Your Guide to ISO 42001 Controls for AI Governance

Investors have been asking companies the tough question: How strong is your AI adoption and usage? Employees are under pressure to rethink their workflows, use the right tools, cut costs, and maximize efficiency. But hardly anyone talks about the governance side of it, when in fact, AI risks are not in the models you use but in how you govern and monitor them.

Enter ISO/IEC 42001: the world’s first AI management system standard. At its core are 38 controls designed to help organizations build AI that’s not just smart, but safe, fair, and audit-ready. From handling model bias and data quality to enforcing oversight and transparency, these controls structure AI governance without slowing innovation.

In this guide, we break down the list of ISO 42001 controls, how they differ from other standards like ISO 27001, what implementation really looks like, and how Sprinto helps teams tackle compliance with speed, clarity, and confidence.

What are ISO 42001 controls?

ISO 42001 controls are a set of guidelines and requirements for designing and managing AI systems that follow ethical practices and ensure compliance. These controls focus on AI governance, risk management and system lifecycle management to ensure trustworthy and transparent Artificial Intelligence (AI) Management Systems (AIMS).

Who is responsible for implementing ISO 42001 controls?

The responsibility for implementing ISO/IEC 42001 controls is shared across multiple roles within an organization and is not just an IT initiative. Depending on the control and governance structure, the key stakeholders include:

• Leadership & Executives: Set the vision for AI governance, allocate resources, and ensure alignment with broader business strategy and ethical commitments.
• Product & Engineering Teams: Embed AI controls into the design and development lifecycle, ensuring responsible AI is not bolted on but built in.
• AI Practitioners: Conduct impact assessments, manage model training, and validate fairness, transparency, and explainability in outputs.
• IT & Security Teams: Ensure technical controls are implemented securely and that infrastructure supports safe and compliant AI operations.
• Compliance & Risk Officers: Interpret ISO/IEC 42001 clauses and controls, maintain documentation, and monitor adherence to regulatory obligations.
• Legal & Privacy Teams: Evaluate legal risks, support policy development, and ensure AI systems comply with data protection and ethical guidelines.
• People & HR Teams: Promote AI awareness, train employees, and address the human side of responsible AI, such as no bias, accountability, and usage ethics.
• All Employees: Everyone interacts with AI in some capacity. Building a culture of responsible AI starts with ongoing awareness and role-based accountability.

A systematic look at ISO 42001 control objectives and domains

ISO 42001 follows a high-level structure with standardized format, clauses and control structure just like other ISO standards like ISO 27001 or ISO 9001.

At a broader level, ISO 42001 has four pillars:

Annex A: Reference Control Objectives and Controls

This is the meat of the standard. It contains:

• 9 control objectives that frame what good AI governance looks like.
• 38 ISO 42001 controls list that organizations can implement to manage AI risks, from development to decommissioning.

Annex B: Implementation Guidance for AI Controls

Think of this as the playbook. It explains how to apply the 38 controls in Annex A, with real-world guidance tailored for the complexities of AI systems.

Annex C: AI-Related Organizational Objectives and Risk Sources

This annex identifies why AI needs special treatment. It outlines:

• Potential organizational objectives for AI (e.g., automation, personalization).
• Unique risk sources (e.g., model drift, bias, explainability gaps) which are a crucial resource for risk assessments and stakeholder alignment.

Annex D: Cross-Sector Applicability

This annex shows how the standard applies across sectors whether you’re in fintech, health tech, SaaS, or anything in between. It underscores ISO 42001’s flexibility in different organizational and regulatory contexts.

ISO 42001 Clauses

The first 3 clauses of ISO 42001 are generic and talk about scope, normative references, and terms and definitions. Clauses 4-10 particularly hold importance:

Clause	Purpose
4. Context of the Organization	Understand internal and external AI-related issues, stakeholder expectations, and define the scope of your AI Management System
5. Leadership	Ensure leadership commitment, set AI policy, and assign roles and responsibilities.
6. Planning	Identify AI-related risks and opportunities. Set objectives and plans to manage them.
7. Support	Allocate resources, build AI competency, and manage documented information.
8. Operation	Implement and control processes needed to meet AIMS requirements, especially for developing and managing AI systems.
9. Performance Evaluation	Monitor, measure, analyze, and audit AI performance and compliance. Conduct management reviews.
10. Improvement	Address nonconformities, take corrective action, and drive continual improvement of the AIMS.

ISO Annex A: Control objectives and domains

The standard organizes its 38 controls across 9 domains. Each domain corresponds to a key area of AI lifecycle or governance. Here’s a quick overview of ISO 42001 domains and controls:

A.2 – Policies Related to AI

Objective: Provide management direction and support for AI systems aligned with business requirements.

Controls:

• A.2.1: Establish an AI policy.
• A.2.2: Ensure alignment with other organizational policies.
• A.2.3: Regularly review the AI policy.

A.3 – Internal Organization

Objective: Establish accountability within the organization for responsible AI implementation and management.

Controls:

• A.3.1: Define AI roles and responsibilities.
• A.3.2: Establish procedures for reporting concerns related to AI systems.

A.4 – Resources for AI Systems

Objective: Ensure comprehensive documentation and management of resources critical to AI systems.

Controls:

• A.4.1: Document AI system components and assets.
• A.4.2: Manage data resources, including provenance and quality.
• A.4.3: Oversee tooling resources used in AI development.
• A.4.4: Manage system and computing resources.
• A.4.5: Ensure human resources have the necessary competencies.

A.5 – Assessing Impacts of AI Systems

Objective: Evaluate potential impacts of AI systems on individuals, society, and systems.

Controls:

• A.5.1: Conduct impact assessments for AI systems.
• A.5.2: Document and address identified impacts.

A.6 – AI System Life Cycle

Objective: Implement controls across the AI system’s life cycle, from design to decommissioning.

Controls:

• A.6.1: Manage AI system design and development processes.
• A.6.2: Oversee testing and validation procedures.
• A.6.3: Ensure proper deployment and maintenance.
• A.6.4: Plan for system decommissioning.

A.7 – Data for AI Systems

Objective: Ensure data quality, provenance, and management for AI systems.

Controls:

• A.7.1: Establish data governance policies.
• A.7.2: Manage data collection and processing.
• A.7.3: Ensure data quality and integrity.
• A.7.4: Address data privacy and security concerns.

A.8 – Information for Interested Parties

Objective: Provide relevant information about AI systems to stakeholders.

Controls:

• A.8.1: Communicate AI system purposes and functionalities.
• A.8.2: Disclose AI system limitations and risks.
• A.8.3: Engage stakeholders in AI system development and deployment.

A.9 – Use of AI Systems

Objective: Guide the responsible use and monitoring of AI systems.

Controls:

• A.9.1: Define acceptable use policies for AI systems.
• A.9.2: Monitor AI system performance and behavior.
• A.9.3: Implement feedback mechanisms for continuous improvement.

A.10 – Third-Party & Customer Relationships

Objective: Manage relationships with external parties involved in AI systems.

Controls:

• A.10.1: Assess third-party AI system providers.
• A.10.2: Establish contractual agreements outlining AI responsibilities.
• A.10.3: Monitor third-party AI system performance and compliance

ISO 42001 Controls Vs ISO 27001 controls: Shared Structure, Different mission

ISO 42001 and ISO 27001 follow the same high-level structure. Both require you to define scope, assess risk, implement controls, and continuously improve. So if you’re ISO 27001 compliant, you’re already fluent in the language of structured risk management. However, that’s where the similarity ends.

ISO 27001 is built to manage information security risks, such as data breaches, unauthorized access, and system vulnerabilities.

ISO 42001 focuses especially on AI-specific risks, such as bias, model drift, lack of explainability, and unintended consequences.

What’s different?

Here’s where ISO 42001 controls are different from ISO 27001 controls:

ISO 27001	ISO 42001
Focuses on data security	Focuses on AI safety, ethics, and transparency
Controls for systems and access	Controls for AI models, data provenance, lifecycle
Concerned with confidentiality	Concerned with bias, autonomy, and impact
Technical implementation focus	Cross-functional by design: legal, product, eng, ops

How to implement ISO 42001 controls?

ISO 42001 control implementation does not require you to reinvent the wheel. Just do an audit of AI systems, assess risks, draft AI policies and implement the right controls.

Here are the details on ISO 42001 implementation:

Define the scope of AI use

Start by identifying where AI systems exist across your organization. This may include internal tools such as ML models that power automation, customer-facing features such as AI-powered recommendations, third-party AI integrations, etc. The scoping exercise helps you set boundaries for your AI Management System (AIMS) and identify the systems, processes, and teams it must cover.

Run an AI-specific risk assessment

Use Annex C of ISO 42001 to discover and map common risk scenarios with AI systems. These can include bias, loss of transparency, poor data governance or quality, model drift over time etc. For each identified risk, evaluate the likelihood and impact to classify items as high, medium or low risks. Once scored, map them to relevant ISO 42001 controls. For example, a bias risk can be solved by A.7 (data governance).  Not all controls will apply and that’s fine. Just tailor the implementation as per requirements.

Align policies and ownership

Draft policies that specifically cover AI governance ie. how AI systems are developed, trained, validated, monitored and retired. Also ensure org-wide distribution and acknowledgement. Next, assign ownership across functions. ISO 42001 demands cross-functional accountability so your data science, engineering, legal, and compliance teams should all be looped in.

Map controls to AI lifecycle

Map relevant controls to each phase of your AI system’s lifecycle:

• Design & development: Apply controls on training data quality, model objectives, and fairness.
• Testing & validation: Introduce explainability, robustness, and performance benchmarks.
• Deployment: Define usage parameters, fallback options, and human oversight.
• Monitoring: Track for anomalies, ethical drift, and real-world behavior.
• Decommissioning: Build structured offboarding for legacy or failing AI systems.

Build supporting infrastructure

You’ll need more than policies. ISO 42001 expects operational proof including documentation, system logs, audit trails, access records, training completions. This is where infrastructure matters. Where possible, reuse what you’ve built for ISO 27001. If your compliance stack already supports access control, training, or vendor risk workflows, use it. Don’t reinvent unless you have to.

Monitor, review and improve

Once your AIMS is in place, schedule regular reviews of AI policies, system behavior, risk posture, and stakeholder impact. If your model starts generating drift or stakeholder feedback shifts, your controls must adapt. Keep iterating and improving to ensure your AI governance stays relevant.

Common challenges in ISO 42001 control implementation and how to address them

ISO 42001 is still new territory for most organizations and like any new standard, it brings a learning curve. From unclear ownership to misunderstood risks, here are the most common implementation challenges:

1. Struggling to Locate “Hidden” AI Systems

One of the biggest hurdles in implementing ISO 42001 is recognizing where AI exists. Unlike traditional IT systems, AI often hides in features and decision logic or is integrated via third-party APIs. It rarely comes with a transparent “AI system” label, which makes scoping and AI risk management tricky.

How to fix it: Start with a discovery audit. Map where automated decision-making is happening internally and externally. Look beyond your in-house models. If something influences decisions autonomously, it’s likely in scope.

2. Assuming ISO 27001 is Enough

Another common challenge is trying to force-fit ISO 27001 processes into ISO 42001. While both follow the same structural blueprint, their purposes are entirely different. ISO 27001 protects data and infrastructure; ISO 42001 governs behavior, outcomes, and accountability in AI systems. Applying the same lens to both leaves critical AI risks like bias, drift, or lack of oversight unaddressed.

How to fix it: Build on your ISO 27001 base but layer in new thinking. ISO 42001 focuses on outcomes, autonomy, explainability, and impact. Run an AI-specific risk assessment. Focus on what your models do, not just how your data is stored.

3. Cross-Functional Ownership Gaps

Cross-functional collaboration is crucial for ISO 42001 compliance and companies that think security and legal can handle it by themselves set themselves up for failure. AI systems are built by engineering, trained on ops data, monitored by product, and governed by compliance. The Implementation gets impacted or misses key risks without buy-in from data science, product, HR, and beyond.

How to fix it: Assign control-level ownership across functions. Treat AI governance as a team sport. Without shared accountability, compliance misses blind spots.

4. No Playbook for Impact Assessments

For many compliance teams, concepts like bias, fairness, and explainability are unfamiliar territory. Unlike access logs or encryption standards, these are subjective and open to interpretation, and that uncertainty leads to inaction. Teams often skip these controls not out of neglect, but because there’s no clear playbook. But ISO 42001 makes them mandatory, not optional.

How to fix it: Don’t overengineer. A simple, repeatable AI impact assessment process is enough. Ask yourself what decisions are being made. Who’s affected? Can they challenge the outcome? Document the process and use it across systems and processes.

5. Compliance Drifts

AI systems are not static. They degrade as models drift, data changes, and assumptions break. So ISO 42001 requires continuous oversight. When companies only check for compliance during audits, the issues go unnoticed and create more significant problems.

How to fix it: Treat compliance as continuous. Set alerts for model performance shifts, track data quality and monitor how systems behave over time not just at launch.

6. Vendor and supply chain risk

Most companies use third-party AI tools or even open-AI models (think LLMs like GPT) and don’t create all AI systems from scratch. However, according to ISO 42001, you’re still responsible for everything AI and must have technical, organizational and administrative guardrails in place. So ensuring vendor compliance and accountability is another challenge.

How to fix it: Treat AI vendors like critical parts of your system, not just plug-and-play tools. Risk assessments on their outputs, monitor performance and, where possible, keep a human in the loop for oversight.

How can Sprinto automate ISO 42001 controls?

ISO 42001 is new, but your approach doesn’t have to be messy, fragmented, or resource-heavy. With Sprinto, you get a purpose-built compliance automation platform already equipped to support ISO 42001 workflows.

For most companies, Sprinto can handle ISO 42001 implementation end-to-end. From control mapping and risk assessments to evidence capture and continuous monitoring, we’ve built the infrastructure to help you meet AI governance standards without slowing down your teams.

If your company is building and deploying its own AI models, especially in high-stakes or regulated industries, we may bring in a trusted implementation partner. This helps address deeper AI-specific risk modeling and lifecycle monitoring needs.

But for everyone else? Sprinto has you covered.

Whether you’re adding ISO 42001 to an existing ISO 27001 stack or starting fresh, Sprinto helps you move fast, stay accurate, and prove control at every step.
Ready to get ahead of AI compliance? Talk to an expert to kickstart your journey.

FAQs

1. Is ISO 42001 certification mandatory for companies using AI?

No, it’s voluntary but it’s becoming a strong signal of AI maturity and risk awareness. Regulatory bodies, enterprise buyers, and risk teams are starting to treat ISO 42001 the way they treat ISO 27001 for security: a mark of trust, and in some cases, a procurement requirement.

2. Can ISO 42001 be implemented without full certification?

Yes. Many companies choose to adopt the controls and align with ISO 42001 principles without going through formal certification. This still strengthens governance, reduces risk, and prepares the organization for future audits or regulatory reviews.

3. Does ISO 42001 apply only to companies building their own AI models?

Not at all. ISO 42001 is just as relevant for companies using third-party AI tools. If an external model influences your decisions, customer experience, or data, you’re accountable for how it’s governed even if you didn’t build it.

4. How long does it typically take to implement ISO 42001 controls?

It depends on your AI footprint and existing compliance posture. If you’re already ISO 27001-compliant and your AI systems are limited in scope, implementation can be fairly quick, often within a few months. For organizations with complex or custom AI pipelines, the timeline can extend, especially around impact assessments and monitoring infrastructure.