Playbook

Designing a continuous AI red teaming program

How to move from point-in-time GenAI testing to ongoing validation and remediation.

Playbook9 min readCISO, Security Engineering

Why Continuous Testing

AI systems change frequently. Prompt templates, retrieval content, tools, models, and policies evolve, so a one-time assessment quickly becomes stale.

Test Coverage

Cover direct jailbreaks, indirect prompt injection, data leakage, unsafe actions, RAG poisoning, tool abuse, and policy bypass. Include business-specific scenarios that reflect real impact.

Risk Ranking

Prioritize findings by exploitability, data sensitivity, action severity, affected users, and compensating controls. This helps teams fix what matters first.

Retest Discipline

Every serious finding should have an owner, fix plan, SLA, and retest record. Continuous red teaming becomes a governance program when remediation is tracked.

More Resources

Continue exploring AI security thinking.

Guide

9 min read

The enterprise guide to AI agent runtime security

A practical security model for agents that retrieve context, reason over data, call tools, and act across business systems.

For CISO, Head of AI, Security Engineering

Executive Brief

8 min read

How CISOs can govern workforce AI without slowing adoption

Policy patterns for shadow AI discovery, prompt DLP, app governance, and employee enablement.

For CISO, CIO, Risk and Compliance

Research Note

10 min read

Red teaming RAG systems for poisoned context and over-disclosure

Testing approaches for retrieval abuse, source trust, sensitive context leakage, and model response drift.

For Security Engineering, AI Product Teams

Request a Demo

Secure the AI your enterprise runs on.

See how Kavalan helps security and AI teams govern workforce AI, protect agentic systems, and continuously validate GenAI risk.

Request a Demo Explore Platform