systems behave under adversarial conditions.

For LLM applications, relevant tests include prompt injection, indirect prompt injection, sensitive data disclosure, insecure output handling, unsafe tool use, retrieval leakage, model overreliance, and logging of sensitive context. These are not fully covered by classic web application testing.

For AI agents, the highest-risk areas are tool permissions, approval gates, tenant boundaries, workflow side effects, and auditability. If an agent can take action in another system, the security review should test both the model behavior and the application authorization layer.

For RAG systems, evidence should cover retrieval permissions, data source trust, indexing rules, poisoning resistance, citation integrity, and output handling. A governance policy that says data access is restricted is less convincing if retrieval testing has never challenged that boundary.

Pentest evidence can support AI governance by showing what was tested, what failed, what was remediated, and what residual risks remain. It can also help product teams prioritize technical controls instead of treating AI safety as a policy-only exercise.

DeepScan AI pentesting is designed to produce technical evidence that security, compliance, and AI governance teams can use together. The output is not a replacement for a management system, but it is a strong input to one.

As AI standards mature, teams that can show reproducible testing evidence will be in a better position than teams relying only on acceptable-use policies and model provider documentation.