Maintained AI Systems Register
Current use cases, owners, status, data, risk outcomes, evidence links and review dates.
Managed AI
Governance.
Keep governance current as employees adopt new tools, vendors change their terms, systems add AI features and the organisation creates new use cases.
Ongoing governance cycleNew-use-case reviewsManagement reporting
What this solves.
A one-time evidence pack becomes outdated quickly. Managed AI Governance provides the operational support and review rhythm needed to keep registers, risk decisions, policies, training and actions aligned with actual use.
Outcome: management receives a current, traceable view of AI use and open risks without asking an internal employee to become a full-time AI governance specialist.
How it works.
The service operates as a recurring governance cycle with clear intake, review, maintenance and reporting activities.
Baseline onboarding
Confirm existing registers, policies, risk decisions, owners, open actions and evidence locations. Agree service boundaries and escalation contacts.
New-use-case intake
Provide a structured route for teams to propose new AI tools or materially changed uses before they become established practice.
Proportionate assessment
Review role, purpose, affected people, data, vendor, risk category, transparency, human oversight and required specialist input.
Register and evidence maintenance
Update the AI Systems Register, risk register, AI-to-RoPA mapping, decisions, evidence links and review dates.
Policy and vendor updates
Track relevant operational changes, review material vendor or system changes and keep working policies and procedures aligned.
People and onboarding support
Provide guidance for new starters and role changes, maintain training records and identify teams needing targeted refreshers.
Quarterly governance review
Review new systems, incidents, exceptions, overdue actions, material changes and emerging priorities with named stakeholders.
Management reporting
Produce a concise update showing current systems, risk movement, decisions, incidents, completed actions and next priorities.
What you receive.
The exact cadence is agreed in the service scope. Typical managed outputs include:
New-use-case assessments
Documented decisions for proposed tools, features and material changes, with conditions or escalation where required.
Updated risk and action register
Tracked risks, actions, owners, deadlines, evidence and changes in priority.
Policy maintenance
Controlled updates to relevant AI policies, approved-tools guidance and operational procedures.
Vendor review support
Review notes for material vendor, contract, subprocessor, transfer, retention or data-use changes.
Training and onboarding support
Refresher content, targeted guidance and maintainable attendance or acknowledgement records.
Quarterly review record
Agenda, decisions, open issues and assigned actions from the governance review.
Management compliance update
A concise, board-ready view of the governance position, material changes and decisions needed.
The benefit.
Governance becomes a continuing business process instead of a folder created once and forgotten.
Control change
Assess new tools and features before they create undocumented data flows or decision risks.
Keep evidence credible
Maintain review dates, owners, actions and records so the pack reflects current practice.
Support the compliance owner
Give the internal owner a reliable specialist process and an escalation route for difficult questions.
Report clearly
Help management see material risks, progress and decisions without reading every technical document.
What we need from you
- A named internal governance owner
- Teams that use the agreed intake process
- Notification of new systems and material changes
- Timely access to owners, vendors and documents
- Management decisions where risk acceptance is required
Service boundaries
- Legal opinions remain with qualified legal advisers
- Full DPIAs and high-risk assessments may require separate scope
- Technical remediation is agreed separately
- The organisation remains responsible for its decisions
- Emergency incidents follow an agreed escalation route
Keep your AI governance current.
Managed support works best once a reliable baseline exists. Discuss your current registers, policies, internal capacity and expected volume of new AI use cases.
Discuss managed support ↗