# Stop unsafe AI outputs in real time, before it reaches a user.

> Block prompt injection, jailbreaks, sensitive-data exposure, and harmful content at the moment they occur. Every policy decision is automatically preserved as evidence.

## AI risk happens at runtime. Your controls should too.

Every production AI system can encounter malicious prompts, sensitive data, unsafe tool requests, and harmful outputs. Static reviews and pre-launch testing cannot anticipate every interaction once the system reaches real users.

Organizations need to show which controls were active, how each interaction was evaluated, and what happened when a violation occurred. That evidence must come from the system enforcing the policy, not from documentation assembled afterward.

- EU AI Act
- ISO/IEC 42001
- NIST AI RMF
- OSFI E-23


## By the numbers

| Stat | Meaning |
| --- | --- |
| <10mins | to deploy a new runtime guardrail |
| 50+ | PII and PHI entity types detected and blocked inline |
| 100% | of policy violations blocked before the output reaches a user |


## Documenting a policy doesn't enforce it.

GRC tools document what an AI system is allowed to do, but they do not apply those rules to live inputs, outputs, and agent actions. Traditional security tools can monitor infrastructure, network activity, and sensitive data without evaluating the full context of an AI interaction.

Without runtime guardrails, prompt injection, data exposure, harmful responses, and unauthorized tool use may reach a model or user before security teams can intervene.

### No control at the moment of interaction.

Detection often occurs after the request has reached the model or the response has reached the user. Nothing is positioned to inspect and stop the violation inline.

### Traditional security controls were not designed for AI behavior.

Infrastructure and data-security tools can detect known threats without fully evaluating prompt injection, jailbreaks, agent actions, model responses, and conversational context.

### Protection is inconsistent across AI systems.

A guardrail configured for one application does not protect every model, agent, or provider. Without shared controls, coverage depends on each team remembering to build and maintain its own safeguards.


## Enforce policy, not just document it.

Openlayer applies governance policies directly to live AI interactions. Violations are blocked, redacted, escalated, or routed for review, with every decision recorded as evidence.

Guardrails use the same test definitions as evaluation and monitoring. What you test is what you enforce, and evidence is generated automatically.


## Real-time protection for every AI interaction

### Inline PII/PHI blocking

Detect and block sensitive data in prompts and responses before it reaches a model, external provider, application, or user.

### Prompt injection defense

Identify and stop prompt-injection and jailbreak attempts as requests flow through.

### Harmful and toxic output filtering

Block toxic, harmful, and malicious content inline based on configurable policy.

### Audit evidence by default

Record every policy evaluation, enforcement action, exception, and outcome with a timestamp, system version, owner, and supporting trace.


## Put enforcement between your AI and your users.