AI Red Teaming

3 articles

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Attack

From Jailbreaks to Business Impact: How to Write AI Security Findings That Executives Understand

AI security findings should connect tested behavior to business impact through scope, preconditions, evidence, reproducibility, affected assets, control failure, severity rationale, and remediation. Findings must avoid unsupported company-level claims, product endorsement language, and exaggerated conclusions.

10 min read

Attack

Building an AI Red Team Lab: Tools, Datasets, Harnesses, Attack Libraries, and Reporting Templates

An AI red team lab should provide a controlled, authorized, reproducible environment for testing LLM applications, RAG systems, AI agents, model endpoints, tool use, output handling, and governance evidence. It must include safe datasets, attack libraries, test harnesses, telemetry, evidence handling, reporting templates, and operational guardrails.

10 min read

Attack

AI Evals as Security Tests: Building Regression Suites for Prompt Injection, Leakage, and Unsafe Actions

Security evals should test prompt injection, indirect injection, data leakage, RAG access, unsafe output, excessive agency, over-reliance, and cost abuse. These should be repeatable regression suites in CI/CD and governance evidence.

10 min read