AI-specific risks under ISO 42001 and how to manage them

If you use artificial intelligence (AI) in your business, you’re probably already aware that it brings significant benefits. What’s less obvious is how to manage the risks that come with it.

The challenge isn’t AI itself. It’s the fact that many businesses are deploying AI tools without clear governance, accountability or proper oversight of how these systems work, the decisions they make  and the outputs they generate. When something goes wrong, it’s often unclear who’s responsible or how to fix it.

ISO/IEC 42001:2023 gives you a structured approach to managing these risks through an Artificial Intelligence Management System (AIMS). Rather than treating AI as something that just runs in the background in your business, the standard helps you establish proper governance, assign accountability and maintain control throughout the AI lifecycle. In this blog, we’ll explain the key AI-specific risks that ISO 42001 can help you manage, and, more importantly, what that looks like in practice.

Why AI creates different risks from traditional systems

Technology and software have always introduced risks and there are well-established frameworks to help you handle them. But AI introduces a different type of challenge.

Traditional software behaves predictably. The logic is fixed, so the outcomes are consistent. If you input A, you’ll get output B every time. So when something goes wrong, you can usually trace the problem back through the code to find out exactly what happened.

AI doesn’t work like that. The same input can produce different outputs depending on how the model has been trained, what data it’s been exposed to, or even subtle changes in how a prompt or command is phrased.

As AI systems learn from new data or operate in different contexts, their behaviour can drift over time. You might find that a tool that worked perfectly six months ago is now producing questionable results, and it’s not always obvious why. This unpredictability creates practical problems you need to manage.

When your AI makes a decision that affects a customer or employee, could you explain how it reached that conclusion?

If a regulator asked you to justify an automated decision, would you have a clear answer?

If the training data your AI uses contains biases or quality issues that you weren’t aware of, those problems are now baked into every decision your AI makes.

Many businesses are integrating AI services from external providers without fully understanding how those systems work or what dependencies they’re creating. They’re essentially trusting someone else’s black box to make decisions on their behalf.

In practice, this means your risk management needs to expand beyond the usual technical concerns you’re used to. Yes, cybersecurity and data privacy still matter. But now, you also need to think about whether your AI is making fair and consistent decisions, whether you can defend those decisions to regulators or customers, whether your brand could be damaged by a mistake made by AI, and whether your suppliers’ AI systems are introducing any risks you haven’t accounted for.

The key AI-specific risks ISO 42001 expects you to manage

Here are some of the key AI-specific risks you’re expected to manage under ISO 42001:

Data and privacy risk

AI processes large datasets that sometimes include personal or sensitive information. Could you confidently say where all your training data came from, and whether you have the right to use it?

AI systems also often process far more data than they need, creating unnecessary privacy issues.

Access controls are more complex, too. It’s not just about who can see the raw data, but whether someone could extract sensitive information by asking your AI the right questions.

ISO 42001 aims to combat this by requiring you to establish clear data governance rules, implement proper access controls and align your AI data handling with your existing privacy obligations, like GDPR.

Bias and inconsistent outcomes

The way your AI was trained and designed can produce uneven results. Bias often creeps in through data and design that prioritises certain patterns over others. You might not even realise it’s happening until someone challenges a decision the AI has made.

ISO 42001 requires you to assess the potential impact before deploying AI in sensitive areas, monitor outcomes for patterns of bias and introduce human oversight to catch any problems before they escalate.

Lack of transparency

Your business leaders and stakeholders need to understand how your AI reaches its decisions, especially when those outcomes affect people or finances. If you can’t explain how and why your AI made certain choices, you could face increased complaints and regulatory scrutiny.

This can be a real problem when someone asks you to justify a decision. “The AI said so” isn’t an acceptable answer to a customer who’s been declined, an employee who’s been passed over for promotion, or a regulator investigating a complaint. You need to be able to show the logic behind the decision, even if the AI’s workings are complex.

ISO 42001 addresses this by requiring you to document your AI’s decision logic, maintain records of significant decisions and establish review processes that allow you to trace and explain outcomes when needed.

Model drift and performance decline

AI’s performance changes as it updates with new data, the tools get more sophisticated and the systems respond to new inputs. Without monitoring, this can lead to declining accuracy or unintended behaviour. Your AI that worked perfectly at launch might be making questionable decisions six months later, and you won’t necessarily notice unless you’re actively looking for it.

ISO 42001 requires you to track and monitor your AI’s performance over time, set clear performance thresholds that trigger reviews and establish processes for investigating and addressing drift before it causes significant problems.

Over-reliance

AI tools are designed to save time, which means individuals and teams may begin accepting AI outputs without questioning them. When your staff stop double-checking AI decisions, performance issues or drift can go unnoticed until they cause real damage.

The more reliable your AI appears, the more likely people are to trust it completely. Then, when it makes a mistake, will anyone catch it? Or will it slip through because everyone assumes the system has it covered?

ISO 42001 addresses this by requiring you to define clear responsibilities for AI oversight, establish approval steps for significant decisions and create usage policies that specify when and how human review is required.

Third-party AI risk

Many businesses use external AI platforms, APIs or embedded AI within their existing software. These external tools can introduce unknown data sources and unexpected changes that create risks you haven’t accounted for.

Do you know how your supplier’s AI works?

When they update their model, do they tell you?

If their AI makes a mistake that affects your customers, who’s liable?

Third-party AI introduces a layer of opacity that’s difficult to manage. You’re relying on someone else’s training data, model design and quality controls, but you’re still responsible for the outcomes. ISO 42001 requires you to assess your AI suppliers before you engage them, define their responsibilities and maintain ongoing oversight to ensure any third-party AI you use continues to meet your standards.

What this means for your business

A structured AI management system provides better visibility, ownership and control over how your business uses AI. ISO 42001 offers a recognised framework to achieve that without having to build your own governance from scratch.

You’re free to read the ISO 42001 standard and apply it to your own operations. You don’t necessarily need to pursue certification, although we’d strongly recommend it. That said, implementation usually goes more smoothly when you’ve got support from someone who’s done it before, and that’s where ISO QSL comes in. We can help your business map its AI use, identify gaps and risks, and implement an AI management system aligned with ISO 42001. We’ve also launched a new Introduction to ISO 42001 online training module.

So, if you want to understand your current AI risks and work out what level of control makes sense for your situation, let’s start with an obligation-free consultation. We’ll help you decide on the most cost-effective and practical solution for your business.