[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"framework:eu-ai-act\u002F10":3},{"item":4,"lessons":16,"controls":26,"related":36},{"family":5,"item":6,"code":7,"title":8,"citations":9,"publisher":10,"summary_md":11,"official_text":12,"official_attribution":12,"official_url":13,"link_label":14,"note":12,"updated_at":15},"eu-ai-act","10","Article 10","Data and data governance",2,"European Union","**What it asks for.** High-risk AI systems that are trained on data must be developed using training, validation and testing data sets that meet quality criteria. Data sets must be relevant, sufficiently representative and, to the best extent possible, free of errors and complete in view of the intended purpose, and subject to appropriate data governance and management practices.\n\n**In practice.** Providers document design choices, data collection processes and origin, the original purpose of any personal data, preparation steps such as labelling and cleaning, the assumptions the data is meant to represent, and an assessment of availability and suitability. They examine data for possible biases that could affect health, safety or fundamental rights, apply measures to detect, prevent and mitigate them, and record data gaps and how they are addressed. The data must reflect the geographical, contextual and behavioural setting of use. Special category data may be processed for bias detection only under strict safeguards set out in the article.\n\n**Common gaps.** Typical gaps are missing data lineage, no documented bias examination, and training data that does not represent the population or region where the system is actually deployed.",null,"https:\u002F\u002Feur-lex.europa.eu\u002Feli\u002Freg\u002F2024\u002F1689\u002Foj","Read the AI Act on EUR-Lex","2026-10-18T15:18:43.664+00:00",{"total":9,"list":17},[18,22],{"slug":19,"title":20,"published_at":21},"ai-hiring-tools-trigger-gdpr-scrutiny-over-missing-risk-assessments","AI Hiring Tools Trigger GDPR Scrutiny Over Missing Risk Assessments","2026-10-14T10:20:18.748+00:00",{"slug":23,"title":24,"published_at":25},"xais-grok-allegedly-trained-on-csam-highlighting-ai-data-governance-failures","xAI's Grok Allegedly Trained on CSAM, Highlighting AI Data Governance Failures","2026-08-27T18:20:40.269+00:00",[27,31],{"slug":28,"name":29,"problem":30,"stories":9},"ai-security","AI application security","Prompt injection, data leaks through AI tools and unapproved AI use get caught.",{"slug":32,"name":33,"problem":34,"stories":35},"data-loss-prevention","Data loss prevention (DLP)","Sensitive data cannot quietly leave by email, upload, USB stick or AI tool.",1,[37,42,47,51,55,59,63,67],{"family":38,"item":39,"code":40,"title":41,"together":9},"nist-ai-rmf","overview","AI RMF 1.0","AI Risk Management Framework (AI RMF 1.0)",{"family":43,"item":44,"code":45,"title":46,"together":9},"gdpr","5","Article 5(1)(f)","Principles relating to processing of personal data",{"family":43,"item":48,"code":49,"title":50,"together":35},"13","Article 13","Information to be provided where personal data are collected from the data subject",{"family":43,"item":52,"code":53,"title":54,"together":35},"22","Article 22","Automated individual decision-making, including profiling",{"family":43,"item":56,"code":57,"title":58,"together":35},"25","Article 25","Data protection by design and by default",{"family":43,"item":60,"code":61,"title":62,"together":35},"35","Article 35","Data protection impact assessment",{"family":43,"item":64,"code":65,"title":66,"together":35},"9","Article 9","Processing of special categories of personal data",{"family":68,"item":69,"code":70,"title":71,"together":35},"iso","27701","ISO\u002FIEC 27701","ISO\u002FIEC 27701: Privacy information management systems"]