Privacy Management & Data Protection Topics
Privacy compliance, data protection frameworks, privacy incident investigation, and regulatory requirements. Covers privacy impact assessments, data classification, regulatory interpretation, and privacy-first operational practices.
Data Protection and Governance Strategies
Covers practices to protect and govern data throughout its lifecycle. Topics include protecting data at rest and in transit through encryption and key management; data classification and access control models; backup, recovery, and disaster recovery strategies; retention and secure disposal policies; auditing and logging for compliance; masking and anonymization techniques for privacy; and regulatory considerations when designing data protection measures.
Data Privacy and Compliance
Covers principles, frameworks, and operational practices for managing personal and sensitive data in compliance with law and ethics across contexts such as research and marketing. Topics include regulatory regimes and requirements for data protection, privacy by design, consent management and informed consent procedures, rights subject mechanisms including data access and deletion requests, data retention and deletion policies, deidentification and pseudonymization techniques, Institutional Review Board and research ethics considerations, vendor and third party data processing agreements, auditing and compliance monitoring of systems, privacy impact and risk assessments, secure data storage and access controls, breach response and notification processes, and how platform and marketing technology capabilities affect compliance. Candidates should be able to explain both conceptual requirements and practical implementation tradeoffs when applying privacy and compliance controls in research operations and marketing technology stacks.
Apple Privacy and Security Philosophy
Candidates should demonstrate a practical and architectural understanding of a privacy first engineering ethos and how that philosophy drives cryptographic choices. Key areas include minimizing data collection, favoring on device processing when feasible, default encryption of data at rest and in transit, hardware backed key storage and attestation, and designing for user control and transparency. Candidates should be able to explain privacy preserving techniques such as client side encryption, privacy preserving analytics, federated approaches, secure multiparty computation, and privacy aware key lifecycle decisions. Practical discussion should cover trade offs between functionality and data minimization, how hardware constraints of secure coprocessors affect design, and examples of how to advocate for and operationalize privacy first principles in product and engineering discussions.