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Test Data Privacy Best Practices Methodology . Bill Mackey Subject Matter Expert. Introduction Why Do Companies Care About Data Privacy? . Worldwide Data Privacy Drivers. Regulatory Compliance… United States Gramm-Leach-Bliley Act, Sarbanes-Oxley Act

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test data privacy best practices methodology

Test Data Privacy Best Practices Methodology

Bill Mackey

Subject Matter Expert

worldwide data privacy drivers
Worldwide Data Privacy Drivers
  • Regulatory Compliance…
    • United States Gramm-Leach-Bliley Act, Sarbanes-Oxley Act
    • European Union Personal Data Protection Directive, 1998
    • Health Insurance Portability and Accountability Act (HIPAA)
    • Australia Privacy Amendment Act of 2000
    • Japanese Personal Information Protection Law
    • Canadian Personal Information Protection and Electronic Documents Act (PIPEDA)
  • Internal auditors are forcing data protection controls and procedures, especially for offshore use/outsourcing arrangements
  • Risk of exposure can cause significant damage
    • Corporate embarrassment, lawsuits, negative press, fines/penalties, loss of customers, etc.
data breaches reported since the choicepoint incident
Data Breaches Reported Since the ChoicePoint Incident

2846 Incidents Reported Between 2-15-05 – 1-19-12

543,066,426 Consumers Impacted

  • The catalyst for reporting data breaches to the affected individuals has been the California law that requires notice of security breaches, the first of its kind in the nation, implemented July 2003.
  • Personal information compromised includes data elements useful to identity thieves, such as Social Security numbers, account numbers, and driver's license numbers.

A Chronology of Data Breaches Reported Since the ChoicePoint Incident

Privacy Rights Clearinghouse, January 19, 2012

how are companies addressing this issue
How are Companies Addressing this Issue?
  • Signing non-disclosure agreements
  • Restricting security access to sensitive/confidential data
  • Applying minimal “de-identifying” rules
  • Implementing a complete data disguise solution with processes and procedures

Low Effectiveness

High Effectiveness

technology alone is not the answer
Technology alone is not the answer

Comprehensive Solution

  • Methodology
  • Data Analysis
    • Analyze metadata
    • Discover PII
    • Classify data
  • Design
    • Associate disguise rules
    • Define extract criteria
    • Identify target environment(s)
    • Identify load method(s)
    • Define population strategy
  • Develop
    • Extract data and relationships
    • Apply rules across data sources
    • Load data
  • Deliver
    • Produce reports
    • Audit results
    • Enable best practices
  • Technology
  • Related Data Extraction
  • Data Sub-setting
  • Data Format Conversion
  • Disguise Rules Definition
  • Common Rules Across the Enterprise
  • Unified Rules Repository
  • Support for Mainframe and Distributed Environments
  • Roles Based Authorization
  • Audit and Reporting
  • Services
    • Repeatable Best Practices
    • Assessment
    • Implementation
    • Superior Expertise with
    • 3rd Party Software
    • Financial
    • Healthcare
    • Government
    • Meet dates within high risk projects
process data privacy methodology

Analyze – Understand each application’s sensitive information

Design – Define strategies for disguising test data

Develop – Build the processes to disguise test data

Deliver – Deploy and maintain data protection processes

Process: Data Privacy Methodology
deployment approaches
Deployment Approaches
  • Two project approaches:
    • Progressive: Organizations that have large numbers of applications and multiple lines of business benefit more from a progressive approach. The progressive approach builds upon the success of early efforts, building up a library of disguise routines and process definitions that align with existing projects within the organization.
    • Parallel: Organizations that have small to medium numbers of applications benefit more from the parallel approach. The parallel approach covers a wider range of applications at the same time, which is possible when the applications are less intertwined or more independent. Both approaches use a risk based methodology.
operational structure
Operational Structure

Centralized- A single team responsible for performing the data masking function for all lines of business or application areas. This organization is also often referred to as a center of excellence model.

Benefits

Fewer resources need to be trained on the data disguise software and activities;

Increased control over consistency of the disguise techniques and behavior; and

Increased productivity of these resources as they work across applications.

Drawbacks

Increased effort during the Analyze phase as these resources gain the necessary application centric knowledge;

Increased duration as there are typically less of these resources, so more effort with less people results in long duration.

Decentralized- Each application group is responsible for the data masking functions.

Benefits

Existing application domain knowledge can be leveraged;

The duration of Analyze phase may be shortened as activities can be performed in parallel; and

This model streamlines the communication model between the groups.

Drawbacks

Increased effort related to training; and

Increased demand on communications in order to maintain consistency.

process how we get there
Process: How we get there
  • Establish an actionable roadmap
      • Determine the scope
      • Establish a strategy
      • Identify constraints (internal and external)
  • Select the technology
      • Recognized and adaptable
      • Support multiple environments, platforms, & techniques
  • Partner to gain the experience
      • Minimize first time hurdles, pit-falls, & dead-ends
      • Maximize analysis and design efficiency
analysis
Analysis

Analysis phase can be broken down into two major activities:

  • Identification and documentation of the data model (DM),
  • identification and documentation of the functional model (FM) components of the application.

These two activities provide the cornerstone for a Data Privacy initiative, and as such, are arguably the most critical of the entire project scope. 

data model analysis
Data Model Analysis

The goal of the Data Model Analysis activities is to provide knowledge about the environment’s data.

  • determine the elements that are considered sensitive
  • define their association to other data objects.
function model analysis
Function Model Analysis

identifies and documents information about the application processes.

  • determine what business rules and logic apply to the data considered sensitive or private.
  • Outline how the affected data should be changed.
  • Identify all data validations and checks done against sensitive fields within the application programs.
analysis tasks

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Analysis Tasks

Data Modeling Tools

Data Management Tools

File-AID/DB2 / DBA-Xpert Impact Analysis

File-AID/Data Solutions Analysis

design overview
Design Overview

Design is the second phase of the Compuware Data Privacy Best Practices methodology and it is broken down into three major activities:

  • Documentation of the Data Extracts to be created
  • Identification and documentation of the data disguise rules to be created/implemented
  • Documentation of the Data Loads to be created

These activities provide the background for the creation of the actual rules and specifications to create a Disguised copy of the data 

design
Design

Define application disguise strategy and process

  • Field-level disguise rules (encrypt, translate, age, generate)
  • Source extract criteria for data (filters, naming conventions, etc.)
  • Security rules for supporting files
  • Structure, value domain (content), population strategy for translate table(s)
  • Target environment(s) and load method(s) to be used
data extract design
Data Extract Design 

Identifies the required information to extract the data from the original source tables/files/environments.

  • Includes the following:
    • environmental data (region, subsystem, server, etc),
    • driving object identification (which table/file do we drive the extract from),
    • selection criteria information,
    • extract specific information needed to pull the needed information from the source tables/files.
  • Finally, the overall extract execution strategy will be documented (when to execute, frequency of execution, etc)
data disguise design
Data Disguise Design 
  • Takes the fields to be disguised and begin to scope out what exactly will be done to these fields to create a disguised test environment.
  • Identifies the specific disguise technique
  • selection criteria to be applied
  • field masking to be applied
  • If any translations will be done, the Translation Table information is also documented (creation data, fields to be created, etc).
data disguise techniques

Replace sensitive values with formulated data based on a user-defined key

Replace sensitive values with meaningful, readable data using a translation table

Replace sensitive dates consistently while maintaining the integrity of a date field

Conceal partial fields

Generate fictitious data from scratch or from some other source

Data Disguise Techniques

Encrypt

Translate

Age

Mask

Generate

develop
Develop

Data Privacy Manager

Production

Test

z/OS

z/OS

Load

Maintain Integrity

Subset

Extract

Distributed

Distributed

  • Build
  • Test
  • Validate
develop z os relationships
Develop - z/OS Relationships

Production

AR/RI

z/OS

develop z os extract
Develop - z/OS Extract

Production

Subset

Extract

z/OS

develop distributed related extract
Develop - Distributed Related Extract

Production

Subset

Extract

Distributed

develop disguise
Develop - Disguise

Test Data PrivacyManager

  • Build
  • Test
  • Validate
develop z os load

z/OS

Disguised

Extract

Develop - z/OS Load

Test

Load Maintain Integrity

develop distributed load

Extract

File

Develop - Distributed Load

Test

Load

MaintainIntegrity

Distributed

deliver

z/OS

z/OS

z/OS

z/OS

z/OS

z/OS

z/OS

z/OS

z/OS

z/OS

Privacy

Audit Reports

Distributed

Distributed

Distributed

Distributed

Distributed

Distributed

Deliver

SystemTest

Unit Test

Production

Test

Data

Privacy Manager

Apply Privacy Rules

Subset

Extract

Load

Maintain integrity

AcceptanceTest

QATest

managing delivery tasks

System

Unit

Fictionalized Data

Acceptance

QA

Privacy

Audit Reports

Managing Delivery Tasks
deliver disguise rule administration
Deliver - Disguise Rule Administration

Test Data Privacy Manager

Disguise

Rules

data privacy solution
Data Privacy Solution

Product Technology

Tools that can deliver quality data that meets the integrity, consistency and usability demands of your data privacy requirements

Process

A clear strategy backed up by a methodology that serves as a roadmap or blueprint for an enterprise-wide data privacy initiative

Expertise

The knowledge and experience to effectively manage the process and drive the technology to implement data privacy assurance in the application testing environment