WitnessAI

Key takeaways Context-aware detection decides what AI data masking catches. AI data masking identifies sensitive information in AI interactions and substitutes or obscures it at appropriate control points. Depending on the deployment, these controls can apply to prompts, responses, retrieved content, and agent workflows. Confidential business details such as de...

Key takeaways Govern, Map, Measure, and Manage turn AI risk management into an ongoing process rather than a checklist. Govern provides accountability throughout; the other functions establish context, evaluate risks, and guide responses. Reliable AI inventories are essential to putting the framework into practice. Shadow AI and unknown agents leave gaps in risk mapping, ...

Key takeaways The OWASP AI Testing Guide turns AI risk categories into 32 repeatable tests across applications, models, infrastructure and data. Use it alongside the OWASP Top 10 to check whether controls withstand the risks that taxonomy identifies. Launch approval is not lasting assurance, so repeat tests after material changes and throughout production. Shadow AI disco...

Key takeaways Many-shot jailbreaking steers models toward harmful answers through repetition: hundreds of fabricated examples establish a pattern inside one prompt. Unlike multi-turn attacks, it needs no back-and-forth escalation. Where supported, agent activity should be attributed to the initiating user or service identity and governed through policies ...