Enterprise teams often ask for an “AI pen test” when what they need is a structured AI security assessment. The difference matters: AI systems combine classic application risk with model, prompt, retrieval, and tool-use failure modes.
Start with the real footprint
Map where AI already touches production:
– Customer-facing assistants and copilots – Internal tools with access to tickets, code, or documents – Vendor models embedded in SaaS you already bought – Agents that can call tools, APIs, or browsers
If you cannot draw that map in one page, the assessment should start there.
Core assessment areas
1) Threat model for the AI path
Identify assets (data, credentials, model outputs), actors, and abuse cases unique to LLM features — not only OWASP API risks.
2) Prompt and input abuse
Test for prompt injection, instruction override, and jailbreak patterns that change system behavior or policy.
3) Data leakage and retrieval risk
Check whether retrieval, logs, or tool responses can expose secrets, PII, or other tenants’ data.
4) Tool and agent controls
If the system can act (send email, query databases, change tickets), verify authorization, confirmation, and blast-radius limits.
5) SDLC and vendor controls
Review how prompts, models, and vendors enter production — and what evidence you keep for audits.
What good deliverables look like
Leadership needs residual risk in plain language. Engineers need reproducible findings. A useful report includes both: executive summary, severity-ranked issues, evidence, and a remediation roadmap with owners.
Next step
If AI is already customer-facing or connected to sensitive systems, schedule a focused briefing before expanding features.