Risks and governance
AI Act, GDPR, algorithmic bias, hallucinations, guardrails, digital sovereignty. The vocabulary to distinguish experimentation from responsible production.
- AI ActThe AI Act is European Regulation 2024/1689, the world's first legal framework to classify AI systems by risk level and to impose differentiated obligations: prohibition for unacceptable uses, strict requirements for high-risk systems, transparency for the rest.
- AI carbon footprintThe AI carbon footprint is the set of greenhouse gas emissions related to its life cycle: model training, component manufacturing, data centre operation, and inference at usage. It is becoming a significant consumption item on a global scale.
- Algorithmic biasAn algorithmic bias is a systematic deviation in an AI model, inherited from training data, design choices, or deployment context, that produces unfair or erroneous decisions to the detriment of a population group or a type of case.
- AlignmentAlignment is the set of techniques that aim to steer the behaviour of an AI model towards the goals and human values of its user or publisher. It turns a raw model, capable of producing anything, into a useful, honest assistant that refuses requests contrary to the rules set.
- Digital sovereigntyDigital sovereignty is the capacity of a company or a state to control its data, its infrastructure, and its AI tools, independently of foreign jurisdictions. It conditions confidentiality, regulatory compliance, and resilience to access disruptions dictated by external political decisions.
- GDPR and AIGDPR governs all personal-data processing by an AI system operated in Europe or concerning Europeans. Four obligations apply as priorities: explicit purpose, data minimisation, access and opposition rights, and impact assessment for high-risk processing.
- GuardrailsGuardrails are technical control layers, added at the input or output of an AI model, that detect and block undesirable behaviours: malicious prompts, data leaks, forbidden content. They are distinct from alignment, which acts on the model's default behaviour itself.
- Red teamingRed teaming, borrowed from cybersecurity, is the practice of testing an AI system by simulating adverse usage attempts: rule bypassing, sensitive-data extraction, forbidden-content generation. It aims to identify vulnerabilities before a malicious actor exploits them in production.
- Shadow AI Barometer 2026In 2026, the ungoverned use of generative AI is no longer a blind spot: it is the norm. Two-thirds of professionals use tools at work they believe are banned — and pour emails, customer data and financial documents into them.
- Training dataTraining data is the set of texts, images, code, and other content used to train an AI model. Its composition determines what the model knows, what it ignores, its biases, and its legal risks. A major part of current AI litigation concerns their provenance and lawfulness.