AI security topic
Production AI security testing
Updated · Class: Reviewed claim · Review: Reviewed by Dread team guidance · Maintainer: Dread Research · Contact [email protected]
Production AI security testing evaluates deployed AI behavior under realistic conditions: real integration boundaries, actual tools, retrieval paths, logging, monitoring, and rollback procedures.
Production workflow
Production testing should be staged and permissioned. It should avoid unauthorized data access, uncontrolled traffic generation, and tests that could trigger real-world side effects.
- Inventory model endpoints, agent tools, retrieval sources, and user roles
- Define safe test accounts, rate limits, and rollback triggers
- Run regression suites for known prompt-injection and leakage failures
- Store evidence in a report that engineering and risk owners can act on
Where automation helps
Automation helps with repeatability, regression testing, trace collection, and broad coverage across model versions. It does not replace human threat modeling or authorization decisions.
Current public evidence
DREAD.TECHNOLOGY currently exposes methodology pages and research-oriented content. Public evidence should be expanded with sanitized reports, reproducible examples, and accessible HTML summaries as they become available.