AI Governance Consulting

AI governance covers the rules and controls that make AI use safe and accountable. Paloren designs governance frameworks that include: which AI tools are approved and for what use; what data each tool may access and under what permission boundaries; which decisions require a person to approve; how outputs are reviewed and quality is assessed; what records are kept for audit purposes; and what employees should do when they are unsure whether a use case is appropriate. These elements are designed together so they work as a system rather than a list of restrictions.

• Approved tools and acceptable use
• Data access and permission boundaries
• Approval routes and quality standards

The most common AI governance failure is a policy written without understanding how the work actually gets done. It bans the tools people are already using, ignores the workflows that need AI and creates no route for exceptions. The result is that people find workarounds, which is riskier than a well-designed framework. Paloren designs governance by mapping the workflows first, then defining the controls that fit them. The output is a framework people can follow because it matches how they work.

• Policy written without workflow understanding gets ignored
• Workarounds are riskier than a designed framework
• Controls designed to fit how the work is done

Paloren designs governance in four steps. First, map the workflows where AI is used or proposed. Second, assess the risks: what data is involved, what decisions are made, what happens if the output is wrong. Third, design the controls: access rules, approval steps, quality checks and escalation paths. Fourth, test the controls against representative workflows to confirm they are workable. The framework is documented with the rationale for each control, so the team understands why it exists and not just what it says.

• Map workflows and assess risks
• Design controls that fit the workflow
• Test controls before finalising

Every AI governance framework needs clear approval routes. Paloren designs these based on the workflow: which actions require a named person to accept, which can proceed automatically and which need a supervisor. The approval route is designed into the system, not added as a manual step after the fact. For exceptions, the framework defines who can approve a deviation from the standard controls and what documentation is required. This prevents both unauthorised AI use and unnecessary blocking of useful work.

• Named approvers for consequential actions
• Approval designed into the system
• Exception route with defined authority

AI governance must address how AI systems access and use personal and sensitive data. Paloren designs permission-aware retrieval so users only see records their role allows. Data minimisation means the AI system accesses only the fields it needs for the workflow, not the entire database. Audit records document what the AI accessed, what it produced and who reviewed the output. These controls work together to limit exposure and create accountability. Legal and regulatory interpretation remains with qualified advisers.

• Permission-aware access to data
• Data minimisation and field-level scoping
• Audit records for access and output

Paloren scopes AI governance engagements based on the number of AI use cases, the complexity of the data and the regulatory environment. A governance framework for a single team using one AI tool is smaller than one covering multiple departments with different workflows and data types. The engagement includes workflow mapping, risk assessment, control design, testing and a documented framework. Paloren scopes the proposal after understanding the scope of AI use in the business.

• Scoped by AI use cases and complexity
• Includes workflow mapping and risk assessment
• Documented framework with testing

After the framework is delivered, Paloren provides training so the team understands the controls and how to follow them. The framework is embedded into the systems where AI is used, so approval routes and access rules are enforced by the technology rather than by memory. A review cycle is defined: after a defined period, the framework is assessed against how the work is actually being done, and controls are adjusted where they are too restrictive or too loose. This keeps governance alive rather than becoming a shelf document.

• Training on the framework and controls
• Controls embedded into systems
• Review cycle adjusts controls based on use

The AI use case inventory is the starting point for governance. It documents every AI tool in use, what it does, what data it accesses and who uses it. Paloren builds this inventory during discovery because governance cannot be designed without knowing what is actually happening. The inventory reveals unapproved tools, overlapping use cases and gaps where governance is needed. It also becomes the reference point for the framework design.

• Every AI tool documented with its use
• Data access and users recorded
• Unapproved use identified for review

AI governance is the internal framework the business designs to manage AI use: access rules, approval routes, quality standards and audit trails. AI compliance is the practice of meeting external regulatory requirements, such as data protection laws or industry-specific regulations. They are related but not the same. Paloren designs governance frameworks that support compliance, but the regulatory interpretation stays with qualified advisers. The technical controls make compliance practical in daily work.

• Governance: internal framework the business designs
• Compliance: meeting external regulatory requirements
• Technical controls support both