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An AI risk assessment is the process of mapping where risks emerge during the lifecycle of an AI system, classifying them by severity and probability, and prioritizing which ones to mitigate first, all within the compliance framework imposed by the EU AI Act.
Why an AI risk assessment matters
AI brings unique challenges because of its scale, opacity and autonomy. A biased decision in a human process may affect a handful of people, while a biased algorithm can impact thousands in a fraction of a second. That is why an AI risk assessment is essential not only to reduce exposure to liability but also to safeguard the reputation and credibility of organizations.
The EU AI Act makes this explicit. Providers of high-risk systems must establish and maintain a risk management system throughout the entire lifecycle of the AI system. This obligation goes far beyond drafting a document once and filing it away. It requires continuous mapping, classification and prioritization of risks, together with the adoption of technical and organizational measures to mitigate them.
Mapping risks across the AI lifecycle
The first stage of any AI risk assessment is mapping. This means identifying where risks may emerge across all phases of the lifecycle. Risks can materialize at the data collection stage, where low-quality or non-representative data can introduce bias. They can arise during training, where the choice of model and architecture may affect transparency or explainability. They can also emerge during deployment, for instance if an AI system is used in contexts that were never envisaged by its designers.
Mapping must also take into account the different actors involved. The EU AI Act draws a line between providers, deployers, distributors and importers, and obligations will vary accordingly. A company integrating a general-purpose AI model into its product will face different responsibilities than the original developer of the model. A thorough map ensures that accountability is clear, and that risks are not overlooked simply because they sit outside the immediate control of one actor. On the topic, you can read the article “Are you a Provider or a Deployer under the EU AI Act?“.
Classifying risks under a regulatory lens
Once risks are mapped, they need to be classified. The AI risk assessment cannot stop at a simple list of potential harms; it needs to provide a structured view of their severity and likelihood.
The EU AI Act itself operates through a risk-based logic. It prohibits unacceptable uses of AI, such as manipulative practices or social scoring. It imposes the strictest obligations on high-risk systems, lighter transparency duties on limited-risk systems, and almost no requirements on minimal-risk AI. But while this legal categorization is useful, it is not sufficient for operational risk management.
Companies need to assess severity: how serious would the harm be if it materialized? They need to estimate likelihood: how probable is the event, given current safeguards? And they need to consider detectability: how quickly can the harm be spotted and addressed? A low-probability event that is very difficult to detect can still represent a critical risk.
Classification should also include an analysis of fundamental rights. Discrimination, privacy violations or manipulative practices may not always be captured by technical metrics, but they can have severe legal and reputational consequences. The risk classification is crucial to understand the proper category of AI system under the EU AI Act and for instance whether it is a prohibited practice. On the topic, you can read the article “AI Act on Prohibited Practices Is Now in Force – Are You Ready?“.
Prioritizing risks and planning mitigations
Classification is only useful if it leads to prioritization. Resources are limited, and not all risks can be addressed simultaneously. A structured AI risk assessment allows organizations to determine which risks must be eliminated, which require strong mitigation and which can be accepted with monitoring as residual risks.
Here, the EU AI Act sets clear boundaries. Any risk tied to prohibited practices must be addressed immediately. High-risk AI systems cannot be deployed without the safeguards required by the Act, such as high-quality data governance, transparency, logging, human oversight and post-market monitoring. These are not optional controls; they are obligations.
Beyond regulatory imperatives, organizations should prioritize based on a combination of severity, probability and detectability. A catastrophic harm with a medium probability of occurrence should always come before a minor reputational risk with a high probability.
Mitigation strategies can vary. Technical measures may include bias mitigation techniques, anomaly detection or adversarial testing. Organizational measures can involve human-in-the-loop reviews, escalation procedures or clear accountability structures. Design changes may also be necessary, such as simplifying the model or excluding certain variables that create discriminatory outcomes. What matters is that mitigation plans are documented, tested and updated as systems evolve. On the topic, you can read the article “AI Compliance Assessment – Is Your Company Doing it Right?“.
Embedding the EU AI Act compliance overlay
A key point to remember is that the AI risk assessment is not a standalone process. Under the EU AI Act, it is one element of a broader compliance system that includes technical documentation, conformity assessments, registration obligations and post-market monitoring.
This means that each risk identified must be linked to specific documentation. If bias is identified as a risk, the technical file should show how data governance measures address it. If lack of explainability is flagged, the file must include information on transparency tools or user information provided. Regulators will expect to see a clear chain of reasoning between risks, mitigations and compliance artefacts.
The Act also emphasizes lifecycle management. Risk assessments must be updated when the system is modified, retrained or redeployed in new contexts. They must also be revisited in light of real-world performance, with post-market monitoring feeding back into the assessment process.
Finally, risk management under the AI Act does not operate in a vacuum. Other legislation, from GDPR to product safety and consumer protection rules to intellectual property laws, continues to apply. An effective AI risk assessment must therefore be integrated with wider compliance frameworks. To support businesses in this assessment, my team developed “Prisca AI Compliance“.
A roadmap to implementation of an AI risk assessment
How should companies approach this in practice? Based on experience advising clients across different sectors, I suggest the following roadmap:
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Portfolio review: identify all AI systems in use, including those integrated through third-party solutions.
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Mapping workshop: bring together technical, legal and compliance teams to map risks across the lifecycle.
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Classification exercise: evaluate severity, probability and detectability, while aligning with the EU AI Act risk categories.
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Prioritization: rank risks, taking into account regulatory imperatives as well as business priorities.
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Mitigation planning: assign ownership for each mitigation measure, set timelines and define success indicators.
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Documentation: prepare the technical file and other compliance artefacts, ensuring consistency with AI Act obligations.
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Monitoring: establish procedures for post-market monitoring, incident reporting and continuous updates to the risk assessment.
This process may seem resource-intensive, but it is also an opportunity. Companies that invest in strong risk assessment frameworks can use them to differentiate in the market, demonstrating that their AI is not only innovative but also trustworthy.
The EU AI Act is changing the way organizations think about risk. An AI risk assessment is no longer an internal formality; it is a legal requirement and a competitive advantage. By mapping, classifying and prioritizing risks, companies can build a defensible compliance framework, avoid regulatory friction and enhance trust with clients, investors and regulators.
The lesson is clear: risk assessments are not a cost to be minimized, but an investment in sustainable AI governance. Those who embrace them early will not only comply with the law, but also lead in the marketplace.
On a similar topic, you can read the article “How to Set Up an AI Committee in Your Company’s Governance Framework“.