Database Forensics: Unveiling Data Modifications with Change Tracking

• In this method, you create a separate table to store information about changes made to the original table.
• You can set up triggers on the original table. A trigger is a special program that runs automatically whenever a specific event happens in the database, like inserting, updating, or deleting data in a table.
• Whenever there's a change in the original table, the trigger fires and inserts a new record into the audit table. This new record typically includes details like:
  • What kind of change occurred (insert, update, delete)
  • Before and after values of the changed data (for updates)
  • Timestamp of the change
  • Username of the person who made the change (if applicable)

Change Data Capture (CDC):

This is a built-in feature available in some databases (like Microsoft SQL Server). CDC captures information about all the data modifications (inserts, updates, deletes) happening on a table.

• Unlike triggers, CDC is usually an asynchronous process, meaning it runs in the background without affecting the performance of the main database operations.
• The captured data typically includes details similar to what triggers would record in an audit table, but it might be stored differently depending on the database system.

Choosing the right approach depends on several factors:

• Database system: Not all databases offer CDC functionality.
• Level of detail needed: Audit tables might offer more flexibility in what data you track about the changes.
• Performance impact: Triggers can add some overhead to database operations, while CDC is typically less intrusive.

Here are some additional points to consider:

• You'll need to decide how long to store the change data. Audit tables can grow large over time.
• Security is important. Make sure only authorized users can access the audit tables or CDC data.

CREATE TRIGGER update_tracker AFTER UPDATE ON your_table
FOR EACH ROW
INSERT INTO audit_table (record_id, operation, changed_column, before_value, after_value, timestamp)
VALUES (OLD.id, 'UPDATE',  SUBSTRING_INDEX(CONCAT(COLUMN_NAME), '.', -1), OLD.changed_column, NEW.changed_column, NOW());

Explanation:

• This trigger fires after an UPDATE operation on the your_table table.
• It inserts a new record into the audit_table containing details like:
  • record_id: The ID of the updated record (from the original table)
  • operation: "UPDATE" in this case
  • changed_column: Name of the specific column that was updated (using SUBSTRING_INDEX to extract the column name from the full path)
  • before_value: The value before the update
  • timestamp: The time of the update

Change Data Capture (Microsoft SQL Server):

Enabling CDC on a table:

ALTER TABLE your_table ENABLE CHANGE_DATA_CAPTURE;

This simple command enables CDC for the your_table. Captured data will be stored in system tables maintained by SQL Server. You can then query these system tables to see the change information.

• Temporal tables allow you to see the history of changes made to a table. They essentially create a separate version for each record whenever it's updated.
• You can query the temporal table to see the data as it existed at a specific point in time.
• This approach eliminates the need for a separate audit table and provides a more comprehensive view of historical data. However, it can also increase storage requirements.

Version Control Systems for Database Schema:

• Some database platforms and tools integrate with version control systems like Git. This allows you to track changes made to the database schema itself (like adding/removing columns or tables).
• While not directly tracking data changes, it can be helpful for understanding how the data structure evolved over time and identify potential impacts on data integrity.

Database Auditing Tools:

• Third-party database auditing tools offer a comprehensive solution for tracking data changes.
• These tools can often capture not just data modifications but also user activity, login attempts, and other database events.
• They may provide advanced features like alerting, reporting, and data anonymization. However, they typically come at an additional cost.

Choosing the best method depends on your specific needs:

• Granularity of change tracking: Do you need to track all data modifications or just specific columns?
• Historical data requirements: How long do you need to keep track of changes?
• Performance considerations: How much overhead can your database handle?
• Budget: Are there any cost constraints for implementing additional tools?