Enforce

Runtime guards that block bad outputs before they reach users

Drift detection. Policy controls. Permit/deny/escalate decisions on every request. A Rust sidecar with SLM evaluation that sits between your AI systems and the real world.

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How it works

Every request gets a verdict. Every verdict is logged.

Enforce deploys as a lightweight Rust sidecar next to your AI systems. It evaluates every request and response using configurable policies and a local SLM evaluator—then permits, denies, or escalates. No data leaves your environment.

Not a filter. A control plane.

Capabilities

Built for AI fleet operations

Rust Sidecar

Sub-millisecond overhead. Single binary, no runtime dependencies. Deploys anywhere containers run.

SLM Evaluation

A local small language model scores every request for policy compliance—no data leaves your perimeter.

Drift Detection

Continuous monitoring of model behavior against your baseline. Alerts when outputs shift outside policy bounds.

Shadow Mode

Observe and log without blocking. Deploy Enforce in shadow mode first, then flip to active enforcement when you’re ready.

Fleet Dashboard

See every AI system in your organization. Policy status, violation rates, drift trends—one view.

Policy-as-Code

Define policies in YAML. Version them in Git. Roll out across your fleet with CI/CD integration.

Permit / Deny / Escalate

Three-outcome verdicts on every request. Clean outputs pass. Violations block. Edge cases route to human review.

Immutable Audit Trail

Every verdict, every policy evaluation, every escalation—logged with timestamps and evidence hashes.

Who this is for

Your AI fleet needs a control plane

Teams running multiple AI systems that need consistent policy enforcement
Organizations deploying AI agents that interact with customers or make decisions
Companies needing to prove controls ran for regulatory compliance
Anyone shipping AI who needs to sleep at night knowing bad outputs won’t reach users

Ready to start

Plans for every AI fleet size.

Pricing scales with request volume. See all tiers →

Deploy in minutes. Shadow mode first, active enforcement when ready.

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FAQ

Common questions

What’s the difference between Enforce and a content filter?

Content filters are keyword blocklists. Enforce uses a local SLM to evaluate policy compliance in context, supports three-outcome verdicts (permit/deny/escalate), and generates an immutable audit trail. It’s a control plane, not a regex.

How does shadow mode work?

In shadow mode, Enforce evaluates every request but doesn’t block anything. You see what would have been denied without affecting production traffic. Flip to active enforcement when your policies are tuned.

What’s the performance overhead?

The Rust sidecar adds sub-millisecond latency for rule-based policies. SLM evaluation adds single-digit milliseconds. Both are negligible compared to typical LLM inference times.

Can I use Enforce without Notary?

Yes. Enforce works standalone for runtime policy enforcement. Add Notary when you need cryptographic proof that controls ran for audit or regulatory purposes.

What models does it work with?

Any model behind an HTTP API—OpenAI, Anthropic, Google Gemini, Azure OpenAI, and open-source models. Enforce is model-agnostic by design.

Also from GLACIS

Enforcement is one layer. Here’s the rest of the stack.

autoredteam

Know what your AI is doing

Behavioral assessment in minutes. Toxicity, hallucination, jailbreak resistance, PII leakage, prompt injection. Free, open source.

Try autoredteam

Notary

Cryptographic proof your controls ran

OVERT-format attestation receipts. Tamper-evident, independently verifiable, zero-egress. Every decision on GLACIS is witnessed and receipted — by default, not by upgrade.

Learn about Notary