Why most evaluations fail
Most AI governance RFPs focus on features that look good in a demo — dashboards, policy templates, integrations lists. They miss the three capabilities that determine real-world effectiveness: inspection latency, detection accuracy, and audit evidence quality. Optimize for those and the rest follows.
The six evaluation dimensions
Inspection coverage (which AI tools and surfaces does it reach), detection accuracy (false positive and false negative rates on your data), latency impact (milliseconds added to every interaction), audit evidence quality (can compliance actually use the logs), integration depth (SIEM, IdP, ticketing), and total cost of ownership including ongoing tuning.
The three solution categories
Browser-based proxies intercept traffic at the endpoint — easy to deploy, limited to browser sessions. API gateways sit between your infrastructure and LLM providers — deep coverage for engineering teams, blind spots for consumer tools. Inline policy engines cover all surfaces and add per-prompt controls — highest capability, most complex deployment.
Questions every vendor must answer
What is your p99 inspection latency in production? What is your false positive rate on legal and financial documents? How do you handle multi-turn context for DLP decisions? What is the evidence format your audit logs produce? And who is responsible for tuning detection models when accuracy degrades?
What a good proof of concept looks like
Run the POC on your own data, not vendor-provided samples. Test with your five highest-risk data categories. Measure latency against your SLA requirements. Have your compliance team evaluate the audit log quality. And test incident response — if detection fires, how fast can you investigate and contain?