An AI governance framework is the structure a company uses to decide how AI is approved, controlled and monitored: who owns each system, how risk is assessed, which controls apply at which risk level and how compliance is shown. Principles such as fairness and transparency sit at the top. The framework is what turns them into decisions someone can check.
Most companies do not need to invent one. They adapt an existing standard and focus their effort on the part standards leave open: getting the controls enforced in the tools people use every day.
The parts of an AI governance framework
1. Principles and policy
A short set of commitments, such as human oversight for high-impact decisions and no customer data in unapproved models, plus an acceptable use policy employees can actually read.
2. Roles and accountability
A governance committee that sets rules, and a named owner for every AI system. Security, legal, data and business teams each have defined responsibilities.
3. Inventory
A register of every AI model, tool, app and agent in use, what it does, what data it touches and who owns it.
4. Risk classification
Tiers that decide how much review each use case needs, from low risk (drafting internal emails) to high risk (decisions about credit, hiring or health).
5. Controls
The technical and process controls required per tier: access control, data boundaries, human approval, testing, documentation and logging.
6. Monitoring and audit
Ongoing evaluation of how AI systems behave in production, logs of actions and decisions, and periodic reviews.
7. Incident response
What happens when an AI system produces a harmful output or takes a wrong action: who is told, how it is contained and how it is fixed.
Standards to map your framework to
- NIST AI Risk Management Framework: organizes the work into Govern, Map, Measure and Manage. It is voluntary, practical and widely used as a starting point in the US.
- ISO/IEC 42001: an international standard for AI management systems, certifiable, and structured like ISO 27001.
- EU AI Act: law, not guidance. It sets obligations by risk category, with the strictest rules for high-risk systems.
- Sector rules: banking model risk guidance, healthcare privacy rules and similar requirements layer on top.
How to implement it in seven steps
Step 1: Build the inventory
Survey teams, check expense reports and SaaS logs, and list every AI system in use. You cannot govern what you cannot see, and the first inventory always finds more than expected.
Step 2: Classify by risk
Put each system in a tier based on who it affects, what data it uses and whether it acts or only advises.
Step 3: Assign owners
Every system gets a business owner accountable for its purpose and a technical owner accountable for how it runs.
Step 4: Define controls per tier
Write down what each tier requires. For example, high-risk systems need human approval for actions, restricted data access, testing before release and full logging.
Step 5: Enforce controls in the platforms
This is where frameworks usually stall. A control that exists only in a policy document is a hope. Access rules, approval steps and logs have to be configured in the systems where AI reads and changes data.
Step 6: Monitor
Review logs and evaluations on a schedule, and track metrics such as the share of AI actions that needed human correction.
Step 7: Review and adjust
Revisit the framework as regulation changes and as new kinds of AI, such as autonomous agents, come into use.
Where Jet Admin fits
Step 5 is the one Jet Admin helps with directly. Teams build AI apps and agents in Jet on data the company already has, from 200+ integrations, and every app and agent runs inside the same role model. Row-, column- and action-level permissions, SSO and audit logs are on the Business plan and above; on-premise and air-gapped deployment and SCIM provisioning are on the Enterprise plan. Approval steps can be built into any workflow, so high-risk actions wait for a person.
For the governance and risk platforms that handle inventory and regulatory mapping, see our list of AI governance solutions.
Frequently asked questions
What is an AI governance framework?
It is the set of roles, policies, risk tiers and controls a company uses to approve, run and monitor AI systems responsibly.
Which AI governance framework should I use?
Most companies start from the NIST AI Risk Management Framework, map it to ISO/IEC 42001 if they want certification, and add EU AI Act obligations if they operate in the EU.
How long does it take to implement?
An initial inventory, risk tiers and owners can be in place within a quarter. Enforcing controls across every system takes longer and is ongoing.
Who should own AI governance?
A cross-functional committee sets the rules, usually with security, legal, data and business leaders. Each AI system then has its own named owner.
Enforce the framework where AI touches data
Start with Jet Admin for free and build AI apps that run under your permissions and logs.