AI systems are not like traditional software. They do not always give the same answer to the same input, they learn from data that can contain errors and bias, and their behaviour is not always transparently explainable. That makes the risk analysis more complex than for standard IT implementations.

On top of that, the legal and societal norms around AI are changing rapidly. What is acceptable today may be prohibited under new regulations tomorrow. Those who build now without attention to compliance may be forced into costly adjustments.

AI systems are only as good as the data they are trained on. If that data is incomplete, outdated or not representative of the actual situation, the AI system produces outcomes that are incorrect.

Before starting, analyse the quality of the data on which the system will be based. Are all relevant groups represented? Are there historical patterns in the data that could reinforce undesirable system behaviour? This is particularly relevant for AI systems that make decisions directly affecting people, such as application screening or credit assessment.

An AI system can unintentionally discriminate if the training data reflects historical inequalities. A system that assesses job candidates based on historical hiring decisions can systematically disadvantage certain groups if those groups were underrepresented in the past.

This risk is not hypothetical: several large companies have already suffered reputational damage from AI systems that demonstrably discriminated. Always have AI systems that make decisions about people audited for bias before going into production.

AI systems that work with personal data fall under GDPR. That means obligations around consent, data minimisation, retention periods and the right to explanation for automated decision-making.

Check before starting whether the system is GDPR-compliant. Which personal data is being processed? Where is it stored? Who has access? Is there a data processing agreement with the vendor? Can the system explain why it made a particular decision if a data subject requests it?

Generative AI models can produce incorrect information with apparent confidence. This risk, also called hallucination, is particularly problematic in contexts where accuracy is critical: legal documents, medical information, financial analyses.

For each AI application, assess where the output ends up and how critical an error would be. Apply human review where the risks are high. Never assume AI output is always correct.

Many organisations build AI applications on models and platforms from external providers. That creates dependency. If a vendor raises its prices, changes its services or shuts down, your AI system is vulnerable.

Inventory which external dependencies you are building and what the consequences would be if they disappear. Are there alternatives? Can you switch easily? Contractual agreements on availability, data portability and exit clauses are at least as important with AI vendors as with other software suppliers.

As AI takes over more decisions, the risk grows that people lose control over processes they poorly understand. Employees blindly trust AI output without critically assessing the outcomes.

Ensure AI systems always have a control mechanism where people can review and adjust outcomes. Human final responsibility must be secured, even when AI does most of the work.

AI risk management is not an administrative exercise but a practical necessity. Those who address risks early build better systems and avoid expensive corrections afterwards. A risk analysis does not need to be exhaustive, but must identify the most relevant risks for your specific application.

Mach8 helps organisations conduct AI risk analyses and set up AI governance. Get in touch or view our AI agents service.