AI Privacy Risk Testing and Compliance Support
Service Description
Your AI models might be leaking personal data or trade secrets. GDPR and the AI Act expect you to check this — not hope for the best. We deliver reproducible and defensible testing, so your decisions — and your audits — rest on evidence instead of assumptions.
Organisations that build or deploy AI models must meet strict privacy and cybersecurity obligations under GDPR, the EU AI Act, and sector‑specific rules. Models can unintentionally memorise and leak personal data through attacks such as membership inference, data reconstruction, and data extraction — creating real compliance and reputational risks. GDPR requires organisations to prevent downstream exposure of personal data and to honour the Right to be Forgotten, which in practice demands testing whether individual training data still leaves detectable traces inside a model. Beyond privacy, AI models may also reveal proprietary or confidential business information, turning leakage into an IP and cybersecurity risk, not just a data‑protection issue. The EU AI Act reinforces this by requiring testing under foreseeable misuse, including privacy‑relevant attacks. LeakPro provides structured, empirical stress‑testing of AI models to measure privacy and IP leakage, validate privacy‑enhancing technologies, and generate audit‑ready evidence for DPIAs, GDPR, and AI Act compliance.
This service maps to the following articles within AI-act: • Article 9 Risk management system — Continuous lifecycle risk management to keep your AI compliant and under control. • Article 10 Data and Data Governance — Validate that your training data meets state-of-the-art privacy and security standards. • Article 15 Accuracy, robustness and cybersecurity — Proactively detect and mitigate confidentiality attacks before they become real risks.
The audit reports constitute evidence that feeds into the technical documentation (Article 11 — Technical documentation, Annex IV).
How can the service help you?
• Detect leakage of personal data, sensitive attributes, or proprietary information.
• Check data traceability to support GDPR Right‑to‑be‑Forgotten obligations.
• Validate and tune synthetic data, federated learning, and differential privacy.
• Provide quantitative leakage‑risk indicators for audits, DPIAs, and procurement.
• Advise on privacy‑resilient AI design already at investment and planning.
How the service will be delivered
Logistics • Delivered via RISE secure environments or customer infrastructure. • Supports models across text, tabular, image, time‑series and graph modalities. • Option for iterative collaboration during model development or one‑off audits.
Delivery period Engagements are planned in phases depending on the scope, starting with a general assessment (qualitative), followed by attack simulation (quantitative) and reporting.
Duration • Short engagement: 3–6 weeks • Complex model audits or PET optimization: 2–4 months
Customer requirement • Provide intended use-case, regulatory context, and risk appetite • Documentation of model development and system environment. • Optional: Access to model artefacts and training data for technical study.
Deliveries • Tailored attack suite configuration • Execution of privacy and IP leakage attacks • PET tuning recommendations • Compliance‑aligned summary (reporting evidence for GDPR, AI Act, etc)
Output • Technical leakage‑risk metrics • Training data traceability assessment (Right‑to‑be‑Forgotten) • Harm‑oriented explanation for DPIAs • Recommendations for mitigation and governance
