How to navigate data protection risks in generative systems

A concise, practical guide for companies on managing data protection risks from generative systems and AI deployments

Generative AI is reshaping how organisations create text, images and code — and it’s forcing data-protection teams to rethink compliance. This guide by Dr. Luca Ferretti unpacks what European supervisors (including the Garante and the EDPB) are watching, the real risks for businesses, and the concrete steps legal, product and engineering teams should take now to reduce legal and operational exposure.

Why this matters now
– Generative models process vast, messy datasets and can reproduce or infer personal data.
– Supervisory authorities treat model training, fine‑tuning and inference as linked processing activities subject to GDPR obligations.
– Regulators expect documented, verifiable safeguards — not high‑level claims.

What regulators expect (in plain terms)
– Apply familiar GDPR principles across the model lifecycle: purpose limitation, data minimisation, transparency and accountability.
– Treat training datasets, model versions and inference logs as auditable processing operations.
– Provide clear, usable information to people when model outputs can affect them or reveal personal data.

– Demonstrate that chosen legal bases (legitimate interest, contract necessity or consent) are appropriate and proportionate.

Risk snapshot
– Model outputs may accidentally disclose personal data, recreate copyrighted material, or infer sensitive traits.
– Lack of provenance, weak access controls or missing DPIAs can trigger enforcement: corrective measures, fines and reputational damage.
– Supervisors will look for evidence: dataset inventories, DPIAs, versioned training logs, access records and mitigation measures.

A practical, staged approach

1) Immediate actions (first 30–60 days)
– Data inventory: catalogue training, validation and prompt datasets. Flag personal data and note where it appears in inputs and outputs.
– DPIA: run a targeted Data Protection Impact Assessment for any model likely to cause high risk.
– Legal-basis mapping: record the basis for collecting, storing and reusing each dataset. Document why consent is or isn’t relied upon.
– Notices & disclosures: publish brief, intelligible user-facing notices about automated generation, data sources and profiling risks where relevant.
– Incident & rights processes: ensure you can accept and handle requests that reference generated outputs (access, deletion, objections).

2) Operational controls to implement quickly
– Access controls: role-based access, MFA and least‑privilege for training and inference environments.
– Logging & versioning: tie model outputs to specific model and dataset snapshots; retain training logs for auditability.
– Minimisation & retention: keep only what’s necessary; define retention windows for raw training data and output logs.
– Vendor clauses: require data-provenance warranties, subprocessor lists, audit rights and breach notification timelines.

3) Medium-term governance (3–6 months)
– Cross-functional governance body: include legal, product, security and data science to gate model approval and monitoring.
– Integrate RegTech: automate provenance capture, continuous monitoring for drift/leakage and generate supervisory-ready reports.
– Embed privacy-by-design: formalise secure fine-tuning workflows, pseudonymisation and differential-privacy where appropriate.
– Model documentation: create concise model cards and datasheets that explain data sources, intended uses, performance limits and known biases.

4) Ongoing, audit-ready practices
– Maintain a centralized registry of datasets and model artefacts linked to legal-basis assessments and DPIA outcomes.
– Schedule periodic reassessments and automated DPIA refreshes when models change or retrain.
– Keep incident remediation plans and supplier attestations up to date and easily accessible for supervisory review.

Documentation: what supervisors will look for
– Dataset inventory with provenance and personal-data classification.
– Versioned training logs, model cards and testing results that map harms to mitigations.
– DPIAs and risk assessments with concrete mitigation steps and residual risk explanations.
– Access logs, audit trails, vendor contracts and documented decision-making about dataset composition and retention.

Concrete checks and quick wins
– Run a re-identification threat assessment for each high-risk dataset and publish a concise mitigation plan.
– Add model identifiers to output logs so any generated content can be traced to the exact model/version and data snapshot.
– Require signed attestations from data suppliers about data origin and rights to use the material for training.
– Implement SLA-backed response processes for rights requests connected to generated outputs.