AI & LLM Data Protection
Privacy, governance, and risk controls for organisations building or deploying AI — before regulators start asking questions.
Why AI Creates Privacy Risks That Standard GDPR Approaches Don't Address
Most organisations approach AI compliance the same way they approach general GDPR compliance: document the processing, identify a lawful basis, and draft a generic privacy notice. That approach is insufficient for AI systems, and regulators are increasingly clear about why.
AI and Large Language Models (LLMs) introduce a set of privacy risks that do not appear in traditional data processing. Training data may include personal data that was never intended for use in model training. Model outputs can inadvertently reproduce personal data from training sets. Automated decisions can produce outcomes that are discriminatory, unexplainable, or disproportionate — without any human review. And the opacity of how models process and produce outputs makes the standard GDPR requirement to explain processing to data subjects genuinely difficult to satisfy.
Organisations that treat AI compliance as a standard GDPR exercise are creating regulatory exposure. KewData's AI and LLM data protection services are designed to address the specific risks that AI systems generate.
The Regulatory Landscape for AI — What You Need to Know Now
A snapshot of the key global frameworks forcing organisations to establish strict AI controls.
Risk Phase-In
Entered into force August 2024. Unacceptable-risk systems prohibited from Feb 2025. High-risk obligations phase in during 2025–2026. Applies to any system placed on the EU market or affecting EU subjects.
Automated Decisions
Individuals have the right not to be subject to decisions based solely on automated processing (like profiling) that produce legal or similarly significant effects. Requires contractual necessity, explicit consent, or safeguards.
Mandatory DPIAs
AI systems that conduct large-scale profiling, parse special category data, or make automated evaluations about individuals are legally subject to formal Data Protection Impact Assessments prior to launch.
Auditing Framework
The ICO's specialized framework actively audits AI deployments, assessing fairness in training weights, data minimization policies, consent models, and transparency logic.
Specific Risks AI Systems Create Under GDPR
Traditional security checks miss these structural privacy challenges inherent to ML architectures.
Training Data and Lawful Basis
LLMs trained on public data scraped from the web often contain PII without consent. Training models on proprietary operational datasets requires a documented lawful basis and inclusion in your RoPA.
Model Outputs & Subject Rights
Models can reproduce personal training data in user outputs, creating disclosure risks. Satisfying erasure (the "Right to be Forgotten") if data is encoded in weights remains a complex challenge.
Automated Decision-Making
Credit evaluations, recruitment screening, and insurance pricing software often trigger Article 22. Deploying these without human contestation mechanisms creates severe regulatory exposure.
Transparency & Algorithmic Logic
GDPR demands that organizations offer "meaningful information about the logic involved" in automated outcomes. Vague references to "algorithms" are insufficient for ICO transparency standards.
High-Risk AI System Auditing
Systems used in recruitment, credit rating, and critical sectors carry strict compliance obligations, including conformity reviews, registration in the EU database, and documented human oversight loops.
How We Deliver AI Compliance
AI-Specific DPIA
Assess training data provenance, automated decision-making variables under Article 22, bias vectors, output risk, and third-party API configurations (GPT, Claude, Gemini).
Training Data Governance
Identify PII in training sets, document lawful bases, confirm data minimisation, and establish strict retention/deletion protocols for post-deployment weights.
EU AI Act Readiness Assessment
Categorize systems (unacceptable, high, limited, or minimal risk), map Gap analyses, and compile conformity assessments and technical documents.
Targeted Risk Assessments
- Automated Decision-Making (ADM) Audits: Document Article 22 triggers, establish human review capability validation, and organize access structures.
- Input & Output Security Analysis: Evaluate prompt injection PII leakage, output memory disclosure risk, and negotiate third-party processor DPAs.
- Explainability Framework Design: Build customer-facing logic templates demonstrating how decisions are calculated to satisfy the ICO.
Our Process — Step by Step
A structured, engineering-aligned methodology to configure secure, audit-ready AI workflows.
AI Mapping
Interview engineers and review system architecture to map training sources, APIs, and model pathways.
Risk Class
Identify vulnerabilities across training data, output leakages, ADM, and EU AI Act boundaries.
Reg Mapping
Map identified risks against GDPR Article 22, ICO AI parameters, and EU AI Act obligations.
Mitigation
Design technical prompt filters, PII scrubbers, human oversight loops, and governance registries.
Evidence
Deliver the complete pack: AI-specific DPIA, EU AI Act readiness log, and regulator-ready evidence.
Monitoring
Establish automated review triggers for model re-training, API changes, and legislative shifts.
AI Governance Control Dashboard
Protect your machine learning pipelines and monitor data protection standards. Our portal logs every conforming action to verify safety protocols internally.
- Centralized AI Risk Register: Track severity indicators, mitigation steps, and residual threats as models change.
- Conformity Evidence: Maintain human oversight verification stamps, DPA registries, and training pipeline audits in one central ledger.
- EU Act Phase-In Alerts: Get automated roadmap notifications for upcoming regulatory milestones.
Contact Information
Building or deploying an AI product? Speak with a KewData AI privacy specialist before your system goes live.
Phone Number
Email Address
Office Location
Office #126, Centurion House, London Road, Staines-Upon-Thames, Surrey, England, TW18 4AX
The time to address privacy risk is before deployment, not after.
Ensure your artificial intelligence and machine learning pipelines are GDPR and EU AI Act compliant. Speak with a specialist today.
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