AI evaluation and governance
5 guides tagged AI evaluation and governance, grouped by topic, with the SDLC phase and role each is written for.
AI security
5 guidesBuild evaluations around prohibited outcomes
Test the application’s actual security boundaries, preserve legitimate tasks and distinguish deterministic enforcement from variable model behavior.
Design, Testing, Security program · Developers, AppSec engineers, Platform engineers and SREs
Red-team LLM applications and agents by their effects
A red-teaming method for LLM features and agents: scope the whole system, map attacks to OWASP and MITRE ATLAS identifiers, score trials from observed effects, report rates with confidence intervals and retest on every model or provider change.
Design, Testing, Security program · Developers, AppSec engineers, Platform engineers and SREs
Protect training and retrieval data from targeted poisoning
Track who can influence datasets, keep evaluation independent and investigate targeted behavior changes without relying on aggregate accuracy.
Development, Testing, Release, Security program · Developers, AppSec engineers, Platform engineers and SREs
Turn NIST AI RMF, ISO/IEC 42001 and EU AI Act duties into engineering evidence
What the NIST AI RMF and its Generative AI Profile, ISO/IEC 42001 and the EU AI Act, as amended in 2026, ask of teams that build AI features, turned into engineering practices and one set of evidence. Not legal advice.
Planning, Testing, Security program · Developers, AppSec engineers, Platform engineers and SREs, Security leaders
Contain LLM misinformation (OWASP LLM07:2026)
Stop plausible but wrong model output from driving decisions and actions: ground answers in sources, verify every citation, separate generation from execution with a claim-check-act pattern, gate consequential actions on a human, and evaluate factuality on your own domain.
Design, Development, Testing, Security program · AppSec engineers