What is ISO 42001? And Why Are We Talking About It Now?

If you’ve been paying attention to the news, you’ve probably noticed that AI regulation is a hot topic in everyone’s mind — from government officials to business leaders to customers. And with good reason.

The rapid rise of Generative AI (GenAI) tools, such as large language models (LLMs), facial recognition systems, and real-time geolocation technology, has outpaced existing regulations. 

What’s more? The private sector’s investment in AI is skyrocketing — now 18 times higher than a decade ago.

AI is no longer just a buzzword. It’s a key driver of economic growth and a critical enabler of public services. This brings us to ISO/IEC 42001, a brand-new AI regulatory framework designed to address the challenges of artificial intelligence’s rapid rise.

So, what’s this new ISO 42001 certification all about? Why do we need it? And what could it mean for organizations leveraging AI? Let’s dive in.

💡 Spoiler Alert: The days of “move fast and break things” in AI development might be over. ISO 42001 aims to bring structure and accountability to the wild west of AI innovation.

What is ISO 42001?

ISO 42001 is a framework for ensuring responsible AI management, whether building, using, or selling.

Why do we need this? AI comes with some unique challenges that traditional IT systems don’t. Let me explain:

Automatic decision-making

AI systems can make decisions independently, sometimes in hard-to-main or even understand ways. For example, let’s say an AI system decides who gets a loan — if you can’t explain why it said “yes” to one person and “no” to another, that’s a problem. 

That’s a bit scary, right? So, companies need specific management practices to ensure these decisions are responsible and ethical.

Data-driven learning

Traditional software runs on rules written by people. AI, on the other hand, often learns from data. That’s a different process, and it comes with its challenges, like making sure the system doesn’t pick up biases from that data.

Continuous learning

Some AI systems can learn and change their behavior on the fly. What you launch on Day 1 might evolve by Day 100 without human intervention. That’s powerful, but organizations must monitor these systems to ensure they work as intended.

Hence, the need for ISO 42001 certification arises. It creates specific guardrails to ensure that AI is used ethically, fairly, and transparently. 

Who Needs ISO/IEC 42001?

ISO/IEC 42001 applies to any organization that works with AI, whether it develops AI systems, uses AI in its products, or provides AI-based services. This could be tech companies, public sector agencies, or even non-profits. If you’re touching AI in any way, this standard can be relevant.

As for the legality part, it’s not legally required right now. No law anywhere says a business must comply with ISO 42001. But that doesn’t mean it won’t become mandatory in the future. Governments are already working on AI regulations and could base their laws on frameworks like ISO 42001. So we’d recommend you get started now!

Key Structure of ISO 42001

Different clauses in ISO 42001 define the structure and elements. They are:

1. Scope: Defines what the AI governance standard, which covers – ethical AI practices, risk management, transparency, and accountability.
2. Normative References: Points to standards like ISO 27001 (information security) and ISO 38507 (AI governance principles).
3. Terms and Definitions: Clarifies key terms like “AI system,” “bias,” “explainability,” and “model drift.”
4. Context of the Organization: Establishes the organization’s internal and external AI-related risks, stakeholder expectations, and regulatory landscape.
5. Leadership: This section outlines the leadership’s role in creating AI governance policies, assigning responsibilities, and setting ethical AI objectives.
6. Planning: This focus is on how the organization identifies risks, biases, and regulatory requirements and sets plans to address them.
7. Support: Covers necessary resources, competence, awareness, communication, and documentation to ensure effective AI governance.
8. Operations: This section details the processes for AI system development, deployment, monitoring, and risk management throughout the AI lifecycle.
9. Performance Evaluation: Requires regular monitoring, measuring, and auditing of AI systems to ensure they remain compliant and risk-free.
10. Improvement: Focuses on continual improvement of AI governance practices based on internal audits, incidents, and new regulations.

Clauses & Requirements of ISO 42001

Some clauses in ISO 42001 certification are important for organizations to comply with certification requirements.

Those clauses are:

Clause 4: Context of the Organization 

This clause helps you understand the internal and external factors that impact your AI systems. You must consider regulatory, ethical, societal, and technological influences and identify stakeholders’ expectations. Key things to keep in mind:

• Make sure you know the relevant laws, regulations, and societal expectations around AI
• Understand what your stakeholders expect when it comes to AI usage
• Clearly define the scope of your AI management system

Clause 5: Leadership 

Here, top management must step up and commit to creating an AI governance policy, ensuring accountability, and aligning the policy with business goals. 

Leaders should also delegate responsibilities for managing ethical AI. Key actions to take:

• Create and communicate your AI policy clearly
• Make sure AI governance is integrated into your business processes
• Provide the resources needed to make the framework work
• Assign accountability for AI compliance and performance
• Support continuous improvement of AI governance

Clause 6: Planning 

This clause identifies the potential risks and opportunities related to your AI systems. You must look at ethical, security, and compliance risks and ensure you plan to address potential negative impacts while meeting your governance goals. Key points:

• Conduct risk assessments specific to AI technologies
• Plan for mitigating any AI biases, inaccuracies, or other risks
• Set clear objectives and action plans for deploying AI responsibly

Clause 7: Support 

This ensures that your organization has the right resources to support the standards of the AI management system. It covers everything from competence and awareness to communication and documentation. Key actions:

• Provide training for staff on AI governance principles
• Ensure everyone understands AI policies and why they’re important
• Keep communication channels clear and open for governance-related issues
• Set up processes to manage documentation related to AI systems

Clause 8: Operation 

Here, you need to implement processes throughout the lifecycle of AI systems, from design and development to deployment and monitoring. The goal is to ensure AI systems are ethical, secure, and compliant at every stage. Key points:

• Develop processes for safe and ethical AI development
• Monitor AI systems to ensure ongoing compliance with governance policies
• Put in place controls to make sure AI is being used responsibly

Clause 9: Performance Evaluation 

This clause stresses the importance of measuring and evaluating your AI governance framework. It involves monitoring, conducting internal audits, and holding regular management reviews to ensure everything stays compliant and effective. Key things to do:

• Set clear performance indicators for your AI systems.
• Conduct internal audits to evaluate how well your governance is working.
• Review AI systems and governance policies regularly.

Clause 10: Improvement 

Finally, this clause highlights continuous improvement. You must be ready to address any issues, refine your processes, and adapt to changing risks and regulations. Key actions:

• Address nonconformities and take corrective actions as needed
• Continuously improve governance processes based on feedback and lessons learned
• Keep everything aligned with evolving AI regulations and risks
• Provide a reference point for improvements

How to Get ISO 42001 Certification?

ISO 42001 is designed to help businesses and consumers by promoting ethical and secure development of AI technologies. The goal is to ensure that AI systems are built with care, transparency, and responsibility. 

With that in mind, let’s walk through the steps to implement ISO 42001.

1. Set Up Your AI Policy Framework

This is where you establish the principles, such as aligning with business goals, maintaining ethical international standards, and complying with laws and regulations. 

It’s where you establish the principles, such as aligning with business goals, maintaining ethical standards, and complying with laws and regulations.

How to do it:

• Start by drafting a clear AI policy (over key areas like AI’s role in your business, risk management, and regulatory compliance with laws).
• Get the leadership team to approve the policy. Their backing is crucial for it to be effective.
• Ensure everyone knows about the policy. Share it widely across your organization.
• Regularly check the policy is current, especially when new regulations or business changes happen.

2. Define Who Does What

Now that the policy is set, it’s time to assign roles. Who’s responsible for overseeing AI systems, ensuring they’re compliant, and managing AI-specific risks? Clear responsibilities help make sure there are no gaps in the process.

How to do it:

• Assign roles like AI managers, risk assessors, and data protection officers. Each person should know what’s expected of them.
• Define reporting lines so everyone knows who to report to when there’s an issue or when they need support.
• Ensure all involved parties understand their roles and the importance of their contribution to AI management.
• Set up a system to review if everyone’s doing their part.

3. Document Everything

Documentation is key; without it, you cannot prove that you follow the standards. It ensures that everything is transparent and traceable.

How to do it:

• List all the documents you need, like policies, risk assessments, audit reports, and performance metrics
• Decide how you’ll keep track of these documents (digital, paper, etc.) and make sure they’re easy to find and up to date
• Put a system in place to control who can access and update documents
• Review the documents regularly to ensure they’re still relevant
• Keep everything secure. Don’t let sensitive information get into the wrong hands.

4. Plan and Control Operations

Establish standards for development, deployment, and monitoring. Create systems that consistently check your AI operations, and be ready to act quickly if something goes wrong.

How to do it:

• Set clear standards for your AI operations, including development, deployment, and monitoring.
• Put systems in place to control and monitor the operations. Ensure there’s a clear way to track if things are running smoothly.
• Continuously monitor performance to ensure your AI systems meet your set standards.
• When something isn’t working, act fast. Take corrective action to get things back on track.
• Review and adapt processes if necessary to stay aligned with your goals.

5. Assess AI Risks Regularly

Risk is inevitable with AI, so you need to look for potential issues actively. Third-party software like Sprinto can help you identify risks that could harm your organization or its stakeholders.

With Sprinto, you can assess and visualize the real impact of security risks using trusted industry benchmarks. This lets you approach risks confidently, prioritize what matters most, and handle everything systematically.

Take a look at the tool here and see how we have helped get ISO 27001 certified:

How to do it:

• Schedule regular risk assessments and conduct one whenever significant changes in your AI systems or operations occur.
• Identify risks and look at everything from data privacy issues to the ethical implications of your AI’s decisions.
• Document the risks, their potential impacts, and how likely they will happen.
• Based on your findings, take measures to reduce or eliminate those risks.

6. Treat AI Risks

After identifying the risks, it’s time to deal with them. This step involves finding solutions to ensure that risks are managed effectively and don’t become bigger problems.

How to do it:

• Develop a plan to treat the identified risks. What can you do to minimize them? I suggest redesigning the system or adding extra safeguards.
• Put those solutions into action. Ensure that the treatments are implemented across the board.
• Check if the treatments are working. If they aren’t, adjust your approach to ensure the risks are properly managed.
• Revise your risk treatment plan regularly. New risks pop up, and you want to stay ahead of them.

7. Assess How AI Systems Impact the Organization

It’s not enough for AI to just “work”—it must benefit your entire organization. Regularly evaluate how AI affects your business, employees, and customers. Document the impact to spot any unintended issues early and adjust as necessary.

How to do it:

• Plan when you’ll assess the impact of your AI systems. Do it after significant changes or at regular intervals.
• Look at how your AI impacts your organization, employees, customers, and the community.
• Document the results. If your AI system causes any harm, you’ll need a record to address it.
• If there’s a negative impact, take steps to correct it.

8. Measure AI System Performance

Just because AI is working doesn’t mean it’s working well. You need to measure and track its performance to ensure it’s delivering on its promises and identify improvement areas.

How to do it:

• Define key performance indicators (KPIs) for your AI systems, such as performance, risk management, and compliance levels.
• Set up systems to monitor these KPIs regularly.
• Analyze the data and use it to see where the AI systems excel and where they need improvement.
• Keep a record of performance data to track progress over time.

9. Perform Regular Audits

Auditing is a way to make sure everything is running as it should. These checks will help you find areas where the AI system isn’t up to snuff, ensuring compliance and identifying where improvements can be made.

How to do it:

• Develop an audit program. Decide how often audits should happen and who will conduct them.
• Pick impartial auditors who can give you an honest, objective assessment
• Perform audits to see if your AI systems meet internal policies and ISO 42001 standards
• Share the results with management and other stakeholders
• Make sure any non-conformities found are addressed quickly

10. Management Reviews for Ongoing Improvement

Top management should actively review the AI system to ensure it’s still on track and help the organization reach its goals. These reviews help catch any issues early and ensure continuous improvement.

How to do it:

• Set up regular meetings where management reviews AI system performance
• During these meetings, share key information like audit results, performance metrics, and risk assessments
• Discuss what’s working and where improvements can be made
• Make decisions about necessary changes and document those decisions

11. Fix Nonconformities 

Nonconformities are problems that can throw your whole AI management system out of alignment. When they happen, it’s essential to address them quickly to avoid bigger issues.

How to do it:

• When a nonconformity arises, document it right away
• Investigate what caused it. Was it a process issue? A human error?
• Put corrective actions in place to prevent it from happening again
• Make sure the solution works. Check that the issue doesn’t recur.
• Keep your systems and processes updated to reflect the changes

Challenges of Implementing ISO 42001

When you’re looking to implement ISO 42001, you’ll quickly realize it is not easy. There are real challenges to overcome. 

The good news is that understanding these challenges upfront can help you address them more effectively. This section will discuss some common roadblocks companies face when adopting ISO 42001.

1. Aligning AI Policy with Organizational Goals

One of the first challenges you’ll face is ensuring your AI policy fits your overall business goals well. It’s easy for AI to become this separate entity, but you need the policy to mesh with what your company stands for and what you’re trying to achieve. 

The challenge is staying ahead of the curve as AI evolves while ensuring your policy doesn’t get outdated too quickly.

2. Identifying and Managing AI Risks

AI has its fair share of risks; spotting them early is crucial. However, the tricky part is knowing how to handle these risks properly. AI risks differ from traditional ones, and because these systems can behave unpredictably, it’s tough to know precisely how to evaluate and mitigate them. Also, risks evolve as the systems evolve, so you must stay on top continuously.

3. Handling Documentation and Control

A lot of paperwork is involved while documenting every aspect of your AI system. That might not sound too complicated, but tracking everything becomes challenging when managing complex systems. 

You must ensure all your documents are up to date, accessible, and adequately protected from unauthorized access or loss. It’s a constant balancing act between thorough documentation and avoiding overwhelming it.

4. Monitoring and Evaluating AI Performance

Keeping track of how well your AI systems are performing over time is another challenge. You can’t just set it and forget it. 

Continuous monitoring is required, but the right systems and processes are needed to catch performance issues, risks, and even small nonconformities. The complexity of AI systems only makes this more complicated significantly, as those systems change and adapt.

Optimize Your ISO 42001 Compliance With Sprinto

ISO/IEC 42001:2023 focuses heavily on the ethical side of AI, such as tackling bias, promoting transparency, and ensuring accountability. These are all critical pieces in the puzzle, and at Sprinto, we ensure your AI management systems stay aligned with these principles.

I want to be upfront: we don’t fully support the entire ISO 42001 framework yet. But here’s where we shine: Sprinto helps you implement the core controls this framework requires. 

When managing risks, especially the unique ones tied to AI, our dynamic risk management tools are designed to support a risk-based approach, giving you a clearer picture of where things stand. So, while we don’t check every box for you, we make tackling the risk management and compliance side much easier.

Sprinto guides you because solid groundwork today leads to more freedom and flexibility tomorrow. Let’s make compliance easier together!

FAQs

What’s the difference between ISO 42001 and ISO 27001?

ISO 42001 focuses on organizations that handle and manage AI systems, whereas ISO 27001 focuses on information security management for any organization, not limited to AI. The two differ in implementation timelines, costs, and compliance processes. 

What are ISO 42001 Annex A Controls?

Annex A of ISO 42001 lays out a set of controls designed to give organizations a comprehensive framework for managing AI systems. The aim is to ensure AI is used ethically, risks are managed effectively, and innovation happens within a controlled and responsible environment. It’s all about balancing cutting-edge AI use and ethical practices.

Is ISO 42001 worth it?

Yes, ISO 42001 is worth considering. The standard’s focus on responsible AI and data management helps improve your AI systems’ quality, security, and reliability. It ensures that your approach to AI development and usage is ethical.