Controls & enablement
Make AI useful, visible, and accountable.
AI adoption creates new questions: what can employees use, what can agents access, what requires approval, how do you detect errors, and who responds when something goes wrong? Valentino AI turns those questions into practical operating controls.
Governance should help people work safely
Governance is not only a policy document. It is the combination of rules, permissions, review points, evaluation, monitoring, training, and incident response that keeps AI use aligned with the business.
- Acceptable-use guidance
- Use-case registration and ownership
- Risk-tiering and approval requirements
- Vendor and model review
- Data classification and access controls
- Human-in-the-loop design
- Agent evaluation and testing
- Incident handling and recurring review
A practical risk-tier model
Different uses need different controls.
- Low risk: drafting, brainstorming, summarization, and internal assistance with approved information.
- Moderate risk: classification, routing, recommendations, and draft communications that receive human review.
- Higher risk: external communication, system writes, customer-impacting decisions, or actions with financial, legal, operational, or reputational consequences.
- Prohibited or restricted: uses involving sensitive data, unauthorized surveillance, unsafe advice, or actions outside the organization’s authority.
Observability makes performance visible
A system cannot be governed if the business cannot see what it is doing.
- Inputs, outputs, tool calls, and workflow status
- Human approvals, transfers, and overrides
- Quality scores and evaluation results
- Error, escalation, and exception patterns
- Usage, latency, and cost signals
- Unanswered questions and knowledge gaps
- Changes to prompts, instructions, permissions, and data
Training turns controls into behavior
Employees need clear guidance on what to use, when to verify, how to protect information, and how to report a problem.
Positioning
What we deliver
The engagement can be a standalone governance workstream or a standard part of another AI implementation.
- AI use-case inventory and ownership model
- Policy and acceptable-use guidance
- Risk-tier and human-approval matrix
- Vendor and tool review checklist
- Agent evaluation and testing approach
- Observability and incident-response design
- Role-based employee training
- Recurring governance review cadence
Our process
Controls should evolve with the systems they govern.
- Inventory current and planned AI uses.
- Classify risk, data, users, and actions.
- Define approvals, permissions, monitoring, and escalation.
- Create evaluation cases and review standards.
- Train the people responsible for using and supervising the system.
- Review incidents, metrics, and new use cases on a recurring cadence.
AI Governance & Training
Make AI useful, visible, and accountable.