Quick-reference card
| Field | Value |
|---|---|
| Control ID | PM-25 |
| Control Name | Minimization of Personally Identifiable Information Used in Testing, Training, and Research |
| Framework | NIST SP 800-53 Revision 5 |
| Control Family | Program Management |
| Baselines | PRIVACY |
| Relevance | Organization (First Party) |
| Risk Severity | Medium |
What this control requires
PM-25 requires your organization to limit and authorize every use of personally identifiable information (PII) in internal testing, training, and research environments. That means you need documented policies governing when real PII can appear in non-production contexts, formal authorization processes for each use case, and a defined cadence for reviewing those policies.
In practice, this control targets a gap most organizations ignore until an auditor flags it. Development and QA teams routinely copy production databases into staging environments to reproduce bugs or train staff on realistic data. Without a policy that limits PII exposure in those contexts, sensitive records spread across systems that lack production-grade access controls, encryption, and monitoring. PM-25 closes that gap by requiring you to document acceptable use, minimize the volume of PII in each scenario, and establish an authorization workflow before any real data enters a non-production environment.
The review requirement adds a recurring check. Your organization must define a frequency for revisiting these policies and procedures, ensuring they stay aligned with evolving privacy regulations and organizational changes. This ongoing review cycle prevents policies from becoming stale artifacts that no longer reflect how teams actually handle data.
Why it matters
Most organizations treat non-production environments as low-risk, but those environments often contain the same sensitive data as production with significantly fewer protections. PM-25 exists because PII that leaks from a test database carries the same regulatory consequences as PII stolen from a production system.
Failure to maintain this control introduces audit risk and may result in certification withdrawal or regulatory findings. Privacy auditors look specifically for evidence that non-production data handling has been formally addressed. A missing or outdated policy signals a broader governance weakness that can trigger expanded audit scope across your entire NIST SP 800-53 program.
Where this control breaks down, the consequences compound. Unauthorized PII in training materials creates an untracked data inventory that falls outside your breach notification processes. If a test environment is compromised, you may not even know which individuals were affected, making incident response significantly harder.
Specifically, the authorization gap creates a documentation problem that amplifies during incidents. When your organization can’t produce records showing which PII was authorized for which non-production use, privacy regulators treat the entire non-production data estate as potentially exposed. That gap turns a contained test-environment incident into an enterprise-wide notification obligation.
What attackers exploit
- Non-production databases with full production PII and weaker access controls, including default credentials or shared service accounts
- Test environments excluded from monitoring and logging, allowing undetected exfiltration
- Training datasets stored in shared drives or cloud buckets without access restrictions
- Stale copies of production data in decommissioned staging environments that remain accessible
- Developer workstations containing local database snapshots with unmasked PII
How to implement
For your organization
The most common failure mode is the absence of any formal policy. Teams copy production data into test environments ad hoc, and no one tracks which datasets contain PII or who authorized their use. Start by addressing that gap before building out tooling.
Step 1: Develop and document your PII minimization policy. Draft a policy that defines when real PII is permitted in testing, training, and research. Specify acceptable alternatives, including synthetic data generation, data masking, tokenization, and anonymization techniques. Your policy should reference the NIST 800-53 compliance checklist requirements for the Program Management family and align with your broader privacy program plan. Include specific criteria for when placeholder data is sufficient and when real PII is genuinely required, such as validating production data migration scripts that must handle actual formatting edge cases.
Step 2: Inventory current PII usage in non-production environments. Conduct a privacy threshold analysis across all test, training, and research environments. Identify every dataset that contains real PII, document its source system, and record who has access. This inventory becomes your baseline for minimization efforts and feeds directly into your privacy risk assessment.
Step 3: Establish an authorization workflow. Create a formal process for requesting and approving PII use in non-production contexts. Each request should document the specific purpose, the minimum data elements needed, the retention period, and the protective measures applied. Route approvals through your senior agency official for privacy or equivalent role. Without this step, you can’t demonstrate the “authorize” requirement in PM-25.
Step 4: Implement technical controls for data minimization. Deploy data masking or synthetic data generation tools to create realistic but non-sensitive datasets for testing and training. Placeholder data should preserve the format, distribution, and relationships of production data without exposing actual PII. Common tooling categories include database subsetting tools, synthetic data platforms, and format-preserving encryption solutions.
Step 5: Define and execute a review cadence. Set a documented frequency for reviewing and updating your PII minimization policies and procedures. Annual reviews are common, but your organization should align the cadence with the pace of regulatory change and environment turnover. Each review cycle should verify that authorized uses are still necessary and that minimization techniques remain effective.
Step 6: Validate compliance through periodic spot checks. Policy documentation alone doesn’t satisfy PM-25. Conduct periodic audits of non-production environments to confirm that teams are actually following the approved procedures. Compare the PII present in test and training datasets against your authorization records to identify any unauthorized copies. These spot checks feed back into your privacy risk assessment and provide the implementation evidence auditors expect to see.
Common mistakes to avoid:
- Treating data masking as a one-time project rather than an ongoing operational process
- Failing to include machine learning training datasets in your PII inventory
- Assuming anonymized data is always re-identification-proof without conducting a formal risk assessment
- Documenting policies but never validating that teams follow them in practice
- Allowing indefinite retention of PII in test environments after the original testing purpose has concluded
- Overlooking contractor and third-party developer access to non-production environments containing real PII
Evidence examples
| Evidence Type | Example Artifact |
|---|---|
| Privacy program plan | Privacy program plan referencing PII minimization requirements for non-production environments |
| PII minimization policy | Policy defining acceptable PII use in testing, training, and research, including authorization workflows and review cadence |
| Privacy threshold analysis | Completed threshold analysis identifying datasets in test and staging environments that contain PII |
| Privacy risk assessment | Risk assessment evaluating re-identification risk for anonymized or masked datasets in non-production use |
| Authorization records | Signed approval forms documenting authorized PII use, specifying purpose, data elements, and retention period |
| Data masking documentation | Configuration records for data masking or synthetic data generation tools applied to testing and training datasets |
| Testing and training templates | Standardized templates specifying data requirements, approved data sources, and minimization methods for each testing or training activity |
| Policy review log | Dated records of periodic policy reviews, including reviewer names, findings, and any updates made to PII minimization procedures |
Cross-framework mapping
No cross-framework mappings are currently configured for PM-25.
Related controls
- PM-23 — Data Governance Body: Establishes the governance structure responsible for overseeing data handling practices, including the PII minimization policies that PM-25 requires.
- PT-03 — Personally Identifiable Information Processing Purposes: Defines the authorized purposes for PII processing, which directly informs the authorization decisions PM-25 requires for testing, training, and research use cases.
- SA-03 — System Development Life Cycle: Integrates security and privacy considerations into the development lifecycle, including the use of minimized or synthetic data during system testing phases.
- SA-08 — Security and Privacy Engineering Principles: Provides the engineering principles, such as data minimization by design, that support PM-25’s requirement to limit PII in non-production contexts.
- SI-12 — Information Management and Retention: Governs how long data is retained and when it must be disposed of, which applies directly to PII copies created for testing, training, and research purposes.
Frequently asked questions
What is NIST SP 800-53 PM-25?
PM-25 is the NIST SP 800-53 control that requires organizations to develop PII minimization policies for internal testing, training, and research, authorize each use of real PII in those environments, and review those policies at a defined frequency. The control addresses the risk that non-production environments accumulate sensitive data without the protective measures applied to production systems. Organizations satisfy PM-25 by conducting a privacy threshold analysis, implementing data masking or synthetic data alternatives, and maintaining authorization records for every approved use.
What happens if PM-25 is not implemented?
Without PM-25, your organization lacks documented governance over PII in non-production environments, which creates direct audit findings during privacy assessments. Auditors treat the absence of a PII minimization policy as evidence of a broader privacy program gap, potentially triggering expanded review of related Program Management controls. The risk extends beyond compliance. Untracked PII in testing and training datasets sits outside your privacy risk assessment scope, meaning a breach in those environments may go undetected or trigger notification obligations you haven’t planned for.
How do you audit PM-25?
Auditing PM-25 starts with requesting the organization’s PII minimization policy and verifying it addresses all four assessment objectives: documented policies and procedures, minimization of PII amounts, formal authorization for required use, and a defined review cadence. From there, examine authorization records to confirm each approved use specifies the purpose, data elements, and retention period. Review the most recent privacy threshold analysis to verify non-production environments have been inventoried for PII. Finally, check the policy review log against the organization’s defined frequency to confirm updates aren’t overdue.
What is synthetic test data and how does it replace PII in non-production environments?
Synthetic test data is artificially generated information that preserves the statistical properties, format, and relationships of production data without containing any real PII. Organizations use synthetic data generation platforms to create realistic datasets for testing, training, and research that satisfy PM-25’s minimization requirement. Effective synthetic data maintains referential integrity across tables, reproduces edge cases developers need for validation, and eliminates re-identification risk because no record corresponds to an actual individual. When combined with a privacy risk assessment confirming the synthetic output can’t be reverse-engineered, this approach removes the need for PII authorization workflows in most non-production scenarios.
UpGuard can help you manage your NIST SP 800-53 compliance posture. Explore the NIST SP 800-53 compliance framework or read more about NIST compliance best practices.