Privacy & Security
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Safeguarding sensitive data through privacy-first design and resilient infrastructure that prevents misuse, breaches, and unauthorized access.
Protecting individuals’ rights through strong data governance, privacy-preserving design, and robust system safeguards.
AI systems often rely on large volumes of personal or sensitive data. Privacy must be engineered through design principles such as data minimization, differential privacy, federated learning, and secure access controls. Security must go beyond compliance, ensuring resilience against adversarial threats and unauthorized access throughout the AI lifecycle.
“Personal data shall be processed in a manner that ensures appropriate security... including protection against unauthorised or unlawful processing.”
— General Data Protection Regulation (GDPR), Article 5(1)(f)
 Real-World Applications
Finance
Data Governance for Payment Infrastructure
Working with a global payments provider, I helped design cross-border AI workflows that complied with GDPR and regional financial regulations. My role focused on ensuring that sensitive transaction data used in onboarding and fraud detection was protected via secure model training and access controls.
Cybersecurity
Privacy-Preserving Threat Intelligence
In a multinational threat-sharing platform, I advised on embedding data minimization techniques into AI pipelines that handled sensitive inter-agency traffic. We implemented secure computation and obfuscation strategies to maintain actionable intelligence without exposing identifiable or classified signals.
Healthcare
GDPR-Compliant Cancer Care AI
In collaboration with cancer organisation, I supported the design of a dual-interface cancer care platform that balanced clinical insight with strict privacy protections. We employed cloud-based security protocols and role-based access control to protect both patient and clinician data.