What does enterprise AI governance control?

Enterprise AI governance turns decisions about AI use into permissions, operating rules, records and named responsibilities. For an agent that uses tools, it needs to cover more than which model employees may open.

Who may use an agent? Which tools and skills may it use? Where can its code run? What changes need approval? Who can review what happened? Each question needs both a technical control and a team that owns it.

Start with a task and its owner

Choose one workflow before granting broad access. Identify the people using the agent, the information it needs, the systems it may change and the person accountable for its results.

For an illustrative support workflow, reading a case, drafting a reply and changing the case status are distinct permissions. Decide which steps the agent may perform, which need review and which remain out of scope. A well-written prompt is useful, but it cannot replace those controls.

Manage agents, models and skills

FinHub gives the IT team a control center for agents: identity, tenant-aware access, model choices, templates, skills and tools. Administrators decide what people may use, and skills are checked before installation, with human review as part of the approval process.

FinHub runs on your servers or in your private cloud. It manages FinClaw and supports Hermes through an adapter; the platform is not tied to a single agent engine. Its platform-agent conversations are appended to a write-once record. That record does not imply that unrelated tools or every endpoint conversation are covered.

Separate execution limits from business decisions

FinSafe contains tools and code under file and network policies. FinGuard checks proposed changes routed to business systems, prevents duplicate actions and holds high-risk requests for approval.

These controls serve different purposes. A sandbox can block access to a destination; an action gateway can decide whether a specific permitted business change should proceed. Connect and configure the paths you intend to govern rather than assuming every action is covered automatically.

Own the records and the operating process

Agree who reviews conversation and action records, how long they are retained, and who may access them. Test denied operations, repeat requests, failed dependencies and approval paths. Re-test when tools, policies or models change, and assign owners for incidents and access revocation.

Self-hosting the platform does not by itself make model inference local. Your team still chooses the model endpoints, data sent to them and network controls. Technical controls support your governance process; they do not confer a certification or guarantee compliance.

For the evidence-review and reporting workflow, see Enterprise AI Governance & Evidence. Start with the controls the workflow needs and expand only with clear ownership and tested boundaries.

Explore FinHub, or talk to a Forward Deployed Engineer about a first governed workflow.