Salesforce teams often treat sandbox masking as a background administration task: refresh the environment, run the familiar configuration, and let development continue. That routine now has a deadline.

Salesforce ended support for the legacy Data Mask managed package on July 9, 2026. On December 31, its display becomes read-only. The replacement, Data Mask & Seed, is a native Salesforce application, but the transition is not a direct conversion of existing masking configurations. Salesforce instructs customers to create new policies in the replacement application; existing custom libraries are the exception because they are shared between the two versions. Salesforce's October 3 retirement update

That makes this more than a UI change. For healthcare organizations, insurers, and nonprofit foundations, a masking policy is part of the control that determines whether developers, testers, vendors, and training users can encounter real patient, member, claimant, donor, beneficiary, or employee information. The migration is complete only when the new process protects the required data after a refresh and still supports the business scenarios the sandbox exists to test.

What actually changes on December 31?

The deadline does not mean every sandbox suddenly exposes data on January 1. Salesforce's stated change is narrower: the legacy managed package is already unsupported, issues that arise will not be fixed, and the package display becomes read-only on December 31. The operational risk is that an organization remains dependent on a control it can no longer manage normally while its data model, integrations, and refresh schedules continue to change.

The replacement offers useful improvements. Salesforce describes a native app that requires no managed-package installation, suggests masking options based on field sensitivity and category metadata, supports locale-specific replacement values, provides more precise job errors, and can preserve existing field-value distributions for jobs below 20 million records. It also combines masking and seeding in one interface. Salesforce retirement update

Those features help, but they do not decide which data your organization should retain, transform, delete, or synthesize. Nor do they prove that a masked dataset satisfies a legal standard. HHS, for example, describes two specific HIPAA de-identification methods and notes that even properly de-identified data can retain some re-identification risk. Treat platform tooling as part of a governed control, not as a compliance conclusion. HHS de-identification guidance

The migration mistake to avoid: copying fields without rebuilding the control

A field-for-field recreation can preserve yesterday's blind spots. Mature Salesforce orgs accumulate custom objects, managed-package fields, free text, files, integration payloads, archived records, and formula outputs. A policy written two years ago may not cover the data copied into today's full sandbox.

Start with the purpose of each non-production environment. A developer sandbox for unit work, an integration sandbox, a full UAT environment, and a training org do not need identical data. For each environment, document who can enter it, which business journeys must work, what data is genuinely needed, and which sensitive values must never be present in readable form. This is the practical expression of data minimization and privacy by design; the U.K. ICO explicitly includes protection of personal information in development environments in its product-design guidance. ICO privacy-design guidance

A seven-step Data Mask & Seed migration plan

Find every dependency on the legacy package

Inventory production orgs and sandboxes that use the managed package. Identify users with the legacy Data Mask permission set license, current configurations and schedules, custom libraries, runbooks, notifications, sandbox refresh automation, and downstream processes that assume masking has finished. Include vendors and offshore or temporary teams whose access depends on masked data.

Capture evidence before the interface becomes read-only: configuration screenshots or exports, object and field lists, filter criteria, last successful jobs, failures, runtimes, record counts, and control owners. This is migration input, not proof that the old configuration remains adequate.

Build a current sensitive-data map

Review standard and custom fields, managed-package data, long text, comments, email messages, files, IDs, dates, locations, and relationship keys. Separate direct identifiers from quasi-identifiers that can become identifying in combination. Also identify values that can trigger real-world actions, such as routable email addresses, phone numbers, payment references, portal identities, or integration endpoints.

Use Salesforce field sensitivity and compliance categories as discovery aids, but validate them with the relevant data owners. Missing or stale classification metadata can produce a false sense of completeness.

Design policies around outcomes

Choose the transformation that fits each field and use case: deletion, randomization, pattern-preserving replacement, pseudonymous consistency, or a controlled custom library. Preserve formats where validations and integrations require them. Preserve consistent values across related objects where end-to-end testing relies on joins. Delete data when it is unnecessary rather than manufacturing realism without a business need.

Assign the `Data Mask And Seed User` permission set license before the corresponding permission set, then create new policies in the native application. Existing custom libraries do not need to be recreated, but review their content, ownership, and suitability before reusing them. Salesforce transition instructions

Design explicitly for volume and automation limits

Salesforce documents several limits that belong in the plan. Above 20 million records, a masking job no longer preserves the existing distribution of values or applies consistent field masking across objects. A single job supports no more than 350 million records, and a sandbox can run no more than 12 masking jobs in a rolling 24-hour period. Data Mask & Seed requirements

Large organizations should measure candidate records before finalizing policy boundaries. If consistent replacement or realistic distributions matter, split and filter policies deliberately, then test whether the resulting sequence maintains the required relationships. Do not assume that a successful job status proves analytical or transactional fidelity.

Automation behavior also needs rehearsal. Data Mask & Seed does not bypass duplicate detection, and Salesforce lists deletion scenarios involving Feed Items, History, Case Comments, Email Messages, and Chatter Comments where automations are not bypassed. Decide which rules must be temporarily controlled, document the restoration step, and monitor for duplicate or partially processed records.

Pilot in a newly refreshed representative sandbox

Run the new policy in an environment that resembles the hardest production copy, not a tiny demonstration org. Record the initial count, duration, errors, and final status. Validate more than a handful of contacts: sample high-risk objects, edge cases, sparse records, international formats, large text fields, encrypted fields, relationship chains, and records created through managed packages.

Keep the legacy and new approaches in parallel only long enough to compare results. Salesforce allows both versions to coexist, but the old package is unsupported; parallel operation is a validation technique, not a long-term operating model.

Prove privacy and test utility separately

Use two acceptance tracks. The privacy track checks that prohibited readable values are absent, sensitive combinations have been addressed, routable contact details cannot reach real people, access is limited, and job evidence is retained. The test-utility track checks that record relationships, required formats, segmentation, reports, automations, integrations, service workflows, and negative test cases still behave as intended.

A dataset can pass one track and fail the other. Aggressive randomization may protect identities but destroy household, member-policy, patient-case, or donor-campaign relationships. Format-preserving values may keep tests running while leaving free-text notes or rare attribute combinations exposed. Both results need named owners and explicit sign-off.

Cut over the refresh runbook, then retire the package carefully

Update the end-to-end sandbox process: refresh request, access restriction, policy trigger, job monitoring, error response, validation, approval, and release to users. Add evidence that can be reviewed later, including policy version, source refresh time, job report, exceptions, and approval.

Only after the new control has succeeded across representative environments should the team remove obsolete schedules and consider uninstalling the first-generation managed package. Salesforce links to its managed-package uninstall instructions, but dependencies should be assessed before removal. Preserve records required for audit or operational history under your organization's retention process.

Industry-specific review points

Healthcare: Do not limit the inventory to names and medical record numbers. Review dates, geographic details, case narratives, clinical notes, attachments, portal identities, and combinations that may identify a patient or member. Have qualified privacy and security reviewers determine whether the result meets the organization's intended HIPAA treatment; a successful masking job is not itself a Safe Harbor or Expert Determination.

Insurance: Test identity consistency across person, policy, claim, case, provider, payment, and document records. Pattern-preserving values may be essential for adjudication and integration tests, but plausible test data must not remain routable to a real claimant, broker, member, or bank endpoint.

Nonprofit foundations: Review donor identities, beneficiary information, grant applications, safeguarding notes, payment data, and restricted-program attributes. Preserve the segmentation and household relationships required for fundraising and program testing without exposing real people to a broader non-production audience.

A Practical Migration Timeline Leading to December 31

  • By November 6: Complete org, user, configuration, schedule, data, and access discovery.
  • By November 20: Approve the sensitive-data map, environment purposes, policy design, and acceptance criteria.
  • By December 4: Build policies and complete a representative pilot, including volume and automation tests.
  • By December 18: Reconcile privacy and business results, remediate gaps, and run the new refresh process end to end.
  • Before December 31: Put the new process into operation, preserve evidence, train owners, and remove reliance on the legacy interface.

The decision for Salesforce leaders

Ask one question: after the next sandbox refresh, can the organization prove both that prohibited sensitive data is protected and that critical workflows still have trustworthy test data?

If the answer depends on an unsupported managed package, an undocumented schedule, or a visual spot check, the migration is not finished. Use the remaining window to create a repeatable control with clear ownership, measurable acceptance criteria, and evidence from a real refresh cycle.

Yuniq provides Salesforce migration, implementation, validation, testing, and managed support. For organizations that need to inventory legacy Data Mask dependencies, rebuild policies, test business-critical workflows, and operationalize the new refresh runbook before December 31, explore Yuniq's Salesforce migration and validation services .