The aim of this program is to equip security and privacy professionals with the strategic and technical skills required to design and implement a comprehensive, lifecycle-focused data protection strategy:

• Data Protection Officers (DPOs) and Privacy Managers.
• Data Security Architects and Engineers.
• Compliance and GRC Professionals.
• CISOs and Security Directors.
• Cloud Security Specialists.
• Database and Storage Administrators.
• Legal Counsel focused on data privacy.

• Case studies on major data breaches and the role of DLP/encryption failure.
• Group activity designing a data classification and discovery program.
• Technical discussions comparing different encryption implementation models (e.g., TDE, client-side).
• Practical exercises tuning a mock DLP rule-set to minimize false positives.
• Role-playing a DPIA for a new product feature involving personal data.

Unit 1: Data Protection Governance and Strategy

• Developing a comprehensive data classification scheme (e.g., public, confidential, restricted).
• Automated and manual techniques for data discovery and inventory.
• Data flow mapping and identifying cross-border data transfers.
• Establishing data ownership and accountability within the organisation.

• The seven foundational principles of Privacy by Design (PbD).
• Integrating PbD into the System Development Lifecycle (SDLC).
• Data minimization strategies and purpose limitation.
• Conducting Privacy Impact Assessments (PIAs) and Data Protection Impact Assessments (DPIAs).

Unit 2: Encryption and Cryptographic Controls

• Encryption at rest (disk, database, file-level) vs. encryption in transit (TLS/SSL).
• Homomorphic Encryption and searchable encryption overview.
• Tokenization and anonymization/pseudonymization techniques.
• Regulatory requirements for strong encryption standards.

• Designing a secure and resilient Key Management Infrastructure.
• Hardware Security Modules (HSMs) and their role in key protection.
• Lifecycle of cryptographic keys (generation, storage, rotation, destruction).
• Managing keys across multi-cloud environments (e.g., AWS KMS, Azure Key Vault).

Unit 3: Data Loss Prevention (DLP) Implementation

• Defining sensitive data policies and detection rules (regex, exact data matching).
• DLP deployment models: Endpoint, Network, and Cloud.
• Techniques for reducing false positives and improving accuracy.
• Integration of DLP with email and collaboration tools (e.g., MS Teams).

• Developing incident response and workflow for DLP alerts.
• Tuning DLP policies based on business need and risk appetite.
• The role of User Behavior Analytics (UBA) in DLP enforcement.
• Measuring and reporting on DLP effectiveness and policy violations.

Unit 4: Data Protection in Cloud and Endpoint Environments

• Securing SaaS data (e.g., Salesforce, Office 365) using Cloud Access Security Brokers (CASBs).
• Data residency and sovereignty requirements in multi-cloud.
• Secure configuration of cloud storage (e.g., S3 buckets, Azure Blob Storage).
• Implementing encryption and access controls in serverless databases.

• Full disk encryption and file-level encryption for mobile devices.
• Mobile Device Management (MDM) and Mobile Application Management (MAM) controls.
• Securing data in transit from endpoints to corporate networks.
• The importance of access control in protecting data on unmanaged devices.

Unit 5: Data Governance and Emerging Trends

• Developing and enforcing data retention and destruction policies.
• Responding to Data Subject Access Requests (DSARs).
• Regulatory compliance oversight (GDPR, CCPA, HIPAA).
• Managing data in third-party and vendor relationships.

• The application of AI/ML for advanced data classification and risk scoring.
• Confidential computing and securing data while in use (processing).
• Data Mesh architectures and decentralized data governance.
• The impact of post-quantum cryptography on current encryption standards.