Privacy‑Preserving AI
The challenge of AI in regulated industries
AI drives innovation but can jeopardise sensitive data. Regulations like GDPR, HIPAA and RIA demand that personal data remains protected even during model training. Many startups overlook these constraints, exposing themselves to legal and reputational risks. Our privacy‑preserving AI service ensures your AI ambitions remain compliant.
Our techniques
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Federated learning: Train models across distributed devices or datasets without centralising raw data. This minimises data exposure and improves privacy.
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Differential privacy: Add statistical noise to training data or model outputs to prevent re‑identification of individuals.
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Confidential compute and secure enclaves: Use hardware‑based trusted execution environments (e.g. Azure Confidential VMs) to process sensitive data in an isolated, encrypted state.
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Private endpoints and key management: Configure network isolation and customer‑managed keys for all AI services bagh.co.uk. Ensure encryption at rest and in transit.
Implementation process
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AI use‑case assessment: Determine data sensitivity, regulatory requirements and model goals.
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Architecture design: Select appropriate privacy techniques and Azure components (e.g. Confidential Compute, Differential Privacy algorithms).
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Prototype and test: Build proof‑of‑concepts to validate model performance and privacy guarantees.
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Deployment and monitoring: Integrate the solution into your product, implement access controls and monitoring to detect drift or anomalies.