AI & Data Companies

AI and data companies face risks that standard control sets often miss: prompt injection, sensitive information disclosure, model and data poisoning, supply-chain vulnerabilities, and excessive agent autonomy.

Our approach: Standard GDPR readiness first. AI and data-specific hardening second. The advisory modules below are optional enhancements on top of mandatory controls.

Standard Controls vs. AI/Data Enhancements

Standard GDPR Readiness

Mandatory controls required for compliance:

  • Logical access and privileged access
  • Change management
  • Incident response
  • Risk management
  • Vendor management
  • Backup and availability
  • Logging and monitoring
  • Confidentiality and privacy (where applicable)

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AI/Data Advisory Enhancements

Optional modules justified by AI-risk frameworks:

  • Data lineage and training data governance
  • Prompt/response telemetry
  • RAG and retrieval governance
  • Model/provider vendor review
  • Agent approval gates
  • AI-assisted SDLC controls
  • Warehouse and analytics governance

Advisory Modules

Each module adds specific controls and documentation practices to address risks unique to AI and data-intensive products.

AI-Assisted SDLC

GDPR Art. 25 (privacy by design and by default) requires data protection to be integrated into development processes, including those augmented by AI code generation and automated development tools.

What This Module Adds

  • Privacy review of AI-generated code for unintended personal data collection or processing
  • Automated data collection review to detect new data flows introduced by AI-suggested features
  • DPIA trigger assessment for AI-assisted features that process personal data

Human Review & Agent Gates

GDPR Art. 22 restricts automated individual decision-making that produces legal or similarly significant effects, requiring human intervention and the right to contest such decisions.

What This Module Adds

  • Art. 22 compliance assessment for AI agent decision points affecting individuals
  • Meaningful human review procedures ensuring genuine human involvement, not rubber-stamping
  • Right to contest automated decisions with clear escalation paths and response timelines

Model Provider & Vendor Risk

GDPR Art. 28 processor requirements extend to AI model providers when they process personal data on behalf of the controller, requiring binding agreements, due diligence, and sub-processor chain management.

What This Module Adds

  • Processor agreements for AI model providers covering data handling, retention, and deletion obligations
  • Sub-processor chain mapping for AI inference pipelines spanning multiple providers
  • Training data due diligence to assess whether provider models were trained on personal data without lawful basis

Prompt & Response Logging

GDPR Art. 30 ROPA requirements and Art. 15 right of access apply to AI interaction logs when they contain personal data, requiring documented processing purposes, retention limits, and inclusion in access request responses.

What This Module Adds

  • Lawful basis assessment for logging AI interactions that contain or reference personal data
  • Retention limits for prompt and response logs aligned with purpose limitation and storage limitation principles
  • Inclusion of AI interaction logs in data subject access request scope and ROPA

RAG & Vector Store Controls

Vector embeddings of personal data remain personal data under the GDPR when they can be linked back to individuals; erasure rights under Art. 17 must be technically feasible for embedded data.

What This Module Adds

  • Erasure capability for personal data embedded in vector stores, including re-indexing workflows
  • Purpose limitation controls ensuring embeddings are used only for their documented purpose
  • Re-embedding procedures after individual data deletion to prevent residual data leakage

Training & Inference Data Governance

Using personal data for model training requires a lawful basis under Art. 5–6, and purpose limitation under Art. 5(1)(b) restricts repurposing operational data for training without a compatible purpose assessment or additional consent.

What This Module Adds

  • Lawful basis assessment for using personal data in model training and fine-tuning
  • Data subject notification about training data use through updated privacy notices (Art. 13–14)
  • Opt-out mechanisms enabling data subjects to exclude their data from training datasets

Warehouse & Analytics Governance

GDPR purpose limitation under Art. 5(1)(b) restricts repurposing personal data in analytics warehouses and data lakes without a compatible purpose assessment, and data minimization under Art. 5(1)(c) constrains the scope of data aggregation.

What This Module Adds

  • Compatible purpose test (Art. 6(4)) for analytics use cases repurposing operational personal data
  • Anonymization and pseudonymization strategies for analytics datasets to reduce compliance burden
  • Analytics-specific DPIA for warehouse processing that profiles or segments individuals

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