How to Choose the Right AI Standard: A 7-Point Guide

AI adoption has accelerated across sectors today as the technology becomes easier to access and deploy. Most organizations embed it in at least one aspect of their daily operations, but doing so has also introduced new risks, such as model bias and outcome drift.

There’s a growing gap between AI use and responsible oversight and keeping up demonstrable AI governance practices is a challenge. According to a 2025 AI governance survey, more than 50% of organizations are overwhelmed by AI regulations, with shifting rules being one of the top concerns.

Choosing which AI framework to prioritize is the first step in ensuring your AI meets ethical, security, and transparency expectations. This guide will explore:

• The importance of AI compliance and non-compliance risks
• A comparison of the top AI standards relevant today
• Seven questions that help choose the right AI standard

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Why AI compliance matters

Aligning with an AI framework matters because it helps enforce accountability in operations dependent on automated decisions. Even if your organization is already compliant with privacy frameworks like the GDPR or CCPA, it doesn’t fully attest to the ethical and legal soundness of AI use. Privacy laws regulate how personal data moves and focus on the impact on the individual. AI frameworks, on the other hand, emphasize how automated systems make decisions and how they can impact both individuals and society as a whole.

The challenge is that AI tools evolve rapidly, and most organizations find it complex to update internal governance practices to ensure AI systems meet ethical, legal, and organizational standards.

Adopting the right framework(s) can make all the difference, allowing teams to adapt to changes more effectively while also enhancing security, speeding up innovation, and strengthening customer trust.

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Risks of non-compliance with AI standards and regulations

Failing to comply with AI standards and regulations carries several material risks, including financial, operational, and reputational. If you violate laws such as the EU AI Act, GDPR, or U.S.-specific data regulations, you may face substantial financial penalties or even legal action, depending on the severity of the violation. By contrast, non-compliance with voluntary AI frameworks and standards (like NIST AI RMF or ISO 42001) typically doesn’t trigger direct regulatory penalties, but it can still create commercial risk if customers expect or require alignment.

Non-compliance can also result in operational disruption and downtime caused by an unsecured AI tool. Additionally, risks like biased decisions and incorrect outputs affect business outcomes, often eroding trust with customers and partners, brand value, and competitiveness.

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Comparing standards and regulations relevant for AI use

Here are some of the most relevant AI standards and regulations currently in place that help secure AI-driven systems. Consult the table for an overview:

Name	Status	Purpose	Relevant Industry	Certification
GDPR	Mandatory	Secures the personal data of individuals in the EU	Any organization handling EU personal data	No
The EU AI Act	Mandatory	Sets baseline security and risk management requirements for AI use in the EU	EU-based organizations using AI	No
CCPA	Mandatory	Secures the personal data of California residents	Any organization handling California resident personal data	No
NIST AI RMF	Voluntary	Provides guidance to identify and mitigate AI risks	Organizations leveraging AI across all sectors	No
ISO 27001	Voluntary	Sets a framework for creating, maintaining, and updating an ISMS	Industries handling sensitive information	Yes
ISO 42001	Voluntary	Sets a framework for creating, maintaining, and updating an AIMS (AI management system)	All industries that use, develop, or provide AI-based products or services	Yes
SOC 2	Voluntary	Sets baseline requirements for protecting sensitive data (including data used by AI systems)	SaaS, cloud service providers, and other industries handling customer data	No (attestation report by auditor)

‍Organizations must be deliberate in how they choose the right framework or regulation and keep their AI use cases and broader compliance goals in mind. Over-engineering compliance efforts can easily lead to unproductive outcomes, such as frequent oversights or operational slowdown due to overwhelmed teams.

‍Scoping a framework narrowly is another risk. For example, a business might scope ISO 42001 to the AI features within a single product, only to discover midway that a large customer in another region requires a SOC 2 or ISO 27001 audit because the AI system draws on shared datasets. The remediation here would be rescoping or restarting the compliance effort, which can be expensive and disruptive. 

 

7 questions to determine your AI standard

Use these seven questions as criteria to determine which AI standards your organization should pursue:

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Question 1: Where do you operate and sell?

Geographical markers, such as your organization’s location and market area, play a major role in determining what regulations apply to you. Jurisdictions can significantly vary when it comes to legals and regulatory compliance requirements, so understand your organization’s position to help narrow focus.

‍For example, if your organization is based in the EU or targets the personal information of individuals within it, you must comply with both the GDPR and the EU AI Act. If your organization operates within the U.S., you must account for state-specific privacy laws such as the CCPA. Additionally, US agencies and critical infrastructure operators are also encouraged to lean on NIST AI RMF, which informs responsible AI use.

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Question 2: What role do you play in the AI value chain?

Your role in the AI value chain highlights which parts of your system and operations need greater security efforts. 

‍For instance, if you’re a service provider, you should primarily focus on lifecycle controls, including documentation, testing, and version management. Conversely, deployers mostly focus on use-case risk, data handling, and human oversight. 

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Question 3: What’s the risk profile of your AI use cases?

Assess the likelihood and type of risks your AI systems may face. If your findings show you operate in a high-risk environment or if you provide AI-driven services that touch critical infrastructure such as energy or transportation, you should pursue a structured risk management framework such as ISO/IEC 23894:2023.

‍If the risks are lower, you can explore other options, such as NIST AI RMF, to strengthen AI security.

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Question 4: Do customers require certification?

Compliance with frameworks like NIST AI RMF will help strengthen your AI security, but alignment doesn’t provide you with an official certification. If your primary customer base expects stronger assurance or proof of compliance, you should prioritize certifiable standards such as ISO/IEC 42001.

‍Pursuing certifiable standards will impact overall compliance costs and effort due to stricter readiness work and higher auditor fees, among other things.

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Question 5: What’s your current governance maturity? 

If your organization’s governance maturity is still at the ad-hoc level, alignment with NIST AI RMF can help you build a strong foundation. For more mature governance structures, ISO 42001 enables smoother scaling of your existing controls. It’s flexible enough to be adapted for smaller organizations and also sends a strong message that your organization is more mature and proactive in terms of managing AI risk.

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Question 6: What data are you touching?

The type of information your organization handles influences your choice. If you handle sensitive, personal, regulated, or critical infrastructure–related information, focus on frameworks that emphasize data privacy and security. This includes not just the data itself but also AI-driven outputs or decisions that affect outcomes.

You can consider regulations such as the GDPR and CCPA, as well as frameworks like ISO 27001 and SOC 2. Prioritize frameworks that include AI-specific nuances, such as clear requirements for ethical use of data, privacy protections, and human validation for AI outputs.

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Question 7: Build or buy?

There’s a notable difference between building your own AI tools and relying on third-party services. Creating your own AI software means you must focus on internal risks and embed safety practices at every stage of development.

‍If you use third-party software, vendor risk management (VRM) becomes a critical part of safeguarding sensitive data and ensuring safe AI behavior. This includes both securing the solution once it's implemented, but also ongoing due diligence, which involves reviewing model safety disclosures, assessing how vendors handle training data, and evaluating their AI monitoring practices.

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Which AI framework to choose after assessment

Depending on your answers to the questions above, you’ll likely narrow down to three AI-focused options like:

• ISO/IEC 42001: Opt for this if you need a certifiable, auditable AI management system that procurement recognizes
• NIST AI RMF: Start here if you need a practical operating model and artifacts quickly, but treat it as scaffolding for your overall AI governance program
• EU AI Act: Run an EU AI Act workstream if you sell or operate in the EU or have high‑risk use cases, but tailor it to your role in the AI value chain

‍Before you dive in deep and implement a framework, you can also consider refreshing the trust baselines you have built with other frameworks such as SOC 2 or ISO 27001, and identify relevant AI risks. Once you’ve established strong security and privacy baselines, you can choose and layer on the AI governance processes that make the most sense for your organization.

Note: The governance controls identified in these frameworks aren’t mutually exclusive—there’s overlap, and many of your efforts will be reusable. Define your right-sized journey; you can start with NIST AI RMF and evolve into ISO 42001 certification later, while preparing for EU AI Act alignment if you don't yet have EU customers.

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Challenges of pursuing AI compliance

Pursuing AI compliance comes with its own set of operational and governance challenges. These include:

• Changing risk landscape: AI technologies evolve rapidly, making risks such as model drift, bias amplification, and data poisoning constant threats. Mitigating these requires regular AI reviews to verify the effectiveness of your controls.
• Need for real-time monitoring: AI systems can change rapidly, so point-in-time insights aren’t effective in spotting gaps in AI systems. To catch issues early on, organizations need to embed real-time monitoring into their AI workflows.
• Documentation requirements: Most AI standards require maintaining high volumes of documentation about decision outcomes, version histories, training procedures, and ethical considerations. Gathering this evidence manually can be time-consuming and strain resources.
• Quick response to regulatory changes: The AI compliance landscape evolves quickly. Staying compliant requires organizations to evaluate regulatory updates, scope operational impact, and address gaps without delay, which can be tricky for busy teams.

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Building an effective AI governance approach

AI governance is quickly becoming a core part of responsible AI adoption, but there is no single framework that fits every organization. The right approach depends on factors like regulatory exposure, risk profile, customer expectations, and internal maturity. Rather than trying to adopt multiple standards at once, organizations are better served by building a focused, right-sized strategy that aligns with their specific use cases.

In practice, this often means strengthening existing security and privacy foundations first, then layering in AI-specific controls around transparency, accountability, and risk management. Because many frameworks overlap, efforts such as documentation, monitoring, and governance processes can often be reused, making a phased approach more practical and sustainable.

Ultimately, effective AI governance is not a one-time exercise. As AI systems and regulations continue to evolve, organizations need ongoing monitoring, clear documentation, and adaptable processes. Those that take an iterative, structured approach will be better positioned to balance innovation with accountability and maintain trust in their AI systems.