AI Observability
Monitoring how AI systems behave in production - traces, metrics, and logs of prompts, responses, and cost. Necessary, but not the same as enforcement.
AI observability is the practice of instrumenting AI systems so you can see what they are doing: which prompts were sent, what the model returned, how many tokens were spent, where latency went, and when behavior drifted. It is the AI-specific extension of application observability, and it is essential for debugging quality and cost.
Observability tells you what happened; it does not decide what is allowed to happen. A dashboard that reports a data leak after the fact is not a control. DataStrict is built around enforcement in the request path, with observability as a by-product: because every decision is adjudicated and written to the audit ledger, the record you query is the same one the policy acted on.
Govern AI like infrastructure.
Talk to our team about deploying DataStrict across your enterprise stack.