Enterprise AI Guardrails
to Secure Every AI Request

An AI governance platform gives an organization central control over how every AI and LLM request behaves in production — enforcing policies, guardrails, and access controls, and recording an audit trail of every decision. ai-enforce is an AI governance platform that sits in-line between your applications and every LLM provider, so security, compliance, and cost controls apply at runtime without changing your app code.

AI guardrails are runtime controls that inspect each prompt, tool call, and model response and decide whether to allow, redact, or block it. ai-enforce delivers enterprise AI guardrails and LLM guardrails that stop prompt injection, PII leaks, and secret exposure before they reach your model — rendering a verdict in under 10ms with a built-in regex floor plus your custom policies.

Every request is intercepted and evaluated against your policies using Open Policy Agent — injection patterns, PII detection, tool gates, and rate limits are checked in parallel. AI policy enforcement happens in-path, so a request is forwarded, redacted, or denied with a reason code before it ever reaches the LLM. This runtime governance means no request slips through unchecked.

AI agent governance controls what autonomous agents are allowed to do: which tools they can call, how many steps they can take, and what data they can access. ai-enforce enforces tool gating, step limits, and AI access control on every agent action, and logs each decision so agent behavior stays auditable and within policy.

An AI audit trail is an immutable, per-request record of every prompt, decision, and response. ai-enforce logs each verdict and streams it for forensics and full request replay, so you can prove GDPR, HIPAA, and SOC 2 compliance. That makes ai-enforce an AI compliance platform as well as a security platform — every AI interaction is monitored and attributable.

Yes. ai-enforce is an AI security platform and AI compliance platform in one: it blocks threats in real time, enforces responsible-AI policies, monitors AI usage and model behavior, and maintains complete audit trails for compliance monitoring. It supports AI risk management and enterprise AI security without adding meaningful latency.

ai-enforce is a drop-in, OpenAI-compatible gateway. Change one line — your base URL — and your existing SDK calls work unchanged across OpenAI, Anthropic, Google Gemini, Azure OpenAI, AWS Bedrock, Groq, and Ollama. The same guardrails, governance, and audit trail apply to every provider.

Policy evaluation is designed for production: the average decision latency is around 4.2ms and stays under 10ms, with throughput above 10,000 requests per second and a false-positive rate under 1%. AI runtime governance runs in-path without slowing down your AI features.