GDPR Compliance Checklist for AI Features
AI features processing user data require careful GDPR alignment. This guide gives a focused, actionable roadmap—assess applicability, minimise data flows, choose lawful bases, embed privacy-by-design, enable rights and transparency, run DPIAs, and mitigate risks.
- Quick, actionable steps to determine GDPR scope for your AI feature.
- Concrete techniques to minimise personal data and document lawful bases.
- Practical controls for transparency, consent, DPIAs, and risk mitigation.
Quick answer (one-paragraph summary)
If your AI feature processes identifiable information about EU data subjects, GDPR applies: identify what personal data or profiling occurs, minimise and pseudonymise where possible, select lawful bases (consent, contract, legitimate interests, or legal obligation), document processing in records, enable transparency and rights (access, rectification, deletion, portability, objection), run a DPIA for high-risk processing, and implement technical and organisational measures to mitigate risks.
Assess GDPR applicability to your AI feature
Start by deciding whether the data processed qualifies as personal data under GDPR: any information relating to an identifiable person directly or indirectly (IDs, names, device IDs, IPs, location, behavioral profiles).
- Scope: Identify if you target EU residents or offer services in the EU—GDPR extraterritorial scope applies.
- Processing types: Logging, training, inference, profiling, behavioural scoring, emotion recognition—all can be personal data processing.
- Operator roles: Determine whether your organisation is a controller, joint-controller, or processor for each activity.
| Activity | Likely Role | Notes |
|---|---|---|
| Collecting user inputs via your app | Controller | Decides purposes and means of collection |
| Hosting third-party model APIs | Controller or Processor | Depends on who determines purposes and model configuration |
| Model vendor training on your data | Processor / joint-controller | Contractual terms and purpose determine role |
Map and minimise personal data flows
Create a concise data flow map showing sources, sinks, storage, transformations, and third parties. Mapping clarifies risk points and aids DPIAs and records of processing.
- Inventory data fields: mark which are personal data, special category, or pseudonymous.
- Apply minimisation: only collect fields necessary for the AI task and drop optional PII.
- Pseudonymise at ingestion: replace identifiers with reversible tokens stored separately.
- Limit retention: set explicit retention periods aligned to purpose and purge schedules.
// Example pseudonymisation pattern
user_id_raw -> generateSaltedHash(user_id_raw) -> store(hash)
separateKeyStore contains mapping encrypted with KMS
Choose and document lawful bases for processing
Identify and record the lawful basis for each processing activity. Different features may rely on different bases (e.g., telemetry vs. personalized recommendations).
- Consent: Use when processing is not strictly necessary and you need explicit permission (clear opt-in, granular choices, withdrawable).
- Contract: Use when processing is necessary to perform a service the user signed up for.
- Legitimate interests: Use for analytics or fraud detection after a balancing test; document the test.
- Legal obligation or vital interests: Rarely used for AI; apply only when strictly true.
| Use case | Preferred lawful basis |
|---|---|
| Mandatory feature to deliver service | Contract |
| Personalised recommendations | Consent or contract (if core to service) |
| Product analytics | Legitimate interests (with balancing test) |
Embed privacy-by-design into development
Integrate privacy controls into the software lifecycle: planning, design, implementation, testing, and deployment. Treat privacy as a first-class non-functional requirement.
- Design: minimisation, segregation, and default privacy-friendly settings.
- Engineering: encryption-in-transit and at-rest, access controls, key management, audit logging.
- Model training: prefer synthetic data, federated learning, or differential privacy where possible.
- Testing: include privacy-focused tests (data leakage, re-identification, membership inference).
Example controls: rate-limited logging, hashed identifiers, redact PII in training corpora, and CI checks for secret leaks and data schema drift.
Enable transparency, consent, and data‑subject rights
Transparency and user controls are core GDPR requirements. Provide clear, accessible information and practical tools for rights requests.
- Privacy notices: short, plain-language summaries plus a detailed policy covering purposes, recipients, retention, and lawful bases.
- Consent UI: granular toggles, clear defaults, record consent timestamp and scope, and support easy withdrawal.
- Rights tooling: build endpoints/processes for access, rectification, erasure, portability, and objection — track request lifecycle and SLA.
- Automated workflows: where possible, automate common requests (e.g., export data archive, delete account data) with authenticated flows.
GET /user/export-data -> returns JSON archive of user-related records
POST /user/delete-requests -> triggers deletion workflow and audit entry
Conduct DPIAs and implement risk mitigations
Perform a Data Protection Impact Assessment whenever processing is likely to result in high risk to individuals (systematic profiling, large-scale processing, special categories, or new technologies).
- Scope DPIA: describe processing, purposes, necessity, and proportionality.
- Risk assessment: identify threats (re-identification, bias, incorrect automated decisions) and estimate severity and likelihood.
- Mitigations: technical (encryption, differential privacy), organisational (access controls, training), and contractual (vendor SLAs).
- Consultation: involve DPOs, security teams, legal, and, when required, supervisory authorities.
| Risk | Mitigation | Residual Risk |
|---|---|---|
| Re-identification from model outputs | Pseudonymise inputs; filter outputs for PII | Low |
| Model bias causing discriminatory outcomes | Bias testing, human review on decisions | Medium |
| Unauthorized access to training data | Least privilege, encryption, rotate keys | Low |
Common pitfalls and how to avoid them
- Assuming anonymisation is straightforward — Remedy: validate anonymisation with re-identification tests and document limitations.
- Using vendor model APIs without contractual safeguards — Remedy: include data processing agreements, audit rights, and data deletion clauses.
- Overly broad consent boxes — Remedy: use granular, purpose-specific consent and store consent metadata.
- Neglecting retention and deletion — Remedy: implement automated retention policies and periodic audits.
- Skipping DPIAs for high-risk AI — Remedy: run a DPIA early and update it as models or data change.
- Relying solely on synthetic data without validation — Remedy: ensure synthetic data preserves utility and test for leakage.
Implementation checklist
- Map personal data flows and inventory data fields.
- Decide roles (controller/processor) and update contracts with vendors.
- Select and document lawful bases per processing activity.
- Pseudonymise or anonymise data where feasible; minimise collection.
- Embed privacy-by-design in dev lifecycle and CI/CD checks.
- Implement transparency: privacy notice, consent UI, and rights endpoints.
- Conduct DPIA for high-risk processing and document mitigations.
- Apply technical controls: encryption, access controls, logging, retention schedules.
- Train teams and keep records of processing activities.
FAQ
- Q: When is consent required for AI processing?
- A: Consent is required when no other lawful basis applies, or processing is not necessary for a contract and involves profiling or special categories; it must be explicit, specific, and withdrawable.
- Q: Is pseudonymised data still personal data?
- A: Yes. Pseudonymised data is still personal data if re-identification is possible with additional information; treat it with controls and document safeguards.
- Q: Do I need a DPIA for model training?
- A: Likely—if training uses large-scale personal data, special categories, or leads to systematic profiling. Perform a screening and proceed to a DPIA if risks are high.
- Q: How should I handle third-party model providers?
- A: Use a Data Processing Agreement that specifies purposes, data categories, security measures, subcontractor rules, and deletion obligations; audit vendor compliance where possible.
- Q: What technical measures reduce GDPR risk for AI?
- A: Minimisation, pseudonymisation, access controls, encryption, differential privacy, secure model deployment, and robust logging significantly reduce risk.
