Category: Management System Governance

AI Policy

Also known as: Corporate AI Policy, AI Usage Policy, Responsible AI Policy

Simply put

An AI policy is a formal set of rules and guidelines that tells people in an organization how AI should and should not be used. It aims to guide ethical, responsible, and compliant use of AI tools across teams, and typically covers development, deployment, and everyday use. It is an organizational governance document rather than a technical control on any single model.

Formal definition

An AI policy is a governance instrument comprising documented principles, rules, and procedures intended to guide the responsible development, deployment, and use of AI technologies within an organization. As commonly described, it sits within the broader AI governance function—defining acceptable use, roles, and expectations to support ethical and compliant enterprise AI—and is typically operationalized through supporting standards, controls, and oversight processes. It should be distinguished from model risk management activities such as model validation, monitoring, and risk measurement; a policy sets organizational direction and accountability but does not itself measure or eliminate model-specific risk. Scope, enforceability, and content vary by organization and sector, and the term as used in the evidence refers to internal corporate policy rather than to any specific external regulation or binding legal instrument.

Why it matters

As organizations adopt AI tools across functions such as HR, marketing, IT, and finance, the absence of clear direction on acceptable use creates inconsistency in how AI is developed, deployed, and relied upon day to day. An AI policy provides a documented reference point that guides employees on how AI should and should not be used, helping teams apply AI tools in a manner intended to be ethical and compliant. Without such a policy, decisions about AI use tend to be made ad hoc by individual teams, which can undermine accountability and make organizational oversight difficult.

An AI policy matters because it establishes organizational direction and expectations, but professionals should be careful not to overstate what it accomplishes on its own. A policy sets rules and assigns responsibility; it does not, by itself, measure, monitor, or eliminate the risks associated with any specific model. It is best understood as a foundational governance instrument that is operationalized through supporting standards, controls, and oversight processes rather than as a technical safeguard. Treating a policy document as a substitute for those downstream activities—such as model validation and monitoring, which fall under model risk management—is a common conceptual error.

Because scope, enforceability, and content vary considerably by organization and sector, the practical significance of an AI policy depends on how it is implemented and enforced. The term as used here refers to an internal corporate policy rather than to any external regulation or binding legal instrument, and it should not be conflated with jurisdiction-specific legal frameworks. Its value lies in creating a shared, documented basis for responsible AI use that other governance and risk activities can build upon.

Who it's relevant to

AI Governance and Compliance Leaders

Those responsible for organizational AI governance use an AI policy to define acceptable use, assign roles, and set expectations for ethical and compliant AI use across the enterprise. They should treat the policy as a directional instrument that must be operationalized through supporting standards, controls, and oversight processes rather than as a self-executing safeguard.

Business Function Teams (HR, Marketing, IT, Finance)

Employees and managers in functions that adopt AI tools rely on the policy to understand how they should and should not use AI in daily tasks. For these users, the policy translates broad organizational principles into practical guidance for consistent, responsible use across teams.

Model Risk and Second-Line Functions

Professionals in risk management should understand where an AI policy ends and model risk management begins. The policy sets organizational direction and accountability, but activities such as model validation, monitoring, and risk measurement are distinct and are not performed by the policy itself; conflating the two can leave model-specific risks unaddressed.

Legal and Policy Specialists

Legal and policy professionals should note that an internal corporate AI policy is not the same as an external regulation or binding legal instrument, and that its scope and enforceability vary by organization and sector. They can help ensure the policy is consistent with applicable obligations without treating the document as a source of legal compliance in itself.

Inside AI Policy

Scope and Applicability

A statement of which AI systems, use cases, business units, and personnel the policy governs, along with any exclusions. Well-drafted policies typically specify whether they cover internally developed models, third-party and vendor AI, and general-purpose or generative AI, since coverage boundaries frequently drive downstream control obligations.

Roles, Responsibilities, and Accountability

Defined ownership for AI-related decisions across the organization, commonly mapped to lines-of-defense structures. This is an AI governance element concerned with organizational accountability and oversight, and it should be distinguished from the risk identification and measurement activities that fall under model risk management.

Principles and Objectives

The high-level commitments the organization adopts, such as accountability, transparency, and oversight of AI systems. These often reference voluntary frameworks or standards, but a policy should indicate whether such references are binding internal requirements or aspirational alignment.

Risk Management Linkages

Provisions connecting the policy to risk assessment, validation, monitoring, and control processes. In many organizations this is where AI governance overlaps with model risk management, though the policy typically sets the mandate and reporting structure rather than performing the technical risk measurement itself.

Regulatory and Standards Alignment

References to the external frameworks, guidance, or laws the organization intends to align with. Because instruments differ in force and jurisdiction, a policy should identify the issuing body and note whether each instrument is binding law, supervisory guidance, or a voluntary standard where that status is known.

Lifecycle Controls

Requirements applied across development, deployment, monitoring, change management, and decommissioning of AI systems. This typically includes distinct expectations for pre-deployment review and ongoing monitoring, reflecting the different concerns each stage raises.

Review, Approval, and Escalation

Procedures for approving new AI use cases, escalating issues, and periodically reviewing the policy itself. This component establishes how decisions are documented and how exceptions are handled.

Common questions

Answers to the questions practitioners most commonly ask about AI Policy.

Is an AI policy the same thing as an AI governance framework?

No, though the two are frequently conflated. An AI policy is typically a written statement of principles, requirements, and expectations that an organization sets for how AI is to be developed, procured, deployed, and monitored. An AI governance framework is broader: it encompasses the organizational structures, roles, accountability, oversight bodies, and processes that operationalize and enforce policies. A policy is one component within a governance framework rather than a synonym for it. As commonly understood, the policy states the 'what' and 'why,' while the governance framework addresses the 'who' and 'how' of oversight.

Does having an AI policy in place mean an organization is compliant with regulations like the EU AI Act or with SR 11-7?

Not necessarily. A written AI policy is often a foundational element, but its existence does not by itself demonstrate compliance with any specific instrument. The EU AI Act is legislation issued in the EU jurisdiction, while SR 11-7 is supervisory guidance associated with U.S. banking regulators addressing model risk management; they differ in scope, legal status, and applicability. A policy must be implemented, evidenced, and aligned with the particular requirements of whichever framework applies to the organization. Professionals should avoid treating a policy document as a substitute for the underlying controls, testing, documentation, and oversight those frameworks contemplate.

Who should own and approve an AI policy within an organization?

Ownership and approval vary by organization and are not universally prescribed. In many arrangements, senior management or a designated governance body sponsors and approves the policy, while accountability for its content may sit with a function such as risk, compliance, legal, or a dedicated AI governance office. The specific allocation typically depends on the organization's size, sector, and existing lines-of-defense structure. What matters functionally is that approval authority is clearly assigned and that the owning function has the mandate to maintain and enforce the policy.

How often should an AI policy be reviewed or updated?

There is no single required cadence that applies across all contexts. Many organizations review such policies periodically and also on a triggered basis—for example, in response to material changes in the regulatory landscape, new deployment scenarios, or lessons from incidents. Because regulatory treatment of AI is evolving, some organizations adopt more frequent review cycles than they would for more settled policy areas. The appropriate frequency typically reflects the organization's risk profile and the pace of change in its applicable requirements.

What is typically included in the scope of an AI policy?

Scope varies, but AI policies commonly address matters such as permitted and prohibited uses, roles and responsibilities, risk assessment expectations, documentation and record-keeping, monitoring, and escalation of issues. Some organizations extend scope to procured or third-party AI as well as internally developed systems. It is often useful to state explicitly what the policy does not cover—for instance, whether it applies only to certain classes of systems—so that gaps are visible rather than assumed to be addressed.

How does an AI policy relate to the three lines of defense?

An AI policy can inform how responsibilities are distributed across the first, second, and third lines of defense, but the policy and the lines-of-defense model are distinct concepts. In many arrangements, the first line owns and operates AI systems within policy requirements, the second line provides oversight and challenge of adherence, and the third line offers independent assurance over the overall framework. The policy typically articulates expectations that each line then implements or evaluates according to its role, without collapsing those roles into one another.

Common misconceptions

An AI policy and a model risk management framework are the same thing.

They are related but distinct. An AI policy is primarily a governance instrument that establishes organizational structures, principles, accountability, and oversight for AI systems. Model risk management, historically framed by guidance such as SR 11-7 / OCC 2011-12 in U.S. banking, focuses on identifying, measuring, monitoring, and controlling risks arising from model use. A policy may mandate model risk management activities, but it does not replace the technical risk processes themselves, and the two should not be collapsed.

Adopting an AI policy makes the organization compliant with the EU AI Act, the NIST AI Risk Management Framework, ISO/IEC 42001, and SR 11-7 at once.

These instruments are issued by different bodies, carry different force, and are scoped to different jurisdictions and contexts; they are not interchangeable. A policy can express an intent to align with one or more of them, but alignment is not automatic compliance, and referencing a framework does not extend that framework's applicability beyond its own scope.

Having an AI policy eliminates AI-related risk.

A policy is a control measure that helps reduce and manage risk; it does not eliminate it. Governance documents establish expectations and accountability, but residual risk typically remains, and effectiveness depends on implementation, monitoring, and enforcement rather than on the existence of the document alone.

Best practices

Explicitly define the policy's scope, including whether it covers internally developed models, third-party AI, and generative or general-purpose systems, so coverage boundaries and resulting control obligations are unambiguous.

Clearly distinguish governance responsibilities from model risk management activities within the document, so that accountability and oversight structures are separated from the technical processes of risk identification, measurement, and monitoring.

When referencing external frameworks, identify the issuing body and, where you are certain, state whether each instrument is binding law, supervisory guidance, or a voluntary standard, and use qualified language where status or applicability is uncertain.

Map roles to a defined accountability structure, such as first, second, and third lines of defense, keeping their functions distinct rather than blurred.

Specify lifecycle controls that treat pre-deployment review and ongoing monitoring as separate obligations, and include change management and decommissioning expectations.

Build in periodic review and escalation procedures, and describe governance measures as risk-reducing rather than risk-eliminating to avoid overstating their effect.