How do you handle PII and data privacy when building ML systems?

Start with data minimization — collect and retain only what you need. Identify PII (personally identifiable information) and apply techniques like anonymization, pseudonymization, aggregation, or differential privacy where feasible; note that true anonymization is hard and re-identification via linkage is a real risk. Under frameworks like the EU's GDPR, you need a lawful basis for processing, must honor rights such as access and erasure, and should run a data protection impact assessment for high-risk processing. Practical controls: access restrictions, encryption at rest and in transit, audit logging, and care that models don't memorize and regurgitate training PII. Because privacy law varies by jurisdiction (GDPR, CCPA/CPRA, others) and evolves, coordinate with legal/DPO rather than relying on generic rules.