5 key differences between the NIST AI RMF and ISO 42001

The AI space is developing rapidly but is still largely uncontrolled. According to The State of Trust Report 2024, 62% businesses plan to invest more in AI security in the next 12 months.

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The good news is that AI security can now be better implemented with the help of many authoritative new AI standards and frameworks rolled out in the past few years. The aim with any of these standards is to remove the uncertainty around AI systems and ensure responsible implementation.

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Among the newer voluntary AI standards, ISO 42001 and the NIST AI RMF are two particularly comprehensive options that help organizations implement industry-standard measures and controls for AI governance.

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While these two frameworks share the same end goal, they differ in some key aspects that could influence which option you pursue. In this NIST AI RMF vs. ISO 42001 analysis, we’ll offer comparative overviews of both options and cover their key differences.

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ISO 42001

ISO/IEC 42001:2023 is the world’s first AI management system (AIMS) standard aimed at organizations that provide or use AI-enabled technologies. It helps organizations implement and demonstrate controls and governance practices that showcase the responsible use of AI, increasing stakeholder trust and transparency.

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While the standard has various controls, most of them revolve around one goal—building a comprehensive AIMS that enables accountable, ethical AI implementation as well as continuous improvements.

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ISO 42001 is agnostic to an organization’s size or industry. If your organization uses AI in any capacity to deliver its products and services, you should consider complying with it.

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NIST AI RMF

The NIST AI RMF is a robust risk management framework that helps organizations effectively manage various threats and vulnerabilities associated with AI, with a focus on effective risk management.

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It was created as a response to the 2023 constitutional Executive Order in an effort to support organizations that want to uncover and mitigate AI-related risks and promote trustworthiness in AI systems.

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The framework prescribes the key functions organizations should implement to design, develop, and use AI systems responsibly and with minimal risks. The functions were designed by leveraging the input from both public and private sectors, ensuring comprehensive application across industries.

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NIST AI RMF vs. ISO 42001: Similarities and differences

The NIST AI RMF and ISO 42001 overlap in several areas, most notably:

• End goal: Both frameworks aim to mitigate the volatility of AI systems and related risks, although they may have different focus points
• Scope of application: Organizations of any size can implement ISO 42001 and the NIST AI RMF as long as their product or service scope involves the use of AI solutions
• Core benefits: Whether you implement the NIST AI RMF or ISO 42001, you can access similar benefits, such as leveraging industry-standard controls that hedge against AI risks and governance
• Challenges: Both options demand niche expertise to implement and manage AI-related controls

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Despite these similarities, ISO 42001 and the NIST AI RMF differ in five notable areas:

1. Objective and focus
2. Key principles
3. Structure
4. Certification logistics
5. Implementation cost and timeline

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Let’s break down the nuances for each.

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1. Objective and focus

The main objective of ISO 42001 is to ensure the ethical and responsible use of AI throughout business processes. This means the standard may have a broader focus, which encompasses many concerns, such as:

• AI systems development and deployment
• Customer data protection and risk management
• AI’s performance
• Organization-wide impact of AI systems

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By contrast, the NIST AI RMF primarily focuses on risk management as it relates to reliable AI development and implementation. The framework’s main requirements revolve around the development of trustworthy AI systems that help organizations minimize the inherent risks AI systems carry.

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2. Key principles

There is an overlap between the key principles of ISO 42001 and NIST AI RMF, but the subtle differences may impact your decision-making process.

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ISO 42001 relies on the following primary principles:

• Transparency: AI systems should make decisions that are transparent and free of bias
• Accountability: Organizations implementing AI must hold themselves accountable for all AI-related decisions and their consequences
• Fairness: The output generated by AI should be assessed to avoid unfair treatment of specific groups or individuals
• Explainability: Parties affected by AI implementation should get clear insights into the factors influencing the decisions, predictions, and recommendations of AI systems
• Data privacy: AI implementation must involve an elaborate data protection system that safeguards user privacy
• Reliability: AI systems must demonstrate stability and reliability

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The NIST AI RMF has slight nuances in its principles, and we can especially see a greater focus on safety. Here are the framework’s principles:

• Validity and reliability: AI deliverables should be dependable and accurate
• Safety: AI systems must prioritize safety across applications
• Security and resilience: AI systems should be protected from malicious attacks and similar risks
• Accountability and transparency: The underlying mechanism behind AI systems should be transparent and address ethical challenges in the process
• Explainability and interpretability: Users should be able to understand and interpret AI results, which enables intellectual oversight
• Privacy-enhancing: User privacy should always be protected, especially sensitive data
• Fairness: Harmful bias should be removed from an AI system’s decision-making process

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3. Structure

ISO 42001 has a comprehensive structure, consisting of 10 clauses and 4 annexes (A–D). Annex A contains 38 controls in total, including the following:

• Policies related to AI
• Internal organization structure
• Resources for AI systems
• Assessing impacts of AI systems
• AI system lifecycle
• Data for AI systems
• Information for interested parties
• Use of AI systems
• Third-party and customer relationships

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The NIST AI RMF differs considerably in structure—it offers controls spread across four core functions:

1. Govern: Establish policies, processes, accountability, and oversight functions for AI systems
2. Map: Identify and establish the context to frame risks throughout the AI lifecycle
3. Measure: Use qualitative, quantitative, and other methods to analyze and monitor AI risks
4. Manage: Mitigate the mapped and measured AI risks with appropriate risk treatment plans

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The framework encompasses 60 controls, so it’s also comprehensive and granular. That said, both ISO 42001 and NIST AI RMF offer precise control categories within their respective scopes.

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4. Certification logistics

ISO 42001 is a certifiable standard that involves an external audit. Once you pass the audit, your certification will be valid for three years and will include annual surveillance audits after the initial certification audit. Being certified comes with various benefits, such as:

• Demonstrable commitment to responsible AI operations
• Easier trust-building with customers
• Faster security reviews with new prospects
• Greater stakeholder assurance

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Unlike ISO 42001, the NIST AI RMF isn’t certifiable—the framework’s implementation involves self-attestation. That said, you may still consider engaging an external auditor so that a qualified third party can attest your NIST AI RMF implementation. You can then publicly share the validation with your partners and clients for credibility.

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5. Implementation cost and timeline

The costs related to both ISO 42001 and NIST AI RMF implementation mainly depend on the following factors:

• Organization size
• Current compliance posture
• Security program maturity
• AI implementation specifics

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Considering the several moving parts, a direct comparison may not be possible. Still, it’s worth noting that the NIST AI RMF is free to download, while ISO 42001 must be purchased. The latter also carries external audit costs, which you won’t face with the NIST AI RMF unless you opt for a third-party audit.

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As far as the timeframe is concerned, the complexity and scale of your AI system will determine how long it takes to implement the guidelines. Since NIST AI RMF has no compulsory audit layer, you might be able to implement it within six to nine months. ISO 42001’s certification timeline can be anywhere between six months to a year or even longer, depending on the size of your organization.

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Should you implement ISO 42001 or the NIST AI RMF?

Considering the overlap between ISO 42001 and the NIST AI RMF, the best practice will be to get certified for ISO 42001 while following the guidelines of NIST AI RMF in conjunction. We’re likely to see the AI industry undergo heavy regulation in the coming years, so your security team will benefit from ensuring readiness with both—since they harmonize global AI best practices.

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Based on costs alone, it might make more sense to start with the NIST AI RMF as it's free and comes with no certification commitment. You can expand to ISO 42001 as your security program matures.

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Whichever standard you decide to implement, you should consider using compliance solutions like Vanta to reduce inefficiencies with repetitive processes like security reviews, evidence collection, and internal audits.

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Vanta helps you comply with ISO 42001 and NIST AI RMF faster

Vanta offers cost-effective products for both ISO 42001 and the NIST AI RMF. These solutions come with prebuilt features and functionalities you can leverage to streamline compliance. You can expect to reduce inefficiencies with:

• Streamlined evidence collection
• Gap analyses and progress tracking
• Standard-specific (and custom) controls
• Centralized AI documentation management
• Resources like templates and policies for prescriptive guidance

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With Vanta, you can also score, prioritize, and remediate AI-related risks in a streamlined manner.

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Request a demo of the ISO 42001 or any other solution for a closer look.

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