Microsoft Dynamics 365 Customer Experience Analyst : Configure auditing of data

Auditing in Dataverse is a powerful feature that helps organizations track and monitor changes made to their data for compliance, security, and operational insights. When enabled, auditing records key details such as who made the change, when it was made, and what values were updated, created, or deleted. This provides a historical log that can be used for troubleshooting issues, meeting regulatory requirements, and ensuring accountability in business processes. Auditing can be configured at the environment, entity (table), and field levels, giving flexibility to track only the data that is critical to the business. By leveraging Dataverse auditing, organizations gain transparency and control over their data lifecycle while strengthening trust and governance.

Auditing in   Dataverse   is a feature that helps track changes to data for accountability, compliance, troubleshooting, and business insights. It works like a "black box recorder" for your environment—capturing who did what, when, and sometimes even why.

What Auditing Does

1.   Tracks Data Changes  

• Captures create, update, and delete actions on records.
• Shows the   old value   and the   new value   for each field.

2.   User Accountability  

• Records the   user   who performed the action.
• Stores the   timestamp   of the change.

3.   Compliance & Governance  

• Useful for industries like finance, healthcare, or government where audit trails are required.

4.   System Auditing  

• Not just business data—you can also audit changes to user roles, security privileges, or solution changes.

How It Works

 Enable Auditing   at 3 levels:

  1.   Environment level   → Turn on auditing globally.

  2.   Table level   → Choose which tables (e.g., Accounts, Contacts) are auditable.

  3.   Column level   → Select specific fields to audit (e.g., Postal Code, Email).

  Once enabled, Dataverse automatically writes logs to the   Audit table  , which stores all changes.

 Example

Imagine you have a   Contact   record:

 Key Benefits

• Transparency   → Everyone’s actions are traceable.
• Compliance   → Meets regulatory requirements like GDPR, HIPAA.
• Troubleshooting   → Quickly find out why a record looks incorrect.
• Security   → Detect unauthorized or suspicious activity.

Considerations

• Auditing can increase   storage usage   in Dataverse → Monitor audit log size.
• Not designed for   real-time reporting  —best used for historical tracking.
• For analytics, audit data can be exported to   Azure Data Lake   or   Power BI  .

Recommendations for Architects

1.   Define Audit Scope Clearly  

• Enable auditing   only on critical business tables and fields   (e.g., financial data, compliance-related attributes).
• Avoid enabling it everywhere to reduce storage and performance impact.

2.   Compliance & Governance Alignment  

• Work with compliance teams to ensure auditing supports regulations (GDPR, SOX, HIPAA).
• Define   data retention policies   (e.g., how long to keep audit logs).

3.   Storage & Cost Planning  

• Audit logs consume   Dataverse database storage   → estimate impact and plan capacity.
• For long-term retention, recommend   exporting audit data   to Azure Data Lake or Log Analytics.

4.   Security & Access Control  

• Restrict audit log visibility to   admins, auditors, or compliance officers  .
• Ensure logs themselves are not editable or deleted by end-users.

5.   Integration & Insights  

• Consider surfacing auditing data in   Power BI dashboards   for compliance monitoring.
• Use   Azure Synapse Link   or   Data Export   for advanced analytics.

 Recommendations for Developers

1.   Enable Auditing Selectively  

• Configure auditing at   table and column level   instead of enabling for entire schema.

            Example: Audit "Credit Limit" field in Account, but not "Fax Number".

2.   Performance Awareness  

•  Keep in mind that auditing adds   write overhead   on transactions.
•  Don’t rely on auditing as a   real-time trigger   (use plugins/Power Automate instead).

3.   Testing Audit Scenarios  

• Test audit logs during   create, update, and delete   operations.
•  Validate that the right fields are being tracked and visible in   Audit History  .

4.   Error Handling & Troubleshooting  

• Don’t depend on audit logs for   business logic validation  —use validation rules, plugins, or Power Fx.
• Use audit logs primarily for   tracking and investigations  .

5.   Automation Support  

• Provide ways to   export audit logs periodically   (e.g., scheduled Power Automate flows).
• Implement cleanup strategies if storage consumption grows too high.

Summary:

Auditing in Dataverse should be approached strategically, balancing compliance needs with performance and storage considerations. Architects should define a clear audit scope, enable it only for critical fields, align with governance and retention policies, and plan for long-term storage through exports to Azure or Data Lake. They must also secure audit logs and provide oversight through controlled access and monitoring. Developers, on the other hand, should implement auditing selectively, test thoroughly across create/update/delete operations, and avoid relying on audits for business logic. Instead, they should focus on performance-conscious configurations, automated export or cleanup processes, and ensuring audit data supports investigations and compliance reporting. Together, this approach ensures reliable, compliant, and cost-effective auditing in Dataverse.