AI governance implementation
Build and activate your AI governance operating system
Sentient turns governance requirements into an AI inventory, ownership records, controls, review paths, escalation, and evidence capture inside the tools your teams already use, then tests them on real work.
For companies that know what governance they need and now need it running.
01
- Require
- Build
- Activate
Where implementation starts
From requirements to a working operating system.
Implementation starts from requirements that already exist and turns each one into a record, a control, or a workflow with a named owner.
The work is built into the tools, workflows, and processes your teams already use, so approvals, evidence, and review come from doing the work, not from additional tasks.
Requirements not decided yet? Start with consultingSentient doesn't assume adoption. We make it inevitable.
Where the requirements come from
- An approved governance design
- Framework obligations already interpreted
- Customer and contract commitments
- Audit, diligence, or insurer findings
- An internal AI policy that has not yet been put into practice
02
- Records
- Controls
- Workflows
- Cadence
What gets built
Twelve working parts, each with an owner.
Each part is a working record, control, or workflow, connected to the others so it stays current as systems, people, and vendors change.
01
AI inventory
A maintained register of every AI system, model, vendor, and integration in use, with its purpose, data, and dependencies.
Register
02
Ownership records
A named owner recorded against each system, with a handover step when people or teams change.
Accountability
03
Authority boundaries
What each system is approved to do, with which data, and the limits it may not cross without approval.
Approved use
04
Controls
The approval gates, checks, and restrictions the control requirements call for, placed in the workflows where the work happens.
Operating controls
05
Review paths
Defined review points, named reviewers, and the authority to approve, intervene, or stop an outcome.
Human review
06
Escalation
The conditions that trigger escalation, who receives it, and what they are authorized to decide.
Triggers and routes
07
Exception handling
How exceptions are requested, approved by the right authority, time-limited, and recorded.
Departures from approved use
08
Evidence capture
Approvals, reviews, and decisions recorded as they happen, with the systems and versions involved.
Records generated by the work
09
Change traceability
Changes to models, prompts, data, vendors, and workflows trigger reassessment and re-approval, with the record kept.
Reassessment
10
Workflows and automations
Reminders, routing, record updates, and evidence collection automated so governance does not depend on memory.
Lower friction
11
Runtime-control requirements
What runtime mechanisms must enforce, including access limits, shutdown, and revocation, specified for the teams and tools that run them.
Enforcement specification
12
Operating cadence
The recurring reviews, decision preparation, and renewals that keep the system current after launch.
Recurring review
03
- Require
- Assign
- Record
Requirement to operating control
A requirement says what. A control shows who and how.
Every requirement ends up with an owner, a trigger, and a record.
The requirement
The operating control
The requirement
Know which AI systems are in use.
The operating control
An AI inventory with an accountable owner for each system, updated when systems and vendors change.
The requirement
Use AI only for approved purposes.
The operating control
Approved-use boundaries for each system, with an exception path that requires approval and leaves a record.
The requirement
Keep a human in the loop.
The operating control
Named reviewers at defined review points, with the authority to approve, intervene, or stop.
The requirement
Accept risk deliberately.
The operating control
A risk acceptance record from someone authorized to accept it, with its conditions and the reasoning behind it.
The requirement
Manage changes to AI systems.
The operating control
Change controls that trigger reassessment and re-approval when models, prompts, data, or vendors change.
The requirement
Be able to stop a system.
The operating control
A defined shutdown and revocation path: who can trigger it, what it stops, and which runtime mechanism enforces it.
The requirement
Maintain evidence.
The operating control
Evidence captured as the work happens, so the records already exist when a customer, auditor, or insurer asks.
04
- Build
- Test
- Commission
Governance Activation Sprint
Built, tested on real work, then commissioned.
The Governance Activation Sprint is the implementation engagement. It puts the essential records, controls, review paths, escalation, automations, and operating cadence in place.
The approach is simple. Reduce friction through automation and embed meaningful controls, and adoption gets stronger while risk gets lower.
AI governance operationsNothing is called operational until it has worked on real work.
How the sprint moves
- Confirm the requirements and what to build first
- Stand up the inventory, ownership records, and authority boundaries
- Build controls, review paths, escalation, and exception handling into existing workflows
- Automate evidence capture and routine governance tasks
- Exercise every control on real work and fix what fails
- Commission the system and hand over the operating cadence
Ongoing Governance Operations can keep it running
05
- Records
- Controls
- Evidence
Questions, answered
AI governance implementation, answered.
What is AI governance implementation?
AI governance implementation turns governance requirements into working records, controls, and workflows with named owners: an AI inventory, ownership records, authority boundaries, review paths, escalation, and evidence capture. It is finished when those parts have worked on real decisions, not when documents are delivered.
What records and controls does AI governance require?
At minimum, an AI system inventory, an owner for each system, approved-use boundaries, decision and risk-acceptance records, human review points, escalation and exception paths, change records, and evidence that each control ran. A system's risk level sets how much of each it needs.
How do companies turn AI policy into operating practice?
Give each policy requirement an owner, a trigger, and a record, then build it into the tools where the work already happens. Automating the routing and evidence capture makes following the policy the easiest path for the team.
What evidence should AI governance preserve?
Approvals and who gave them, risk acceptances with their conditions, human review outcomes, exceptions and their expiry, changes to models, prompts, data, and vendors with their re-approval, and records that each control operated. Captured as the work happens, this is the audit evidence reviewers ask for.
Prepare that evidence for reviewCan implementation follow NIST AI RMF or ISO/IEC 42001?
Yes. The same records and controls can be mapped to NIST AI RMF functions and ISO/IEC 42001 requirements, so one operating system supports several frameworks. ISO/IEC 42001 certification still comes from an accredited certification body.
NIST AI RMF implementation
06
- Discuss
- Design
- Implement
- Operate
Be ready before they ask
Build the operating structure before the next customer, investor, insurer, partner, or buyer demands proof.
Sentient builds the governance structure companies need to show how AI is owned, controlled, reviewed, evidenced, and operated. Talk to Sentient about what your customers, investors, insurers, partners, or buyers will expect to see.