When creating a copy of a Salesforce instance—whether a sandbox or a full copy—one of the biggest challenges is how to anonymize the data without altering relationships between objects or external keys. Salesforce uses a highly relational data structure, and any anonymization that does not respect this can create inconsistencies, orphaned data, or broken relationships between objects.
For this reason, many organizations look for a reliable way to anonymize data in Salesforce while maintaining the environment’s consistency. In this article, we’ll explain how to apply anonymization in a Salesforce copy using Gigantics, preserving internal dependencies and avoiding common errors of manual or export-based approaches.
Why It’s Necessary to Anonymize Data in Salesforce Copies
When replicating a Salesforce instance, all operational data is transferred along with its relational structure. This means the copy contains the same sensitive information as production, leading to several risks.
When you create a copy of the production instance, it replicates:
- Customer data
- Opportunities and business activity
- Personal or sensitive information
- Custom objects with dependencies between them
This data often ends up distributed in environments where:
- Controls aren’t as strict as in production
- Internal or external profiles may access information they shouldn’t see
- There are integrations or internal validations that could expose data
That’s why many organizations seek a way to anonymize data in Salesforce without altering the instance’s overall behavior. The goal is to preserve functionality and reduce the exposure of sensitive information.
Ideal Scenario for Anonymizing Data in Salesforce with Gigantics
The Gigantics driver for Salesforce is designed to work in a specific case that maximizes stability and maintains system consistency.
This is the recommended flow:
- Start from a production instance.
- Create a copy of that instance (sandbox or full copy).
- Run Gigantics on that copy, using the same instance as both source and destination.
- In this model, the tap and the sink are exactly the same environment.
Our platform reads the data, applies transformations, and updates it within the same instance. This results in fewer points of failure and greater security in preserving relationships.
What Happens During the Anonymization Process
Below we detail how the process of anonymizing data in Salesforce with Gigantics works:
Reading Data from the Cloned Instance
It reads standard objects (like Accounts, Contacts, Opportunities) as well as custom objects defined by the organization.
Identifying Fields and Anonymization Rules
It applies the specified rules: substitutions, masking, consistent rewrites, or values compatible with Salesforce data types.
Preserving Internal Relationships
Gigantics respects:
- Parent–child links between native objects
- Cross-references
- Hierarchies
- External keys
The goal is that once data is anonymized, the instance maintains data coherence and consistency without exposing sensitive information.
Writing Anonymized Data to the Same Instance
By using the same environment as both source and destination, no IDs, paths, or essential system structures are altered. This reduces the likelihood of breaking existing flows.
The result is a fully operational Salesforce copy, but without exposure of real data.
Inserting New Data After Anonymizing Data in Salesforce
The driver also allows other operations besides anonymization:
Inserting New Data
You can add additional records that comply with anonymization rules from the start.
Generating Synthetic Data
Gigantics can generate completely new information to supplement the existing dataset.
This mode fulfills the basic functionality of data synthesis, although it requires specific configuration by the team and is currently being optimized to make it more usable, intuitive, and customizable.
Limitations of Traditional Approaches vs. a Structured Process
Many times, anonymization in Salesforce is attempted by means of:
- Partial exports to CSV
- Manual scripts
- Tools that do not respect internal relationships
- Batch transformations without considering dependencies
These methods often produce errors such as:
- Orphaned data
- Lost relationships between standard and custom objects
- Inconsistencies affecting the operation of the instance
Gigantics avoids these problems because it works on Salesforce's own model, respecting its complexity and preserving every relationship.
Conclusion: Ensure the Integrity of Your Salesforce Sandbox
Data anonymization in Salesforce does not have to be a manual and error-prone process anymore. To maintain the referential integrity of your test or development environments, it’s essential to use a solution that understands the complexity of your relational data model.
Gigantics offers a driver designed specifically to perform anonymization directly in your Salesforce copy. This ensures that relationships, IDs, and dependencies between standard and custom objects are preserved, delivering a sandbox environment that is operational, consistent, and free from sensitive data.
If your team needs an efficient way to meet privacy policies and speed up development cycles without compromising functionality, discover the structured method for data anonymization in Salesforce.