The short answer
An AI governance layer connects policy requirements to access controls, action checks, human approvals, evaluation, and records for the AI systems and workflows in scope.
Enterprise AI governance
AI governance layer for enterprise workflows
Connect policy to the system’s permissions, review steps, records, and operating owners.
Cortez Lake
Translate AI policy into defined access, action controls, review responsibilities, and evidence. Establish what the governance layer covers and how the team will maintain it.
What this means in practice
Governance includes technical controls and people responsible for their use. Define where each requirement is enforced, what evidence it produces, and who reviews exceptions and changes.
Bring H2H in when…
- AI systems use sensitive data or take actions in business workflows.
- Written policies have not been connected to specific system controls.
- Security and operations need agreed evidence and ownership for ongoing review.
Related H2H pages
AI governance and agentic securityScope policy, approvals, evaluation, and logging for an AI workflow.AI agent approval gatesDesign and test the decision required before a consequential action.Why frontier-model investments need an operating layerConnect governance work to the investment and operating plan.
Begin with the systems and actions in scope
Create an inventory of the workflows, models, tools, data sources, and users the governance work covers. Identify the actions each system can take and the consequences that require control. This gives policy a concrete place to apply.
For each requirement, specify the system that enforces it and the person responsible for its operation. Some controls belong in identity or application permissions, some at an agent tool call, and others in human review or release procedures.
Connect controls to evidence
A useful control can be described and tested. Document what it permits, when it requests review, and what it denies. Then identify the record needed to establish whether it operated as intended in the cases evaluated.
- Access rules for users, agents, data, and tools.
- Policy checks and review paths for consequential actions.
- Evaluation cases covering allowed, denied, and failed actions.
- Agreed records, retention, and access for investigation and review.
Assign ownership after the initial release
Policies, integrations, and model behavior can change. Name who evaluates changes, approves exceptions, reviews incidents, and decides when a workflow needs to be paused or reassessed. Include the people who operate the workflow in that process.
Set a review cadence suited to the system and trigger additional review when material changes occur. Logging is only useful if someone can interpret the records and act on findings. Keep sensitive information in those records subject to appropriate access and retention controls.
Use Tutela where its controls fit
Tutela provides policy enforcement and decision records for supported agent actions in connected systems. It can form part of a governance implementation alongside the existing identity, application, and operating controls.
H2H helps define the workflow requirements and assess product fit. Document what Tutela covers and what remains with other systems or people. No single control layer substitutes for the full set of responsibilities, and an audit log alone does not establish compliance.
Next step
Make your AI policy actionable.
H2H helps connect policy requirements to controls, reviewers, and evidence for the workflows your team needs to govern.