How to Be Compliant When Your AI Is High Risk

What are the core compliance requirements for high-risk AI systems in the EU?
To place a high-risk AI system on the EU market, you must follow the "blueprint for responsible AI" found in Articles 8 through 15. These are mandatory prerequisites, not just optional best practices. These requirements are a comprehensive framework where each part reinforces the others to build 'trustworthy AI'. While this is a substantial undertaking, the effort is proportional to the impact. These rules exist because high-risk AI makes significant decisions about people's lives, such as in credit scoring or hiring, and therefore must be held to a higher standard of accountability. 

You are expected to:

What is needed to fulfil the risk management requirements of Article 9?
Fulfilling Article 9 requires a continuous, living process rather than a one-off assessment. You must identify and analyse risks during design, monitor them during development, and track them after deployment. This system should address technical risks alongside threats to health, safety, and fundamental rights. You must estimate the likelihood and severity of these risks and implement measures to reduce them to acceptable levels. A key part of this requirement is active testing to ensure your mitigation measures actually work. A red flag for any organisation is a risk assessment that hasn't resulted in any changes to the system’s design. Real risk management involves asking uncomfortable questions early and being willing to remove features that cannot be made safe.

How does the Act define data quality and governance under Article 10?
Article 10 requires rigorous quality standards for training, validation, and testing data. To comply, your data must be relevant, representative, complete, and free of errors. The goal isn't absolute perfection but "fitness for purpose" - meaning the data is high enough quality for the AI's intended use. You must comprehensively examine data sources, document limitations, and systematically check for gaps or historical biases that could lead to discrimination. Robust governance practices are essential, meaning you need clear processes for who decides which data to use and how to validate its quality. Organisations often mistake quantity for quality; having millions of data points is useless if they are all biased. You must actively implement measures to detect and mitigate these biases as a hard compliance requirement.

What documentation and record-keeping are required by Articles 11 and 12?
You must draw up comprehensive technical documentation before placing a high-risk system on the market. This "user manual on steroids" must demonstrate compliance by covering everything from the development methodology and training datasets to accuracy metrics and human oversight measures. Additionally, Article 12 mandates automatic logging to ensure traceability. This means the system must record usage patterns, critical decisions (such as a human overriding an AI recommendation), and any irregularities.

This record-keeping might feel like an overhead, it serves several vital purposes:

How are transparency and human oversight implemented through Articles 13 and 14?
Transparency, according to Article 13, is about ensuring that those using the system (the deployers) can understand and use it appropriately. This is achieved through detailed instructions for use that outline the system's capabilities, limitations, and potential risks. Article 14 then mandates that the system be designed for effective human supervision. Humans in charge must have the authority and ability to interpret outputs, detect anomalies, and intervene or override decisions when necessary. A major challenge is automation bias - the human tendency to over-rely on automated systems. To fulfil these requirements, oversight measures must actively combat this bias. Simply having a human 'rubber-stamp' an AI's decision does not count; the human must have a real understanding and a genuine opportunity to interrupt the operation if something goes wrong.

What are the performance standards for accuracy, robustness, and cybersecurity in Article 15?
Under Article 15, your AI must be accurate, robust, and secure. Accuracy means the system performs exactly as you declare it will, backed by proven metrics. Robustness refers to resilience; the system must handle unexpected inputs, data drift, or attempts at manipulation without failing. This is particularly important for continuously learning systems to prevent "negative feedback loops" where the AI amplifies its own biases over time. Finally, cybersecurity involves protecting the system from unauthorised third parties. Because high-risk AI makes important decisions, it is an attractive target for attackers. You must implement security measures like access controls, encryption, and integrity checks. A final warning: if your accuracy metrics look too good to be true, they likely are, as real-world performance rarely matches perfect laboratory conditions.