Inline runtime enforcement
Real-Time Guardrails
Stop unsafe prompts and unsafe model output in the same request path before they become incidents.
What it does
Real-Time Guardrails place UMAI directly between the calling system and the model runtime.
- Evaluate prompts before the model sees adversarial, out-of-scope, or sensitive input.
- Inspect responses before users, agents, or downstream systems consume model output.
- Apply deterministic and context-aware policies in the same control point.
Why it matters
Enterprise AI failures usually happen in live traffic, not in static policy documents.
- Reduce exposure to jailbreaks, prompt injection, and unsafe instructions at runtime.
- Prevent accidental disclosure of regulated or confidential data before it leaves the system.
- Give security and platform teams one enforcement point instead of fragmented controls.
Key capabilities
The control layer is built for low-latency enforcement without forcing application rewrites.
- Heuristic and context-aware evaluations for PRE_LLM and POST_LLM phases.
- Policy actions such as allow, warn, redact, block, and escalate.
- Versioned guardrails with draft, publish, and rollback workflows.
- Inline policy telemetry written to the UMAI evidence trail by default.
Example enterprise use cases
Teams use Real-Time Guardrails where model behavior must stay inside clear operational boundaries.
- Customer support assistants that must avoid account leakage and prohibited advice.
- Internal copilots that need role-aware controls for source access and output exposure.
- Workflow automation that must detect prompt injection before actions are triggered.
Related compliance or governance value
Runtime evidence is what turns policy intent into defensible control coverage.
- Maps guardrail decisions to EU AI Act, GDPR, KVKK, and internal control requirements.
- Supports monitor-first rollouts before hard enforcement for policy tuning.
- Provides structured records that can be reviewed by compliance, legal, and audit teams.
Related integrations
The runtime path is designed to sit in front of common enterprise AI stacks.
- LLM endpoints such as OpenAI, Azure OpenAI, Anthropic, and Vertex AI.
- Application backends, agent runtimes, and service APIs.
- Downstream SIEM, data warehouse, and governance reporting systems.
CTA section
Move from policy intent to governed rollout.
Start with one critical AI flow and move from monitor mode to enforce mode with evidence.
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