What is an enterprise AI governance framework?
An enterprise AI governance framework is the set of decisions, owners, controls and evidence that lets an organization approve an AI use, run it and audit it later. A policy states intent. The framework says who decides, which controls apply at each level of risk, what proof must exist and when the decision gets reviewed. For a CIO, CFO or CDAO, it is the machinery behind 'yes, finance may point Claude at NetSuite'.
The earlier article, Why Enterprise AI Governance Fails, and How to Fix It, diagnosed four patterns: policies without owners, approvals attached to tools instead of uses, controls with no evidence and review cycles slower than the technology they govern. This article is the constructive half: a framework and a matching AI governance framework template you can adopt in one quarter, aligned to standards auditors already recognize.
How does the framework map to NIST AI RMF, ISO/IEC 42001 and the EU AI Act?
The NIST AI Risk Management Framework supplies the operating structure, ISO/IEC 42001 supplies the management-system discipline and the EU AI Act supplies the risk tiers. NIST AI RMF 1.0, released on 26 January 2023 for voluntary use, organizes the work into four functions: Govern for accountability and culture, Map for context and inventory, Measure for testing and evidence, and Manage for response and prioritization. The companion Playbook adds voluntary suggested actions under each function.
ISO/IEC 42001:2023 specifies requirements for establishing, implementing, maintaining and continually improving an AI management system, and the system can be independently certified. NIST's AI Resource Center lists a crosswalk between the AI RMF and ISO/IEC 42001, so one control set can serve both. The EU AI Act entered into force on 1 August 2024 and sorts uses into unacceptable, high, transparency and minimal risk levels. After the AI Omnibus amendment, obligations for the listed high-risk areas apply from 2 December 2027.
Related: Explore the AI readiness assessment and advisory engagement
What goes into scope, inventory and ownership?
Scope the framework around uses and the data they reach, not around products. The core artifact is a use register. Each entry records the business question, the systems and tables involved, the user population, the consequence of a wrong answer, the model family and the connector version. Connectors get their own column: an MCP server that exposes NetSuite or Salesforce data is a standing access path and needs a named owner.
Ownership is a list of decision rights with response times, not a committee. Name an executive sponsor who accepts residual risk, a use owner for each register entry, a data owner for each source system, a security reviewer and a platform team that runs models and connectors. Each role gets a written answer to 'what can I approve alone, and what needs a second signature'.
How do risk tiers decide the controls?
Tiers keep review effort proportional: the higher the consequence, the more controls apply. Tier 1 covers read-only questions on non-sensitive internal data, such as summarizing last quarter's shipped orders. Tier 2 covers anything touching regulated data, customer records or financial reporting, such as a margin analysis by legal entity. Tier 3 covers write actions and decisions that affect people, such as an agent that proposes vendor payments, plus anything the EU AI Act would treat as high risk.
Four control families carry the weight. Data access: the assistant acts under the requesting user's identity, so source-system row-level and field-level permissions apply to every query. Definitions: revenue, backlog and days sales outstanding come from a certified semantic layer. Evidence: every answer records the query that ran, the certified metric definition used and the policies applied, which is how Nexatron answers today. Human approval: no write to a system of record completes without a named approver. Test against the OWASP Top 10 for LLM Applications, especially prompt injection and excessive agency.
How do you monitor, handle incidents and keep the framework current?
Monitor the controls rather than the models, give one named person authority to stop a use, and put the register on a fixed review calendar. Track monthly the share of live uses in the register, the percentage of Tier 2 and Tier 3 uses with a current evaluation record, access review completion, median working days from request to approval, blocked write attempts and answers users flag as wrong. Rising approval times or falling register coverage signal bypass before an incident does.
Write that stop authority into an incident runbook: who can suspend a connector or a use within the hour, what counts as an AI incident (a leaked field or an unapproved write) and the short root-cause note that feeds back into the register. Then fix the cadence: quarterly review of the register and tiers, annual review of the framework itself, and re-approval whenever a use gains a new data source, write access, a new user population or a different model family.
What sections does the AI governance framework template include?
The AI governance framework template has nine sections, each a page or less. The first three set out scope and principles, the use register and roles with decision rights. The next three carry the risk tiering rubric, the control catalog by tier and the evidence and logging requirements. The last three hold the monitoring measures and incident runbook, the review cadence and change triggers, and a crosswalk that maps each section to the NIST AI RMF functions, ISO/IEC 42001 and the EU AI Act risk levels.
Pilot the template on one Tier 2 use, say a margin analysis by legal entity against Oracle Fusion or NetSuite data. Complete its register entry, assign the tier, list the controls it already satisfies and the evidence you can produce today. The gaps become your first quarter of governance work and the finished entry becomes the worked example for later requests. For a second set of eyes from a team that has implemented those systems for more than two decades, Talk to Our AI Team or Book a Demo.