There are four realistic ways to keep production PII out of Salesforce sandboxes: Salesforce’s own Data Mask package, two kinds of third-party masking products, and masking applied during the copy itself. Here is how they differ, dimension by dimension.
The unnamed options below are real, widely-used commercial products, not composites. Their capabilities are taken from each vendor’s public documentation as of the review date shown, and cells we could not verify publicly say so. We anonymize the names because this page is about capabilities, not vendors.
capabilities reviewed July 2026, from vendor-published documentation.
| Dimension | Salesforce Data Mask | Option B | Option C | Syntharil |
|---|---|---|---|---|
| When masking happens | Runs only in a sandbox after it already exists — masking happens in place, not during the copy itself | Runs on data already in the org — filters to records last modified before the job started, then masks in place via the Bulk API | Anonymizes data as part of the seeding step itself — masking happens during the copy | During the copy — target org only ever receives masked values |
| Deterministic output | Varies / not documented | Yes — masking algorithms produce a consistent result for the same input under a given algorithm configuration | Varies / not documented | Yes — same input always maps to the same masked output |
| Cross-object consistency | Varies / not documented — only object-specific guidance exists (e.g. Person Accounts need masking configured separately per object) | Yes — referential integrity is retained across data sources masked together | Yes — preserves relational structure so test environments mirror production | Yes — masked values match across related objects, joins keep working |
| PII auto-detection | None — admins manually select objects and fields and configure masking rules for each one | Built in for the standard Salesforce schema, with sensitive fields pre-identified and assigned a masking algorithm | Yes — auto-detects sensitive fields using Salesforce’s own data classification framework | Email, phone, and SSN/Tax-ID fields recognised automatically |
| Custom field coverage | Standard and custom objects/fields supported, except checkbox, lookup, and picklist field types | Custom fields are processed as part of job setup alongside the standard schema | 40+ ready-to-use anonymization patterns for standard and custom objects, plus custom regex patterns | Any field can be marked for masking; 8 styles |
| Where it runs | Managed package installed in the production org; the masking job itself runs from a sandbox created from that org | External data automation platform that connects to Salesforce through configured connectors | Varies / not documented | In transit, as part of a replication or seeding run |
| Setup effort | Install the managed package, obtain masking permission-set licenses, then configure masking rules per object/field | Configure a Salesforce connector and a masking ruleset; org automations may need disabling before a run and re-enabling after | Varies / not documented | None beyond connecting the orgs — masking is part of the copy config |
| Licensing shape | Add-on license to a Salesforce edition (Professional/Enterprise/Unlimited/Developer), not a standalone subscription | Positioned as one capability inside a broader data-automation platform, not sold as a Salesforce-only point product | Sold as part of a data-security product suite/category, not as a standalone tool | SaaS subscription with a free tier |
Salesforce’s own managed package for masking sandbox data — installed in the production org and run against sandboxes created from it, using four configurable masking types: replace with random characters, replace with library values, pattern-based replacement, or delete.
Where it wins: Native to Salesforce and licensed as an add-on rather than a separate vendor relationship — it covers both standard and custom objects and fields, with four selectable masking types.
Trade-offs: Every object and field must be manually selected and configured, with no automatic PII detection; masking runs only after data already exists in the sandbox; checkbox, lookup, and picklist fields, plus External Objects, Platform Events, and BigObjects, are excluded; masking bypasses the org’s own workflow rules, triggers, and validation rules.
An external data automation platform that connects to a Salesforce org through connectors and reads and writes records via the Salesforce API to mask sensitive data, as one part of a broader masking platform that also covers many other systems.
Where it wins: Deterministic masking algorithms, sensitive fields in the standard Salesforce schema pre-identified with a matching algorithm, referential integrity retained across masked data sources, and custom-field processing built into job setup.
Trade-offs: Because masking reads and writes large volumes of data directly against the org, Salesforce’s own automations can activate mid-run and may need to be temporarily disabled and re-enabled; setup is connector and metadata configuration work rather than a native in-app install; masking runs on data already in the org, not during a copy.
A Salesforce data-security vendor offering data masking as part of its Data Security product category, anonymizing production data before it is seeded into a target org.
Where it wins: Auto-detects sensitive fields using Salesforce’s own data classification framework, ships 40+ prebuilt anonymization patterns plus custom regex patterns covering standard and custom objects, and preserves relational structure across masked data.
Trade-offs: Public documentation doesn’t specify deployment architecture, setup steps or prerequisites, or whether masked values are deterministic and repeatable across runs.
Syntharil masks personal data as it copies records into a sandbox — deterministically, so the target org stays realistic and referentially consistent without ever holding a real customer value.
Where it wins: You want the sandbox to never contain raw PII at any point, masked values that stay consistent across objects and runs, and masking that needs no separate tool or post-refresh step.
Trade-offs: Masking is part of a data copy — it does not mask records already sitting in an org you are not copying into. For masking data in place, an in-org tool fits better.
See how Syntharil’s masking works in detail on the data masking page →, or compare hands-on methods in How to mask PII in Salesforce sandboxes →.
Two realistic categories: third-party masking products that mask data already sitting in an org, or masking applied during the sandbox copy itself. Syntharil takes the second approach — it masks personal data in transit during a replication or seeding run, so the target org never receives a raw value in the first place.
Deterministic masking maps the same input to the same output every time, so a contact’s masked email matches wherever it appears — across objects, and across runs. Random replacement breaks cross-object joins and makes test results unrepeatable.
If masking runs after the data lands, the sandbox briefly holds raw production PII and every person with sandbox access can see it until the masking job finishes. Masking during the copy means the sandbox never receives a real value.
It depends on the product: Salesforce Data Mask doesn’t document general cross-object consistency, while the two third-party options above each say they preserve relational structure or referential integrity. What actually keeps foreign keys and joins working is deterministic masking — Syntharil’s masking is deterministic by design, so masked values stay consistent across objects and runs.