AI Governance

When you need this

• Employees are already using AI faster than any policy allows.

• There are no clear rules for confidential data or third-party vendors.

• Existing governance is too vague or too heavy for people to use.

• Agents and automated workflows need defined permissions and accountability.

• You need controls in place before expanding AI across the organization.

What Robert does

• Governance and policy design tailored to how your teams actually work.

• Risk classification and data-boundary definition.

• Permissions and accountability models, including for AI agents.

• Vendor and model evaluation, plus education for the people affected.

What you leave with

Typical outputs, scoped to each engagement:

• A governance framework and acceptable-use policy people can follow.

• A risk model and vendor/model evaluation approach.

• A permissions and accountability model, plus clear data-handling rules.

• Human-oversight requirements, implementation controls and a rollout plan.

Approach

• Make governance usable, not just documented.

• Give agents and automations explicit permissions and boundaries.

• Match the weight of controls to the real level of risk.

• Treat education as part of governance, not an afterthought.

A good fit when

• You need controls people will actually follow, not a binder no one reads.

• You want governance that enables adoption instead of blocking it.

• Leadership will stand behind the rules and back the rollout.

Not the right fit when

• You want a policy template with no intent to operate it.

• The goal is a compliance checkbox rather than real accountability.

• No one is empowered to set data boundaries or enforce them.