Turn AI policy into controls and evidence

Enable authorized AI use while ensuring material decisions, controls, evidence, exceptions, and residual risk can be understood and defended.

The pressure you are holding

• AI use is expanding faster than the organization can maintain an accurate system and vendor inventory
• Policies describe principles but do not identify enforceable technical or operating controls
• Applicability, role, geography, data, use, and impact can change the obligations attached to a system
• Vendor documentation and internal claims are being accepted without representative evidence
• Evidence is assembled reactively for customers, procurement, audits, or incidents instead of produced by the lifecycle

Questions worth resolving before scale

1. Which systems and uses are in scope, who performs each role, and how are risk and applicability determined?
2. What controls are actually enforced in product, infrastructure, workflow, vendor management, and human oversight?
3. What evidence demonstrates that evaluations, approvals, notices, monitoring, incidents, and changes occurred?
4. How are exceptions, residual risks, model or provider changes, and retirement decisions authorized?
5. Where is qualified legal, privacy, security, accessibility, sector, or assurance expertise required?

• A governed inventory connecting systems, uses, models, vendors, data, jurisdictions, owners, risk, and lifecycle state
• A control matrix mapped to enforceable technical and operating mechanisms
• Representative evaluation, human-oversight, transparency, vendor, incident, and change evidence
• Decision records for approvals, exceptions, residual risk, material changes, and retirement
• A readiness roadmap that separates verified controls, gaps, dependencies, and qualified-specialist decisions

The engagement path

1. Define scope and roles. We inventory systems and intended uses, map organizational and provider roles, identify relevant data and affected parties, and record applicability questions for qualified interpretation.
2. Trace obligations to controls. Requirements and internal policies are mapped to accountable owners, product behavior, technical enforcement, workflow, vendor terms, documentation, and retained evidence.
3. Test operating effectiveness. Representative records and system behavior are sampled to determine whether controls exist, operate under realistic conditions, produce evidence, and surface exceptions.
4. Create the lifecycle. Intake, approval, release, monitoring, incident, material change, review, exception, and retirement gates are assigned to owners and connected to evidence rather than periodic document exercises.

Decision criteria to keep visible

• The inventory describes the real system, intended and prohibited uses, roles, data, users, geography, providers, and lifecycle state
• Every claimed control identifies its mechanism, owner, evidence, test, frequency, exception path, and dependency
• Representative evaluation covers affected workflows and foreseeable failures rather than only general model benchmarks
• Legal interpretations, formal assurance, certification, and risk acceptance are performed by appropriately qualified and authorized parties
• Changes to model, data, tools, purpose, users, geography, vendor, or autonomy trigger a defined review when material

Questions teams ask