How Should Enterprises Govern AI? A Practical Guide to Policies, Controls, and Monitoring
How Should Enterprises Govern AI? A Practical Guide to Policies, Controls, and Monitoring
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Blog
Latest
2/3/26
·
Brendan Kelly
AI governance is the system of policies, controls, and oversight that ensures every AI use case in your organization is safe, compliant, and aligned with business and regulatory expectations. The most effective programs define clear life cycle phases, maintain a central inventory of AI solutions, and apply consistent controls, risk assessments, and monitoring to each one.
Why does AI governance matter now more than ever?
AI introduces new opportunity and new risk at the same time. It often relies on external models (like foundation models and LLMs), operates on sensitive data, and is regulated by an evolving landscape of standards and laws.
Without governance:
Teams ship AI without clear owners
Handoffs break, and projects stall
Risks go unmanaged until they show up as incidents or audit findings
With governance, you create:
Clear phases every AI initiative follows
Defined handoffs between teams
Assigned ownership for key activities across the life cycle
What are the core building blocks of an AI governance program?
A practical AI governance program usually includes these pillars:
Policies – What’s allowed, what’s not, and under what conditions
Solution Inventory – A central list of all AI systems and use cases
AI Controls – Specific requirements and safeguards applied to solutions
Risk Assessment & Vendor Review – How you evaluate and approve AI risks
Model Monitoring & Support – How you manage AI in production
Audit & Review – How you check that controls and policies are followed
Each piece reinforces the others. Policies define expectations; inventories show where AI exists; controls, risk reviews, and monitoring make sure behavior matches intent.
How should enterprises approach AI policies?
Start by leveraging existing policies—like data, security, and acceptable use—and extend them for AI instead of reinventing everything.
A good starting point is an AI Acceptable Use Policy that:
Explains how employees may and may not use AI tools
Clarifies use of third-party AI (e.g., public LLMs) vs. internal systems
Defines responsibilities for protecting sensitive data
Outlines escalation paths if something goes wrong
You can use templates as a base, then adapt them with your legal and risk teams to fit your organization.
What is an AI solution inventory and why is it critical?
An AI solution inventory is a central list of every AI system and use case in your organization—whether built internally or purchased from vendors.
It enables you to:
See where AI is used across departments
Track which policies and controls apply to each solution
Identify high-risk or high-impact use cases
Support audits, regulatory inquiries, and internal reporting
Without an inventory, governance becomes reactive and manual. With one, you can manage AI like any other critical asset.
How do AI controls, risk assessments, and vendor reviews work together?
AI controls translate high-level policies into concrete requirements you can test against.
Typical controls include:
Data retention and access rules
Model validation and testing requirements
Human-in-the-loop or override mechanisms
Documentation and explainability expectations
AI risk assessments and vendor reviews then evaluate specific solutions against these controls:
For internal builds, an AI risk assessment examines data, model behavior, impacts, and mitigations before go-live.
For vendors, a vendor risk review checks how their AI is built, monitored, and updated and how it handles your data.
This ensures every AI use case—internal or external—meets a minimum governance bar before it’s trusted in production.
Why are model monitoring and support core to AI governance?
AI isn’t “one and done”—models drift, data changes, and user behavior evolves.
That’s why model monitoring and support are called out specifically in the governance workbook: they are “fundamentally required controls” for safe operation.
Good monitoring and support include:
Tracking model performance over time
Alerting on anomalies, failures, or threshold breaks
Clear ownership for incident response
Processes for updating or retraining models
Communication back to business owners on health and impact
This is also where governance and operations meet: support teams need enough context from development to troubleshoot effectively as the real world changes.
How should organizations handle audit and review for AI?
Audit and review close the loop by checking whether policies and controls are actually followed.
Regular AI audits might cover:
Whether all AI solutions are in the inventory
Whether required risk assessments and approvals were completed
Whether monitoring thresholds and alerts are configured and acted upon
Whether documentation (e.g., model cards) is up to date
This function supports compliance with frameworks like ISO 42001, NIST AI RMF, the EU AI Act, and sector-specific guidance like SR-11.
How can a team get started with AI governance?
You don’t have to launch a perfect governance program on day one. Start with:
A basic AI policy covering acceptable use and data handling.
A simple solution inventory—even a spreadsheet—to list AI systems.
A minimal set of controls for higher-risk AI, like customer-facing or PII-using systems.
A lightweight risk assessment template and review process.
Basic monitoring expectations for AI in production.
Then iterate: add depth where risk is highest, and evolve the framework as regulations and your AI footprint grow.
📘 Ready to put structure around AI governance?
Download AlignAI’s AI Governance Workbook to implement policies, inventories, controls, risk assessments, and monitoring tailored to your organization.
In Short: AI Governance, Explained
Q: What is AI governance in an enterprise context?
AI governance is the combination of policies, processes, and controls that ensure AI systems are used responsibly, safely, and in compliance with internal and external requirements.
Q: Why isn’t traditional IT governance enough for AI?
AI systems often learn from data, evolve over time, and may rely on external models. This creates new risks around bias, drift, explainability, and accountability that traditional IT governance doesn’t fully address.
Q: What’s the first step toward AI governance?
Most organizations start by creating an AI policy, identifying existing AI use cases, and introducing a basic risk assessment and approval process for new AI solutions.
Q: Who should own AI governance?Ownership is often shared between a central AI or data team, risk or compliance, and legal. Some organizations formalize this as an AI governance council or steering committee.
AI governance is the system of policies, controls, and oversight that ensures every AI use case in your organization is safe, compliant, and aligned with business and regulatory expectations. The most effective programs define clear life cycle phases, maintain a central inventory of AI solutions, and apply consistent controls, risk assessments, and monitoring to each one.
Why does AI governance matter now more than ever?
AI introduces new opportunity and new risk at the same time. It often relies on external models (like foundation models and LLMs), operates on sensitive data, and is regulated by an evolving landscape of standards and laws.
Without governance:
Teams ship AI without clear owners
Handoffs break, and projects stall
Risks go unmanaged until they show up as incidents or audit findings
With governance, you create:
Clear phases every AI initiative follows
Defined handoffs between teams
Assigned ownership for key activities across the life cycle
What are the core building blocks of an AI governance program?
A practical AI governance program usually includes these pillars:
Policies – What’s allowed, what’s not, and under what conditions
Solution Inventory – A central list of all AI systems and use cases
AI Controls – Specific requirements and safeguards applied to solutions
Risk Assessment & Vendor Review – How you evaluate and approve AI risks
Model Monitoring & Support – How you manage AI in production
Audit & Review – How you check that controls and policies are followed
Each piece reinforces the others. Policies define expectations; inventories show where AI exists; controls, risk reviews, and monitoring make sure behavior matches intent.
How should enterprises approach AI policies?
Start by leveraging existing policies—like data, security, and acceptable use—and extend them for AI instead of reinventing everything.
A good starting point is an AI Acceptable Use Policy that:
Explains how employees may and may not use AI tools
Clarifies use of third-party AI (e.g., public LLMs) vs. internal systems
Defines responsibilities for protecting sensitive data
Outlines escalation paths if something goes wrong
You can use templates as a base, then adapt them with your legal and risk teams to fit your organization.
What is an AI solution inventory and why is it critical?
An AI solution inventory is a central list of every AI system and use case in your organization—whether built internally or purchased from vendors.
It enables you to:
See where AI is used across departments
Track which policies and controls apply to each solution
Identify high-risk or high-impact use cases
Support audits, regulatory inquiries, and internal reporting
Without an inventory, governance becomes reactive and manual. With one, you can manage AI like any other critical asset.
How do AI controls, risk assessments, and vendor reviews work together?
AI controls translate high-level policies into concrete requirements you can test against.
Typical controls include:
Data retention and access rules
Model validation and testing requirements
Human-in-the-loop or override mechanisms
Documentation and explainability expectations
AI risk assessments and vendor reviews then evaluate specific solutions against these controls:
For internal builds, an AI risk assessment examines data, model behavior, impacts, and mitigations before go-live.
For vendors, a vendor risk review checks how their AI is built, monitored, and updated and how it handles your data.
This ensures every AI use case—internal or external—meets a minimum governance bar before it’s trusted in production.
Why are model monitoring and support core to AI governance?
AI isn’t “one and done”—models drift, data changes, and user behavior evolves.
That’s why model monitoring and support are called out specifically in the governance workbook: they are “fundamentally required controls” for safe operation.
Good monitoring and support include:
Tracking model performance over time
Alerting on anomalies, failures, or threshold breaks
Clear ownership for incident response
Processes for updating or retraining models
Communication back to business owners on health and impact
This is also where governance and operations meet: support teams need enough context from development to troubleshoot effectively as the real world changes.
How should organizations handle audit and review for AI?
Audit and review close the loop by checking whether policies and controls are actually followed.
Regular AI audits might cover:
Whether all AI solutions are in the inventory
Whether required risk assessments and approvals were completed
Whether monitoring thresholds and alerts are configured and acted upon
Whether documentation (e.g., model cards) is up to date
This function supports compliance with frameworks like ISO 42001, NIST AI RMF, the EU AI Act, and sector-specific guidance like SR-11.
How can a team get started with AI governance?
You don’t have to launch a perfect governance program on day one. Start with:
A basic AI policy covering acceptable use and data handling.
A simple solution inventory—even a spreadsheet—to list AI systems.
A minimal set of controls for higher-risk AI, like customer-facing or PII-using systems.
A lightweight risk assessment template and review process.
Basic monitoring expectations for AI in production.
Then iterate: add depth where risk is highest, and evolve the framework as regulations and your AI footprint grow.
📘 Ready to put structure around AI governance?
Download AlignAI’s AI Governance Workbook to implement policies, inventories, controls, risk assessments, and monitoring tailored to your organization.
In Short: AI Governance, Explained
Q: What is AI governance in an enterprise context?
AI governance is the combination of policies, processes, and controls that ensure AI systems are used responsibly, safely, and in compliance with internal and external requirements.
Q: Why isn’t traditional IT governance enough for AI?
AI systems often learn from data, evolve over time, and may rely on external models. This creates new risks around bias, drift, explainability, and accountability that traditional IT governance doesn’t fully address.
Q: What’s the first step toward AI governance?
Most organizations start by creating an AI policy, identifying existing AI use cases, and introducing a basic risk assessment and approval process for new AI solutions.
Q: Who should own AI governance?Ownership is often shared between a central AI or data team, risk or compliance, and legal. Some organizations formalize this as an AI governance council or steering committee.
