How Security Teams Can Evaluate AI Vendors for Safe Adoption
Summary
Darktrace outlines five areas for evaluating AI vendors: governance and data controls, training data, model choice, performance validation, and interpretability. The guide highlights buyer concerns about trust and autonomous AI actions in security workflows.
Key points
- The guide organizes AI vendor evaluation around governance and safety, data and training, model choice, performance validation, and interpretability and transparency.
- It recommends asking vendors about governance frameworks, bias mitigation, AI techniques, model testing, and how they build trust in AI outputs.
- Darktrace cites survey findings that 92% of respondents need to understand defensive AI decisions before trusting them, while only 14% allow AI to act autonomously.
- The article says performance testing should occur before and after release and account for issues such as model drift, latency, and resource use.
- It warns that poor explainability and hallucinations can undermine trust in AI systems used for cybersecurity.
Article Details
- Topic
- Framework for evaluating AI vendors and adopting AI securely in cybersecurity