AI Risk Management Frameworks: How to Choose the Right Approach

Organizations adopting artificial intelligence often face a deceptively difficult question: which AI risk management framework should we use? The challenge is not a lack of available guidance. It is that different frameworks and standards are designed for different purposes.

A framework that helps structure AI risk-management activities may not provide the organization-wide governance structure required for a formal management system. Similarly, a voluntary framework or international standard does not automatically replace legal or regulatory requirements.

The right choice depends on factors such as the organization's role in the AI lifecycle, risk profile, governance maturity, regulatory environment, existing management processes, and implementation capability. Some organizations may need one primary approach, while others may benefit from combining complementary frameworks and standards.

In this blog, you will learn how to compare AI risk management frameworks, assess which approach fits your organization, understand the differences between NIST AI RMF, ISO/IEC 42001, and ISO/IEC 23894, and determine when combining complementary approaches makes sense.

Key Takeaways

• AI risk management frameworks serve different organizational purposes.

• Framework selection should follow business, AI, risk, governance, and regulatory needs.

• NIST AI RMF provides flexible, voluntary guidance for managing AI risks.

• ISO/IEC 42001 provides requirements for establishing and continually improving an Artificial Intelligence Management System (AIMS).

• ISO/IEC 23894 provides AI-specific guidance for integrating risk management into AI-related activities and functions.

• Regulatory requirements must be considered alongside voluntary frameworks and standards.

• Combining complementary approaches can be effective when responsibilities, requirements, and controls are deliberately mapped.

• The right approach is the one that fits the organization's objectives, risk profile, governance maturity, and operating environment.

What Are AI Risk Management Frameworks, and Why Does Framework Choice Matter?

An AI risk management framework provides a structured way to identify, assess, prioritize, treat, and monitor risks associated with artificial intelligence. It can help organizations establish consistent practices for understanding AI risks and integrating risk considerations into decisions across the AI lifecycle.

However, not every framework, standard, or regulation performs the same function.

It is useful to distinguish three related concepts:

For example, NIST AI RMF is a voluntary framework designed to help organizations manage AI risks. ISO/IEC 42001, by contrast, specifies requirements for establishing, implementing, maintaining, and continually improving an Artificial Intelligence Management System (AIMS). ISO/IEC 23894 focuses specifically on guidance for managing AI-related risks and integrating risk management into AI-related activities.

This distinction matters because choosing an approach that does not match the organization's needs can create unnecessary bureaucracy, duplicated controls, unclear ownership, or gaps between policy and operational practice.

The key question is therefore not

Which AI risk management framework is the best?

It is:

Which framework, standard, or combination of approaches provides the right governance and risk-management architecture for our organization?

What Should You Consider Before Choosing an AI Risk Management Framework?

Framework selection should begin with the organization rather than with the framework itself. Before choosing an approach, assess the following factors:

1. Primary objective: Are you looking for practical risk-management guidance, formal AI governance, certification readiness, regulatory support, or a combination?

2. AI role: Does the organization develop, provide, deploy, integrate, or use AI systems?

3. AI risk profile: What types of risks could arise from the organization's AI systems and their intended uses?

4. Governance maturity: Does the organization already have established risk, security, privacy, quality, or compliance management processes?

5. Regulatory and contractual requirements: Which laws, regulations, industry requirements, customer commitments, or procurement conditions apply?

6. Implementation capability: Does the organization have the people, expertise, resources, and governance structure needed to operationalize the chosen approach?

7. Evidence and improvement: How will the organization document decisions, monitor performance, evaluate controls, and improve its AI risk-management practices?

Match the Framework to the Organization, Not the Other Way Around

The framework should support how the organization actually operates.

For example, an organization looking for a flexible way to structure AI risk-management activities may not need the same level of management-system formalization as an organization seeking an organization-wide AI governance system.

Similarly, an organization already operating established ISO management systems may benefit from integrating AI governance into its existing management-system architecture rather than creating an isolated AI risk process.

Framework selection is therefore a governance decision, not simply a compliance purchase.

AI Risk Management Frameworks at a Glance

The major approaches considered in this article answer different questions:

This comparison highlights why there is no universal winner. Each approach addresses a different organizational need.

NIST AI RMF: When Is a Flexible Risk Management Framework the Better Fit?

The NIST Artificial Intelligence Risk Management Framework (AI RMF) was developed to help organizations designing, developing, deploying, or using AI systems manage AI risks and promote trustworthy and responsible AI. NIST describes the framework as voluntary, rights-preserving, non-sector-specific, and use-case agnostic.

The AI RMF Core is organised around four functions:

• Govern: Establish and maintain organisational structures, policies, and practices for AI risk management.

• Map: Establish context and identify risks associated with AI systems and their intended uses.

• Measure: Assess, analyze, and monitor identified AI risks.

• Manage: Prioritise and respond to identified risks.

These functions are not intended to operate as a rigid checklist or a mandatory sequence. NIST describes governance as a cross-cutting function that informs the other three functions, while AI risk management should remain continuous throughout the AI system lifecycle.

NIST also provides an AI RMF Playbook containing suggested actions and references for implementing the framework. The Playbook is voluntary and is not intended to be a checklist that organizations must follow in its entirety.

When NIST AI RMF May Be the Better Fit

NIST AI RMF can be particularly useful when an organization:

• wants a flexible AI risk-management structure;

• needs practical guidance without adopting a formal management system;

• wants to establish common AI risk terminology and practices;

• is developing AI governance processes;

• needs an adaptable approach across different AI use cases; or

• Wants to complement existing governance, security, privacy, or compliance processes.

One important consideration is currency: NIST AI RMF 1.0 is currently being revised. Organizations using the framework should therefore monitor NIST updates and assess how future revisions affect their implementation approach.

ISO/IEC 42001: When Does an AI Management System Approach Make More Sense?

ISO/IEC 42001: 2023 is an international standard that specifies requirements for establishing, implementing, maintaining, and continually improving an Artificial Intelligence Management System (AIMS). It applies to organisations of different sizes and across industries that develop, provide, or use AI-based products or services.

The distinction between ISO/IEC 42001 and a standalone AI risk framework is important.

An AIMS is not simply an AI risk register or a collection of risk assessments. It provides an organisation-wide structure for establishing AI-related policies and objectives and implementing processes to achieve them.

ISO/IEC 42001 addresses areas including:

• organisational context and leadership;

• AI policy and objectives;

• planning and risk management;

• operational processes;

• performance evaluation;

• monitoring and review; and

• continual improvement.

The standard follows a management-system approach based on continual improvement, helping organizations move from ad hoc AI practices toward a structured and accountable governance model.

When ISO/IEC 42001 May Be the Better Fit

ISO/IEC 42001 may make more sense when an organization:

• wants a formal AI management system;

• needs organization-wide AI governance;

• wants structured responsibilities and processes;

• already uses management-system standards;

• needs stronger evidence of governance and continual improvement;

• wants to integrate AI risk management into broader organizational processes; or

• is considering conformity assessment or certification.

ISO/IEC 42001 is not automatically a better choice than NIST AI RMF. It provides a different type of structure and therefore requires a greater organizational commitment to management-system implementation.

Where ISO/IEC 23894 Fits

ISO/IEC 23894:2023 Information technology: Artificial intelligence Guidance on risk management provides guidance for organizations that develop, produce, deploy, or use AI products, systems, and services. It focuses specifically on managing AI-related risks and integrating risk management into AI-related activities and functions.

This makes its role different from ISO/IEC 42001:

• ISO/IEC 23894 → AI-specific risk-management guidance.

• ISO/IEC 42001 → requirements for an organization-wide AI management system.

The two approaches can therefore be complementary rather than interchangeable.

NIST AI RMF vs ISO/IEC 42001: Key Differences

NIST AI RMF and ISO/IEC 42001 are often compared because both support responsible AI governance and risk management. However, they should not be treated as equivalent frameworks.

The practical distinction is straightforward:

NIST AI RMF helps organisations structure how they manage AI risks. ISO/IEC 42001 provides a management-system structure for governing AI across the organisation.

An organisation may therefore use NIST AI RMF practices within a broader management-system environment rather than treating the two as mutually exclusive.

Should Organisations Choose One Framework or Combine Multiple Approaches?

Organisations do not always need to choose a single framework.

In some cases, combining complementary approaches can provide a stronger governance architecture. The objective, however, should be complementarity rather than duplication.

For example, an organisation could use:

• ISO/IEC 42001 to establish its organisation-wide AI management system;

• NIST AI RMF to structure practical AI risk-management activities; and

• ISO/IEC 23894 to provide additional AI-specific risk-management guidance.

This does not mean every organisation should implement all three.

The right combination depends on the organisation’s objectives, existing management systems, regulatory environment, AI use cases, resources, and assurance requirements.

How to Combine Frameworks Without Creating Duplication

A combined approach should clearly map:

1. Requirements: What does each framework or standard require or recommend?

2. Processes: Which organizational processes address those requirements?

3. Controls: Which controls or practices manage the relevant risks?

4. Ownership: Who is responsible for implementation and oversight?

5. Evidence: What records demonstrate that activities were performed?

6. Review: How will effectiveness and improvement be evaluated?

A simple mapping exercise can reveal overlapping requirements and help the organisation avoid creating separate processes for essentially the same risk.

The goal should be an integrated governance architecture rather than multiple disconnected compliance programmes.

How Regulatory Requirements Should Influence Framework Selection

Framework selection should never be separated from the legal environment in which an organisation operates.

A voluntary framework does not become legally binding simply because an organisation adopts it. Likewise, implementing an international standard does not automatically demonstrate compliance with every applicable AI regulation.

Organisations should therefore determine their legal and contractual obligations first and then use appropriate frameworks and standards to support implementation.

Frameworks Do Not Replace Legal Requirements

The EU AI Act illustrates this distinction.

For high-risk AI systems, Article 9 establishes requirements for a risk-management system. The regulation requires providers to establish, implement, document, and maintain a risk-management system that is intended to operate as an ongoing and iterative process throughout the lifecycle of the high-risk AI system.

An organisation subject to such requirements should therefore begin by determining whether and how the regulation applies to its AI systems.

It can then assess how NIST AI RMF, ISO/IEC 42001, ISO/IEC 23894, or other standards can support the organisation’s compliance and governance activities.

The sequence is important:

Legal obligations first → organizational requirements second → framework and standards selection third → implementation and evidence fourth.

This avoids the common mistake of assuming that adopting a recognized framework automatically equals regulatory compliance.

How to Choose the Right AI Risk Management Framework

A practical selection process can be reduced to seven steps.

1. Define the Objective

Start by identifying what the organization needs to achieve.

Is the objective to:

• establish flexible AI risk-management practices;

• create formal AI governance;

• integrate AI risks into existing management systems;

• prepare for assurance or certification;

• address regulatory requirements; or

• combine several of these objectives?

A clearly defined objective makes framework selection considerably easier.

2. Establish the Scope

Determine which AI systems, business functions, products, services, teams, and processes fall within scope.

Consider whether the organisation is acting as an AI developer, provider, deployer, user, or in multiple roles.

The scope should also consider the AI lifecycle and the relationships between internal teams and external suppliers.

3. Determine Applicable Obligations

Identify relevant:

• laws and regulations;

• industry requirements;

• contractual obligations;

• customer requirements;

• internal policies; and

• assurance expectations.

Do not assume that a voluntary framework or standard covers every legal obligation.

4. Assess the Required Level of Structure

Ask whether the organisation needs:

Flexible guidance → NIST AI RMF may be appropriate.

AI-specific risk guidance → ISO/IEC 23894 may be useful.

A formal organisation-wide management system → ISO/IEC 42001 may be more appropriate.

Multiple governance and risk objectives → A combination may make sense.

5. Review Existing Processes

Look at the organisation’s existing:

• enterprise risk management;

• information security;

• privacy;

• data governance;

• quality management;

• compliance;

• internal audit; and

• supplier-management processes.

The selected approach should integrate with these processes wherever possible rather than creating unnecessary parallel structures.

6. Select and Document the Approach

Document why the organisation selected the framework, standard, or combination of approaches.

A framework decision should identify:

• scope;

• objectives;

• applicable requirements;

• selected framework or standards;

• supporting processes;

• responsibilities;

• controls;

• evidence requirements; and

• review mechanisms.

This creates a defensible basis for future governance and assurance activities.

7. Establish Ownership and Review

Framework selection is not a one-time exercise.

Assign clear ownership for implementation, monitoring, internal review, and continual improvement.

AI technologies, organizational priorities, regulatory requirements, and risk profiles can change. The governance approach should therefore be reviewed periodically and updated when necessary.

Which AI Risk Management Framework Should You Choose?

The decision can be simplified into the following model:

This is not a prescriptive ranking. The appropriate choice depends on the organization's context.

A useful decision flow is:

Define objective → establish scope → identify legal requirements → assess governance maturity → select framework or combination → map requirements and controls → assign ownership → monitor and improve.

The framework provides the architecture. People, processes, controls, evidence, and decision-making make that architecture operational.

Conclusion

The best AI risk management framework is not necessarily the most comprehensive, popular, or widely recognized option. It is the approach that best fits the organization's objectives, AI risk profile, governance maturity, regulatory environment, and implementation capability.

NIST AI RMF is well suited to organizations seeking flexible, voluntary guidance for managing AI risks. ISO/IEC 42001 is designed for organizations that need a formal Artificial Intelligence Management System with structured governance and continual improvement. ISO/IEC 23894 provides focused guidance for integrating AI risk management into AI-related activities and functions.

These approaches do not necessarily compete. They can be combined when their roles are clearly defined and overlapping requirements are deliberately mapped.

The most effective approach is therefore to assess organizational needs first, choose the appropriate governance architecture second, and integrate the selected framework or standards into existing processes rather than treating them as standalone compliance exercises.