Data Masking
Best Practices
Information Security Risk
The risk that sensitive
Information Security Risk
Government systems store a huge amount of sensitive information Vital Statistics Health Information Social Services Criminal Justice Financial InformationInformation Security Risk
Many people have access to the information for various different roles System End‐Users Application Administrators Data Consumers Application Support Staff Project Team Members External VendorsSensitive Information
General Information – Name, Address, Date of Birth, SIN, &c. Financial and Banking Information – Credit Card, Bank Account, Salary, &c. Health Information – MCP, E‐Health Records, Consent Management, &c.Potential Repercussions
In the event sensitive data becomes public – Regulatory and Legal Liability – Loss of Trust and Confidence – Salary Reduction / Loss of Employment – Damage to Reputation – Subject to Investigation – Cost of Incident ResponseRisk Stakeholders
• Government Executive • OCIO Executive • Client Department Executive • Project Manager and Project Team • Application Support • Infrastructure and Network Operations • System Administrators and End‐UsersRisk Mitigation
We use information security technologies to mitigate and control the risk – User Authentication and Access Control – Network Perimeter Defence – Virtual Private Networking – Intrusion Prevention & Detection Systems – Antivirus SystemsRisk Mitigation
We have information security processes to mitigate and control the risk – Information Management Assessment – Information Security Classification – Privacy Impact Assessment – Threat / Risk Assessment – Vulnerability AssessmentResidual Risk
Existing risk mitigation focuses primarily on the
product of system development
There is a significant residual risk related to the
Residual Risk
Production data is often being used in – Upgrade and Enhancement of existing systems – Migration to replacement systems – Development of Data Warehousing or Business Intelligence systems – Application Support – TrainingProject Exposure
• Project Managers and other Project Team Members are exposed to this risk • Non‐Disclosure Agreements recognize an awareness and intention to address the risk • Are the strategies employed by your team and your organization sufficient? • How much risk are you accepting?Data Masking
Avoids the Risk • Removes the need for production data in non‐production environments • Allows selected data to be obscured in production environments • Supports development, testing, training, application support, &c.Data Masking
“A set of techniques and technologies aimed at preventing the abuse of sensitive data by hiding it from users”“
The process of concealing private data...such that application developers, testers, privileged users, and outsourcing vendors do not get exposed to such data”Static Data Masking
• Begins by taking production data as input • Applies transformations to de‐identify records and remove sensitive information • Preserves structure of data by maintaining referential integrity in and between databases • Provides high‐quality, realistic test data for use in non‐production environmentsStatic Data Masking creates non‐production data Non‐Production User
Static Data Masking
Non‐Production Database with Masked Data Production User Values in Database Masked Values 1234‐6789‐1000‐4422 2233‐6789‐3456‐5555Dynamic Data Masking
• Creates an additional layer of security between databases and applications • Selectively masks sensitive information from users who do not require it to do their jobs • Provides fine‐grained, role‐based security • Allows security roles to be defined across multiple databases and applicationsAuthorized User Dynamic Data Masking applies rules based on user role Original Values 3890‐6784‐2945‐0093 3245‐9999‐2456‐7658 Scrambled Values 1234‐6789‐1000‐4422 2233‐6789‐3456‐5555 Unauthorized User A Unauthorized User B Masked Values xxxx‐xxxx‐xxxx‐0093 xxxx‐xxxx‐xxxx‐7658
