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What is privacy?
Unlike Security, privacy can be hard to define.
Privacy Architecture for Data-Driven Innovation Nishant Bhajaria - - PowerPoint PPT Presentation
Privacy Architecture for Data-Driven Innovation Nishant Bhajaria - - PowerPoint PPT Presentation
Privacy Architecture for Data-Driven Innovation Nishant Bhajaria What is privacy? Unlike Security, privacy can be hard to define. Confidential Intro - Nishant Bhajaria Staff Privacy Architect History: Nike Netflix
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Confidential
Intro - Nishant Bhajaria
History:
- Nike
- Netflix
- Google Cloud
- Uber
Mandate: Cross-functional technical privacy strategy
Staff Privacy Architect
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Privacy
The Rules are changing
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So what does this mean?
- Privacy is “all hands on deck” not just legal
- Security ≠ Privacy
○ Security is necessary but not sufficient for privacy
- Think beyond breaches
○ Data collection and Internal misuse ○ Data sharing and External misuse
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Confidential Data Classification
- Answers questions
○ “What is this data?” ○ “How sensitive is this data?”
- Tiered ranking of user and business data
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Data Classification Example Category Example Data Sets
Tier 1: Highly Restricted Tier 2: Restricted Tier 3: Confidential Tier 4: Public Government Identifiers and location data (excludes personal data) Vehicle Data Non-Identifying Vehicle Data Public Information Social Security Card Driver’s License License Plate Number Proof of Insurance Make and Model Color Press Releases Product Brochures
Data Classification Examples
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Data Handling Requirements
“How can I protect this data?”
Collection Access Retention, Deletion, Sharing (internal/external)
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Why is Data Inventory vital?
Data Inventory and Tagging Data Use External Sharing
- User Apps
- Export/DSAR
- Third Party
Sharing
Collection
- User Apps
- Web Site
- Third-Parties
Deletion
- Retention Policy
Cannot apply data protection post collection without inventory
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Metadata discovery (UI, Crawlers, APIs,) UMS (In
- house global
metadata store) Data Sources Scanners/Classifiers Manual Scanning and detection (also supports AI models) Other data sources (Hive, Vertica, MySQL, etc) ML-powered classifiers (automated data detection) Data Inventory DB Decider UMS (In
- house global
metadata store) Deletion, Retention and
- ther privacy
services
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Metadata discovery (UI, Crawlers, APIs, etc) Data Sources Scanners/Classifiers Manual Scanning and detection (also supports AI models) Other data sources (Hive, Vertica, MySQL, etc) ML-powered classifiers (automated data detection) Data Inventory DB Decider Deletion, Retention and
- ther privacy
services
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Metadata Sources
UMS
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Metadata Registry/Definition
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Metadata Collection
Pull model Push model ○ Crawler (periodic) e.g. sample data, stats ○ Event-based (Event Listeners) e.g. data quality ○ Automated e.g. data retention policies ○ Crowdsource e.g. table descriptions
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