Data Remediation Lecture Notes

Introduction

• Security Teams Role: Tasked with the protection, hygiene, and compliance of company data.
• Unstructured Data: Majority of data, difficult to manage, causing functional, financial, and regulatory issues.

What is Data Remediation?

• Definition: Process of cleansing, organizing, and migrating data to ensure it is properly protected and serves its purpose.
• Misconception: Not just about deleting data but correcting mistakes (remedy).
• Actions: Replacing, modifying, cleansing, deleting dirty data.

Data Remediation Terminology

• Data Migration: Moving data between systems or formats.
• Data Discovery: Finding patterns in data to identify structured and unstructured data.
• ROT Data: Redundant, Obsolete, Trivial data - 80% of unstructured data.
• Dark Data: Collected but unused data, costly and risky.
• Dirty Data: Inaccurate or incomplete data.
• Data Overload: Excessive low-quality data making management difficult.
• Data Cleansing: Standardizing data format.
• Data Governance: Managing data's availability, usability, integrity, and security.

Stages of Data Remediation

1. Assessment: Understand the value and sensitivity of data.
2. Organizing and Segmentation: Categorize data based on its purpose and protection needs.
3. Indexation and Classification: Classify data sensitivity.
4. Migrating: Move data to a new secure environment.
5. Data Cleansing: Actions like shredding, redacting to clean data.

Business Benefits

• Reduced Storage Costs
• Protection of Sensitive Data
• Reduced Data Footprint
• Compliance Adherence
• Increased Staff Productivity
• Minimized Cyberattack Risks
• Improved Data Security

When is Data Remediation Necessary?

• Business Changes: New systems, mergers, or acquisitions.
• Laws and Regulations: New data privacy laws.
• Human Error: Mistakes like downloading sensitive data improperly.

Barriers to Data Remediation

• Lack of Information: Unawareness of data's location and extent.
• Fear of Deleting Data: Concern over needing data in the future.
• Unclear Data Ownership: No established responsibilities.

Preparing for Data Remediation

1. Create Remediation Teams
2. Establish Data Governance Policies
3. Prioritize Areas for Remediation
4. Budget for Data-Related Issues
5. Discuss Remediation Expectations
6. Track Progress and ROI

Tools for Data Remediation

• Spirion Solutions: Provide tools for data discovery, classification, and remediation.
• Sensitive Data Platform: High accuracy in sensitive data management.

Conclusion

• Data remediation is crucial for managing data risks and maintaining compliance.
• Proper tools and strategies can ease the remediation process.