AI Governance Consulting

Governance begins with an inventory of the tools and use cases in scope. Paloren identifies who uses them, what information they handle, which actions they support and who owns the outcome. The inventory includes informal practices that affect the business, while discovery keeps a clear purpose and appropriate handling. We distinguish an employee drafting internal text from a system changing customer records because different consequences require different controls. We reference the frameworks that apply: ISO 42001 for AI management systems, the NIST AI Risk Management Framework for governance scope, and the EU AI Act for high-risk classifications where applicable. The result is a usable picture of responsibility and exposure mapped to your regulatory context.

• Tool and use-case inventory
• Data and action classification
• Owner recorded for each material use case

A governance policy needs examples that help staff decide what to do under ordinary pressure. We work with responsible owners to define permitted uses, restricted data, required review and the route for requesting an exception. A practical decision guide can distinguish an approved internal draft from an unapproved customer commitment. It also names who can approve a new tool or accept a risk. You receive a one-page policy, an approved-tools list, an approval route for new applications and a data boundary table. Employees should not need to infer rules from a vendor's marketing language. Legal and regulatory interpretation remains with qualified advisers and the organisation's accountable teams.

• Acceptable-use rules with practical examples
• Approval and exception decision guide
• Clear ownership of specialist interpretation

Governance implementation asks how each important rule is enforced or checked. For a connected workflow, that may involve scoped access, approval before a sensitive action, an activity record and a stop procedure. For employee use, it may mean an approved application list, a source-checking requirement and a reporting channel. The exact control depends on the environment and needs testing. A policy saying that people must review output does not show that review happens before an action. We define the evidence a control should produce and who examines it. Technical configuration work is explicitly scoped, with dependencies on the administrators who own the relevant systems.

• Control-to-policy mapping
• Approval evidence and exception records
• Control checks with accountable reviewers

AI governance needs a way to change when tools, processes or responsibilities change. We define review triggers such as a new data source, expanded action authority or a material incident. An incident exercise walks through who stops the workflow, preserves relevant evidence, assesses the impact and decides whether it can resume. Routine reviews examine exceptions and control failures without treating a clean dashboard as proof of safety. The handover includes owners, records and a manageable review cadence.

• Change triggers and review cadence
• Incident tabletop and stop/resume responsibilities
• Governance handover with evidence ownership