1. The Privacy Engineer’s Manifesto
Getting from Policy to Code to QA to Value
Michelle Finneran Dennedy
Vice-President and Chief Privacy Officer
September 17, 2018

2. The Network has Evolved
Five Stages of the Information Age
Intelligence
Dynamic content
data-centric &
person-centric
environment
Net
Manage data inside and outside the firewall
Firewall
Keep data within the firewall
Extranet
Manage data through the firewall
Access Manage data through IDM
and access
control
We added one, right? The Age of Intelligence?
= big data, cloud, dynamic, dynamic content of unclear provenance (comes from multi-devices, multi-users, content in context)
The changing fundamentals of data use and creating greater challenges

3. Privacy Engineering is…
A discrete discipline or field of inquiry and innovation using engineering principles and processes to build controls and measures into processes, systems, components, and products that enable the authorized processing of personal information.
The creative innovation process to manage increasingly more complex data streams and data sets that describe individual humans.
The gathering and application of privacy requirements with the same primacy as other traditional feature- or process requirements and then incorporating, prioritizing, and addressing them at each stage of the development process, project, product or system lifecycle.
September 17, 2018

4. Privacy Engineering and Data Governance
Good Privacy Engineering is built on a foundation of data management and governance
September 17, 2018

5. Privacy Engineering and Data Governance
Data Governance / Stewardship
Data Use Steward
Data Collection Steward
Privacy Reporting Certification
Grade Privacy
Non-Privacy Attribute = 0
Potential PI Attribute = 1
PI Attribute = 2
Sensitive PI = 3
Encryption Needed?
Business Content / Rules
Privacy Rules
Privacy Reqrmnts
Purpose
Notice
Consent
Transfer (Third Party)
Access / Correction / Deletion
Security
Minimization
Proportionality
Retention
Act Responsbly
Screens & Reports Quality
Screen Business Content
Report Business Content
Screen Presentation / Aestetics
Report Presentation / Aestetics
Screen Design
Report Design
User Experience Rqrmnts
Data Governance Rqrmnts
Data Standards Compliance
Use of Metadata Documentation
Metric Driven Quality Assurance
Data Management Structure
Serious PI = 4
September 17, 2018

6. FIPPS / GAPP & Other Privacy Frameworks Distilled
Purpose – Collect and process for purposes that are relevant to the services being provided. PI must not be collected or used for purposes that are materially different from the original purpose for which the data was provided.
Notice - System creators, owners, and fiduciaries must explain to users how their information will be used, collected, protected, retained, kept accurate, accessed, corrected, or otherwise processed before any processing occurs.
Choice/Consent: Data subjects must consent to the collection and use of their personal information.
Transfer: Data should not be transferred to third parties for their own use without the data subject’s permission.
Access, Correction, Deletion: Data subjects must have a means of accessing the personal information that has been collected about them. They also are entitled to delete or amend false or inaccurate data.
Security: Use appropriate technical, logical, and administrative measures to ensure only authorized access and use of data.
Minimization: Collect and process the minimum necessary data to achieve the identified, legitimate intended purposes. The minimization principle is closely related to the purpose limitation requirement where the only the necessary PI is collected and processed to achieve a legitimate purpose.
Proportionality: Data collection should be legitimately proportional to need, purpose, and sensitivity of data. This requirement can be one-step further abstracted to connect that data to quality and value.
Retention: Retain data only as long as it is required.
Act Responsibly: Put a privacy program in place .
September 17, 2018

7. Privacy by Design (PbD)
September 17, 2018

8. Elements of Privacy Engineering Development
September 17, 2018

9. Designing Privacy Policies
September 17, 2018

10. Developing Privacy Requirements
Use Cases - . A complete course of events initiated by a Primary Actor.
Business Data Model
User Experience Requirements
September 17, 2018

11. Use Case Metadata Model
September 17, 2018

12. Developing a Privacy Engineering Solution
September 17, 2018

13. Development Life Cycle Stages
The Project Initiation and Scoping Workshop
The development of Requirement Use Cases and Class/Data Models defines the enterprise and seeks to understand the business requirements sought to be addressed. (See Chapter 5, “Developing Privacy Requirements Use Cases”.)
The solution design including prototyping the user interface for the project
The implementation stage that includes solution construction.
The Quality Assurance stage includes testing and user acceptance.
The solution rollout.
September 17, 2018

14. Model Relationships
September 17, 2018

15. System Activity Diagram with Privacy
September 17, 2018

16. Privacy Awareness and Readiness Assessments
September 17, 2018

17. Privacy Impact Assessments (PIAs)
Privacy Impact Assessment has five phases:
Business Process
Reporting Results
Verification
Analysis
Remediation
Data
Flows
Policies and Notices
Information Gathering
Remedy

18. Business Benefits
Greater organizational alignment with key partners can deliver major benefits to the entire organization. Key benefits include:
Greater business value from data, with less risk of misuse.
Increased operational efficiency
Better business decisions.
Lower cost of developing and deploying products, processes, systems and applications.
Reduced risk of privacy or security breaches.
Improved brand image and marketing data.
September 17, 2018

19. Business Benefits
Greater organizational alignment with key partners can deliver major benefits to the entire organization. Key benefits include:
Greater business value from data, with less risk of misuse.
Increased operational efficiency
Better business decisions.
Lower cost of developing and deploying products, processes, systems and applications.
Reduced risk of privacy or security breaches.
Improved brand image and marketing data.
September 17, 2018

20. Valuation Model Model 1: Model 2: Model 3: Model 4: Model 5:
Find something to count and count it:
Data breach, customer churn after direct enterprise activity or contextual activity (post Snowden in US and re US business or Government healthcare data in the UK etc. or ASIA PAC example).
Leverage GAPP maturity model and gauge cost to move to higher maturity model. Balance cost against brand valuation, data reliant programs, or marketing events, % of spend to acquire customers….
Read 10K annual reports or other publicly available legally binding documents—find data critical programs such as expansions into new jurisdictions, outsourcing, or cloud shifting business models or geographic mix. Make an educated or sample based guess regarding the importance of employee or customer data access.
Estimate IT spend regarding data centric systems, measure cost of management & governance for technology and in terms of FTE’s, legal or other professional services, or audit requirements.
Model 2:
Track time to deployment or proof of concept in a Privacy Engineering instance vs. traditional deployment. Start & track improvements in development; speed to deal closure,
Model 3:
Work with the grain of cyber insurance. Create checklist for coverage for various relevant scenarios: hacker or other criminal external compromise, Advanced Persistent Threat (APT) exploit, negligent loss of media device or physical encroachment etc. Generate cost of repair or staffing to attain reasonable coverage in the event of a cyber-incident.
Model 4:
Look for qualitative or reputational examples rather than numerical values. For example, there are tools and techniques leveraging other individual’s expressed curiosity, socially networked assertions or trends according to Big Data sets or other analytics that can show relevance to the enterprise and value to individual customers.
Model 5:
Leverage the known unknowns of brand valuation.
September 17, 2018

21. Some Thoughts for the Future
September 17, 2018

22. The Privacy Engineer’s Manifesto
Data about people is valuable in and of itself
A Privacy Engineer needs more than just technical skills to protect and extend the value of data
A privacy engineer draws from artistic creativity and expression to innovate
A privacy engineer learns from, but disregards, the failures of the past
We are all privacy engineers
For the privacy engineer, before the mantra to innovate, comes the mantra to do no harm
Innovation and complexity need not be the adversary of Privacy Engineering, though failure of imagination may be
The Privacy Engineer must be able to understand, calculate, mitigate, and accept risk
Privacy engineering happens inside of and outside of code
A privacy engineer needs to differentiate between bad ideas and bad implementations
September 17, 2018

23. For more information and Insight, read the THE PRIVACY ENGINEER’S MANIFESTO. Available now!!
September 17, 2018

25. Questions?
September 17, 2018