AI
policy enforcement
PromptGlass turns your AI acceptable use policy into rules that run in real time. Each rule matches on app, identity group, data class and model, and takes an action: allow, log, coach, redact, block or reroute to an approved model. Policies are YAML, versioned and testable.
Outcomes
- ✓A policy people actually follow
- ✓Predictable rollouts via simulation
- ✓An audit trail for every decision
What you get
Inside PromptGlass ai policy enforcement
Six actions
Allow, log, coach, redact, block, or route to an approved model, combinable per rule.
Policy as code
YAML policies in Git, tested with promptglass policy test, deployed with GitOps or the console.
Fail-open or fail-closed
Choose per policy how the gateway behaves if inspection is unavailable.
Exceptions with expiry
Time-boxed exceptions for a person, group or project, with justification recorded.
Rollout
Four steps, no big bang
Start in observe-only mode and tighten controls as you learn what your organization needs.
- 1
Translate
Start from templates that map common AI acceptable use policies to rules.
- 2
Simulate
Replay last month’s traffic to see what each rule would have done.
- 3
Roll out
Enforce by group, starting with log and coach.
- 4
Audit
Every decision is logged with the rule version that made it.
AI policy enforcement: FAQ
Can we test a policy before enforcing it?
+
Yes. Simulation replays historic traffic against a draft policy and shows what it would have allowed, coached, redacted or blocked.
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