test data privacy best practices methodology bill mackey subject matter expert
TRANSCRIPT
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Test Data Privacy Best Practices Methodology Bill MackeySubject Matter Expert
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Introduction
Why Do Companies Care About Data Privacy?
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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.
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Data Breaches Reported Since the ChoicePoint Incident
2846 Incidents Reported Between 2-15-05 – 1-19-12543,066,426 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
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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
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Best Practices ApproachtoData Privacy
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Technology alone is not the answer
Services
• Repeatable Best Practices • Assessment• Implementation• Superior Expertise with
o 3rd Party Software
o Financial
o Healthcare
o Government
• Meet dates within high risk projects
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
Methodology
• Data Analysis o Analyze metadata o Discover PII o Classify data
• Designo Associate disguise rules o Define extract criteria o Identify target environment(s)o Identify load method(s)o Define population strategy
• Developo Extract data and relationshipso Apply rules across data sourceso Load data
• Delivero Produce reportso Audit resultso Enable best practices
Comprehensive Solution
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Deliver – Deploy and maintain data protection processes
Develop – Build the processes to disguise test data
Design – Define strategies for disguising test data
Process: Data Privacy Methodology
Analyze – Understand each application’s sensitive information
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Data Privacy Best Practices
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Data Privacy Project Plan
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Data Privacy
Best Practices Process Overview
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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.
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Operational StructureCentralized- 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.
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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
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Project Overview – Planning
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Project Phases
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Perform the Analyze methodology phase Data Model Analysis Function Model Analysis
Perform the Design methodology phase Design extract process Design disguise techniques Design load process
Perform the Develop methodology phase Creation and population of Translation/Association tables Creation and population of Encryption keys Development and Unit Testing of Extract/Disguise/Load tasks
Perform the Deliver methodology phase Create the repeatable process
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Data Privacy
AnalysisPhase
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Analysis
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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.
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Managing Analysis Tasks
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Data Model Analysis
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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.
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Data Privacy_1.1.1.4_Data_Model_Analysis
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Function Model Analysis
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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.
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Analysis Tasks
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CONTACT _ TBL
PK , FK 1 CUSTOMER _ NUMBERPK CONTACT _ ID
CONTACT _ NAMETITLECONTACT _ CODEADDRESS
CITYSTATE
ZIP _ CODECOUNTRYAREA _ CODETELEPHONE _ NUM
PART _ TBL
PK PART _ NUMBER
PART _ NAMEEFFECT _ DATEEQUIVALENT _ PART
PURCH _ PRICESETUP _ COSTLABOR _ COSTUNIT _ OF _ MEASUREMATERIAL _ COSTREWORK _ COSTAVAILABILITY _ IND
ENGR _ DRAW _ NUM
ORDER _ LINE _ TBL
PK , FK 1 ORDER _ NUMPK ORDER _ LINE _ NUMBER
FK 2 PART _ NUMPLAN _ QTYUNITS _ COMPLETEUNITS _ STARTEDSCRAP _ QTYSTART _ DATELINE _ STATUS
CUSTOMER _ HIST _ TBL
CUSTOMER _ ROWIDCUSTOMER _ NUMBERCOMPANY _ NAMETELEPHONE _ NUMCONTACT _ NAMECONTACT _ TITLE
SUPPLIER _ TBL
PK , FK 1 PART _ NUMBERPK SUPPLIER _ CODE
SUPPLIER _ NAMESUPPLIER _ MODEL _ NUMWHOLESALE _ PRICEDISCOUNT _ QUANTITYPREFERRED _ SUPPLIER
LEAD _ TIMELEAD _ TIME _ UNITS
ORDER _ TBL
PK ORDER _ NUMBER
FK 1 CUST _ NUMSOC _ SEC _ NUMCREDIT _ CARD _ NUMMOTHERS _ MAID _ NAME
ORD _ TYPEORD _ DATEORD _ STATORD _ AMOUNTORD _ DEPOSITORD _ LINE _ COUNTSHIP _ CODESHIP _ DATEORD _ DESCRIPTION
CUSTOMER _ TBL
PK CUSTOMER _ NUMBER
COMPANY _ NAMEADDRESS
CITYSTATE
ZIP _ CODECOUNTRYAREA _ CODETELEPHONE _ NUMCONTACT _ NAMECONTACT _ TITLECONTACT _ ADDRCONTACT _ CITYCONTACT _ STATECONTACT _ ZIPCONTACT _ COUNTRYCONTACT _ AREA _ CDCONTACT _ TELEPHONE
Data Modeling Tools Data Management ToolsFile-AID/DB2 / DBA-Xpert Impact Analysis
File-AID/Data Solutions Analysis
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Utilize Technology For Analysis
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Understand the Sensitive Elements
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Document Analysis Results
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Data Privacy_1.1.1.5_Data_Model_Analysis
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Design Overview
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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
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Design
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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
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Managing Design Tasks
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Data Extract Design
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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)
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Data Disguise Design
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• 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).
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Data Disguise Techniques
Replace sensitive values with meaningful, readable data using a translation table
Generate fictitious data from scratch or from some other source
Replace sensitive values with formulated data based on a user-defined key
Replace sensitive dates consistently while maintaining the integrity of a date field
Conceal partial fields
Encrypt
Translate
Age
Mask
Generate
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Data Privacy_1.2.2.1_Disguise Rule Design
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Data Privacy_1.2.2.3_Disguise Rule Design
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Data Privacy_1.2.3.3_Data Load Design
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Data Privacy_1.2.3.4_Data Load Design
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Data Privacy Develop Phase
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Develop Phase
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Develop
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Subset Extract
Load Maintain Integrity
• Build• Test• Validate
z/OS
Distributed
Test
z/OS
Distributed
Production
Data Privacy Manager
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Develop - z/OS Relationships
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AR/RI
Production
z/OS
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Develop - z/OS Extract
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z/OS
Production
SubsetExtract
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Develop - Distributed Related Extract
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Distributed
Production
SubsetExtract
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Develop - Disguise
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• Build• Test• Validate
Test Data PrivacyManager
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Develop - z/OS Load
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DisguisedExtract
Load Maintain Integrity
Test
z/OS
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Develop - Distributed Load
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Test
LoadMaintainIntegrity
ExtractFile
Distributed
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Validate Results
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Execution Reports
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Audit Reports
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Data Privacy
Deliver Phase
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Deliver
Production TestSystem TestUnit Test
QA TestAcceptance Test
Apply Privacy Rules
Subset Extract
Load Maintain integrity
DataPrivacy Manager
z/OS
Distributed
z/OS
Distributed
z/OS
Distributed
z/OS
Distributed
z/OS
Distributed
z/OSz/OSz/OSz/OSz/OS
DistributedPrivacy Audit Reports
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Managing Delivery Tasks
SystemUnit
QAAcceptance
Fictionalized Data
Privacy Audit Reports
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Deliver - Disguise Rule Administration
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DisguiseRules
Test Data Privacy Manager
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Document - Extract & Disguise Reports
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Document - Audit Reports
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Data Privacy_1.4.1_Deliver Execution Sequence
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Data Privacy_1.4.1.1_Deliver Execution Sequence
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Data Privacy Solution
Product TechnologyTools that can deliver quality data that meets the integrity, consistency and usability demands of your data privacy requirements
ProcessA clear strategy backed up by a methodology that serves as a roadmap or blueprint for an enterprise-wide data privacy initiative
ExpertiseThe knowledge and experience to effectively manage the process and drive the technology to implement data privacy assurance in the application testing environment
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© 2011 Compuware Corporation — All Rights Reserved
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