Organizations moving beyond pilots
Put policy, risk controls, ownership, and reporting around broader AI deployment.
Enterprise services
AI Governance & Controls
Establish the policy, risk, and reporting foundation needed to operate AI Agents safely and defensibly at scale.
Deliverable preview
Expected outcome
A documented governance framework your organization can operate under and report against, covering policy, risk controls, and audit trail, ready for board and regulatory review.
- Governance policy framework and risk/controls register
- Board and regulatory reporting templates
- Clear ownership model (RACI) for ongoing AI governance
- For
- Organizations moving beyond pilots · Teams in regulated industries
- Takes
- On request
- Costs
- On request
- You get
- Governance policy framework and risk/controls register
Short answer
Enterprise AI governance for systems moving into operation
SUPERWISE® AI governance consulting helps organizations define the policies, risk controls, ownership, audit trail, and reporting needed to operate AI systems at scale.
The engagement delivers an enterprise AI governance framework, a risk and controls register, reporting templates, and a clear operating model.
Who it is for
Choose the path that matches your team
Teams in regulated industries
Prepare a governance framework, risk and controls register, and reporting templates for review.
Deliverables
Enterprise AI governance for systems moving into operation
AI governance, policy & framework
SUPERWISE works with your team to define usage policies, map risk and controls, and design an audit trail, translating the framework into reporting your board and regulators can rely on.
- Policy, risk, and control framework tailored to your AI/Agent use cases
- Explainability and audit-trail design aligned to regulatory expectations
- Defined roles, RACI, and escalation process for ongoing governance
Expected outcome
A documented governance framework your organization can operate under and report against, covering policy, risk controls, and audit trail, ready for board and regulatory review.
- Governance policy framework and risk/controls register
- Board and regulatory reporting templates
- Clear ownership model (RACI) for ongoing AI governance
The engagement
How it works
What's involved in the engagement
Policy Framework
- AI usage policy and acceptable-use guidelines
- Model/Agent approval and change-management workflow
Risk & Controls
- Risk taxonomy for AI/Agent use cases
- Control mapping: pre-deployment, runtime, post-deployment
Explainability & audit trail
- Decision-logging and explainability requirements by use case
- Audit-trail design aligned to regulatory expectations
Board & regulatory reporting
- Board/exec oversight reporting templates
- Regulatory-specific reporting options (e.g., healthcare, manufacturing)
Roles & operating model
- RACI for AI governance across business, IT, legal, compliance
- Escalation and exception-handling process
Deliverable & Readout
- Governance policy framework and risk/controls register
- Board and regulatory reporting templates
Proof
See governed AI in practice
Renova Health delivers AI-augmented chronic care while maintaining the human touch patients need.
Read the Renova Health case studyWhat it puts to work
The products behind AI Governance & Controls
SUPERWISE Sentinel
The policies we set are enforced by Sentinel on every request from a supported tool.
The SUPERWISE platform
One place for policies, guardrails, and the reporting leadership sees.
AI governance for boards
A board governs AI the way it governs any material risk: it sets the appetite, assigns ownership, and asks for evidence that the controls work. Its questions are consistent. Who is accountable when an AI system makes a decision that causes harm? How do we know AI is working as intended across every deployment? What happens when a system fails or produces an incorrect output?
What the board asks for. An inventory of the AI systems and agents in use, including third-party tools, with a named owner for each. The risk and controls register, with open gaps and the date each one closes. Incidents and exceptions since the last report, and what changed as a result.
Reporting cadence. AI reporting follows the board's existing committee cycle, through the committee that already oversees material risk, with a full-board review when strategy or risk appetite changes. Material incidents reach the board between meetings, under the same escalation rules as any other incident.
Who owns what. The board sets risk appetite and holds management accountable. An executive sponsor owns the governance program and reports to the board. Each AI system has a business owner accountable for its outcomes and a technical owner responsible for its controls. Risk, legal, and compliance review use cases before approval and test the controls afterward. A RACI makes those roles explicit, and this engagement delivers one with the board reporting templates.
To see where your organization stands first, take the free AI readiness assessment.
AI governance consulting questions
What to expect before you start
The engagement covers AI usage policy, approval and change workflows, a risk taxonomy, control mapping, audit-trail design, reporting templates, roles, and escalation processes.
Your team receives a documented governance framework, a risk and controls register, board and regulatory reporting templates, and a RACI for ongoing ownership.
Governance defines the policies, controls, ownership, and evidence design. A compliance engagement can then assess that operating framework against a selected target framework and organize evidence for review.
Managed AI operations puts the operating model into practice through monitoring, maintenance, support, reporting, risk reassessment, and roadmap reviews.
The board sets the organization's AI risk appetite, assigns clear ownership for AI decisions and outcomes, and holds management accountable for evidence that the controls work. It reviews AI risk on its regular committee cycle and expects material incidents to be escalated between meetings.
An inventory of AI systems and agents with a named owner for each, the risk and controls register with open gaps, and the incidents and exceptions since the last report. The engagement delivers board and regulatory reporting templates for this.
Related reading
All questions- How do you tell real AI governance from theater?Ask five questions, in order: where the data goes, who built the model, whether the controls are enforced while the system runs, what you can see while it runs, and…
- Who is liable when an AI system causes harm, or an agent acts?The people and organizations behind the system. California now says so in statute: a party that developed, modified, or used an AI system cannot defend itself by saying the system…
Next step
Talk to us about AI Governance & Controls
Best suited for organizations moving from pilot to broader deployment, especially in regulated industries.