AI Governance & Risk

Data privacy

Privacy-by-design for AI and analytics, including data minimization, consent and cross-border considerations.

Vue d’ensemble

AI and analytics depend on data, and much of that data relates to people. Obligations under laws such as GDPR, HIPAA and US state privacy laws apply throughout the lifecycle, from collecting training data to generating outputs. Getting privacy right early avoids costly redesigns and protects trust.

We help teams apply privacy-by-design to AI and analytics initiatives. That includes data protection impact assessments for higher-risk processing, data minimization and purpose limitation, de-identification and pseudonymization, consent and lawful basis analysis, and retention rules. For generative AI, we address specific risks such as personal data in prompts and training sets, model memorization and data shared with third-party model providers.

We also support cross-border considerations for organizations operating across regions, including data residency and transfer mechanisms. Our role is to translate requirements into practical technical and process controls, working alongside your privacy office and legal counsel, who remain responsible for legal interpretation.

Ce que vous obtenez

01Privacy risk assessment for AI and analytics initiatives
02Support for data protection impact assessments
03Privacy-by-design requirements and technical controls
04De-identification and data minimization approach
05Guidance on personal data in generative AI solutions

Technologies que nous maîtrisons

OneTrust
Microsoft Purview
BigID
Microsoft Presidio
Data masking and tokenization tools
In-tenancy model endpoints and private networking

Nous sommes indépendants des éditeurs : les outils sont choisis en fonction de vos besoins et de vos investissements existants.

Les questions que l’on nous pose souvent

Can we use personal data to build AI systems?

Often yes, with the right lawful basis, safeguards and minimization. We help identify what data is genuinely needed and which techniques, such as pseudonymization or synthetic data, can reduce risk, while your legal counsel confirms the legal position.

Is it safe to send personal data to external AI model providers?

It depends on the provider terms, data location, contractual protections and the sensitivity of the data. We assess these factors and design options such as redaction, in-tenancy endpoints or self-hosted models where needed.