# Prove every AI system is governed, not just documented.

> One system of record for every AI system, with policy continuously enforced and evidence automatically generated across its lifecycle.

## AI oversight is no longer optional.

The EU AI Act, emerging US state laws, and sector-specific requirements are turning AI governance into an operational obligation. Organizations must be able to identify which systems are in use, assess their risk, enforce the appropriate controls, and produce evidence when asked.

Frameworks including NIST AI RMF and ISO/IEC 42001 raise the standard further: governance must remain connected to how AI systems are actually built, changed, and used.

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


## By the numbers

| Stat | Meaning |
| --- | --- |
| 100% | of AI systems continuously monitored |
| 55% | of compliance requirements unlocked in under one week |
| 100% | of policy violations caught before deployment |


## 60% of enterprise AI risk is currently unknown.

AI has outgrown the systems meant to govern it. New models, vendors, and use cases reach production faster than review processes can adapt, while responsibility is split across engineering, security, legal, and compliance.

The result is a widening gap between what organizations believe they control and what they can actually prove. Risk stays hidden until a system fails, a regulator asks, or a customer raises the alarm.

### No complete view of enterprise AI.

New models, agents, applications, and vendor tools appear faster than governance teams can identify and assess them.

### Evidence scattered across the organization.

Approvals, test results, risk assessments, and ownership records live across spreadsheets, tickets, documents, and disconnected systems.

### Controls are documented, not continuously verified.

Traditional GRC tools document controls but cannot continuously verify whether an AI system meets them in development or production.


## The governance layer your GRC stack is missing.

Openlayer is the only platform that runs your controls against live AI systems and generates audit-ready evidence as a byproduct.

With leading frameworks already mapped, teams can identify coverage gaps, apply the right controls, and begin governing AI in days—not months.


## Everything between intake and audit.

### Centralized AI system inventory

Every system, built or bought, in one registry with owner, use case, and risk level.

### Multi-dimensional risk scoring

Score at intake against the frameworks you follow; risk drives the oversight required.

### Approval workflows and audit trail

Every decision time-stamped, attributable, and tied to evidence.

### Structured intake
workflows

New AI enters through a consistent review, not a Slack thread.

### Lifecycle stage management

Development, evaluation, and production, each with its own requirements and sign-offs.

### Out-of-the-box compliance mappings

Pre-built mappings for 8 frameworks like the EU AI Act, ISO 42001, NIST AI RMF, OSFI E-23, and state-level regulations.


## Proof

> Trusted by regulated leaders: Sun Life and Gallagher (insurance); Rogers, KPN, and Comcast (telecom and media).

> Jericho Security: 6x deployment frequency and +53% throughput after standardizing on Openlayer.

> Backed by Y Combinator and Race Capital. SOC 2 Type II.
> 
> Founded by ex-Apple/Siri ML engineers.

> Named in the 2026 Gartner Market Guide for AI Evaluation and Observability Platforms.

> Endorsed by Guillermo Rauch (Vercel CEO) and Max Mullen (Instacart founder).


## Walk into your next audit already prepared.