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Ask ten organizations whether they “red team” their AI systems and most will say yes. Ask what that actually involved, and the answers scatter — from a genuine, documented adversarial engagement to an afternoon of a smart engineer typing jailbreak attempts into a chat window and pronouncing the result “tested.”
The gap between those two things is the subject of our newest practitioner cheat sheet, AI Red Teaming Methodology(ODA3-2026-07-CHT-SEC-003), now available as a free download.
The Core Idea
AI red teaming is worth doing only if it produces something an assessor can trust and a second team can reproduce. Its value isn’t in any single clever attack — it’s in the process discipline around the attack: who authorized it, what was in and out of scope, how findings were scored, and whether the fix was actually confirmed. A report full of impressive-looking exploits with no severity ratings, no version pinning, no reproducibility, and no control mapping is a demonstration, not an assurance artifact.
This is a methodology reference, deliberately not an attack cookbook. It contains no working jailbreak strings or exploit recipes — those belong inside an authorized engagement, not a public document. What it gives you is the operational spine of a defensible program.
What’s Inside
The cheat sheet is a single, full-depth reference covering:
- The seven-stage engagement lifecycle — from Rules of Engagement and authorization, through threat modeling, execution, severity scoring, and reporting, to remediation retest. The order isn’t optional: authorization and safety handling come before any testing
- How red teaming differs from penetration testing and model evaluation — worst-case adversarial behavior versus known-vulnerability testing versus average-case quality — and why a mature program runs all three
- A framework-anchored threat model, mapping scope to the NIST AI 600-1 twelve risk categories and the OWASP LLM and Agentic Top 10, so coverage is defensible and findings are portable between teams
- Manual versus automated testing — where each wins, and how NIST’s Human/AI approach combines them — with named open-source tooling as examples
- Agentic and tool-using system testing, team composition, purple teaming, and scoping-and-sizing guidance
- A thirteen-item controls-and-practices matrix, a maturity model with a practical Crawl → Walk → Run adoption path, and an assessor’s lens covering verification versus validation, evidence sufficiency, and residual risk
Every factual claim carries an inline evidence tier tag ([T1]–[T4]), anchored to verified sources — the OWASP GenAI Red Teaming Guide’s four-area structure and NIST AI 600-1’s risk categories and red-teaming approaches. And, as always, the document states plainly what current evidence does not support: there is no universally accepted quantitative red teaming benchmark, no ratified competency certification, and no settled way to measure the gap between tested worst-case and true worst-case behavior. Guidance that hides its own limits isn’t guidance.
Safety Comes First
One principle runs through the entire document: no engagement begins without documented authorization, agentic testing runs in isolated sandboxes with no path to real-world action, and sensitive-capability findings are handled under strict disclosure controls. Red teaming is adversarial by design — the discipline that makes it safe is as important as the discipline that makes it rigorous.
Who It’s For
Written primarily for Security Architects, CISOs, AI Governance Leads, and Compliance Officers, with leadership-oriented questions called out throughout so the same document informs a risk-committee conversation.
This is the third entry in our practitioner cheat sheet series, following Prompt Injection Mitigation Controls and AI Monitoring Architecture. All three map to GAISSF™ v1.0 and UAIF™ v1.0 under the GAISSF Ecosystem License, and are reviewed quarterly.