What is Runtime Enforcement?

Traditional AI governance relies on documentation, reviews, and after the fact audits. You write a policy, assess a model, file a report, and hope everything holds once the system goes live.

Agentic AI moves too fast for that. Agents reason, decide, and act in milliseconds. By the time a manual review catches a problem, the damage is already done.

Runtime enforcement closes this gap. On the Holistic AI Governance Platform, governance policies are not just documents. They translate directly into automated controls that evaluate AI behavior as it happens, at the point of execution, in real time.

How it works

Every governance policy you define on the platform becomes an enforceable rule. You build policies using the visual policy builder or start from regulatory templates preconfigured for the EU AI Act, NIST AI RMF, ISO 42001, industry specific standards, or your own custom principles.

Once active, these policies are evaluated against every relevant AI interaction as it occurs. The platform detects policy violations, prompt injection attempts, unsafe outputs, and anomalous behavior in sub second time. When risk is detected, the system acts instantly through Guardian Agents.

Sentinel Agents handle the detection side. They monitor, score risk, and surface compliance gaps without interrupting the workflow. When a threshold is crossed, Operative Agents step in and execute the enforcement action your policy defines.

What runtime enforcement can do

• Kill switches - Initiate emergency shutdown of an agent or workflow when critical thresholds are breached, gracefully terminate sessions, and roll back to approved safe versions. Immediate containment with no delay
• Automatic blocking - Filter risky requests, prevent prompt injection attempts, and block unauthorized access before unsafe interactions ever reach production
• Automated remediation - Revoke excessive privileges, fix misconfigurations, and quarantine non compliant agents. Contain, correct, and recover without manual intervention
• Response control - Validate AI outputs before they reach users through content filtering, PII detection and redaction, and output validation against policy
• Traffic rerouting - Redirect flagged requests to safer models or fallback paths when the primary agent produces a risky result
• Escalation - Route interactions to human reviewers when automated resolution is not appropriate, pausing the workflow until review is complete

Where it runs

Runtime enforcement works across your entire AI environment through a single control plane. The platform applies governance policies across AWS, Azure, GCP, and on prem environments with unified oversight. No gaps, no silos.

It is also vendor neutral. You can monitor and enforce governance on agents from OpenAI, Anthropic, open source models, and custom systems without being locked into any single vendor ecosystem. One layer of control across every AI platform.

Every action is logged

Every enforcement event creates a full audit record: which policy was triggered, what the input and output were, what action was taken, and what the outcome was. This produces a continuous compliance trail that maps directly to your regulatory obligations and is available for auditors, regulators, and internal review at any time.

If you want to know more about how we enforce governance policies in real time, get a demo now.