SI-19(7): Validated Algorithms and Software

To meet the si-19(7): validated algorithms and software requirement, you must perform de-identification only with algorithms that are validated, and with software that is itself validated to correctly implement those algorithms. Operationally, this means selecting approved de-identification methods, verifying tool validation, locking configurations, and keeping repeatable evidence that de-identification works as intended in your environment. ¹

Key takeaways:

• Your de-identification method and the tool implementing it both need validation, not just a “standard” approach on paper. ¹
• Auditors will look for proof of correct implementation: test results, configurations, version control, and change management records.
• Treat de-identification as a controlled technical process with release gates, not as an ad hoc data-engineering task.

SI-19(7) sits in NIST SP 800-53’s focus area for handling personally identifiable information (PII) and privacy-related processing. If your environment de-identifies data for analytics, testing, research, data sharing, or downstream processing, this enhancement expects more than “we remove names” or “we hash identifiers.” It expects disciplined selection of de-identification algorithms and disciplined assurance that the software you run implements those algorithms correctly and consistently. ¹

For a Compliance Officer, CCO, or GRC lead, the fastest route to operationalizing SI-19(7) is to turn it into a gated workflow: (1) classify the dataset and intended use, (2) pick an approved algorithmic technique aligned to the use case, (3) use a tool with documented validation, (4) validate your own deployment with repeatable tests, and (5) retain evidence that survives personnel changes and tool upgrades. ¹

This page gives requirement-level implementation guidance with concrete steps, artifacts to retain, and the audit questions that commonly stall teams.

Regulatory text

Requirement (SI-19(7)): “Perform de-identification using validated algorithms and software that is validated to implement the algorithms.” ¹

Operator translation (what you must do):

1. Use a de-identification algorithm with a validation basis (for example, a method that has been evaluated, tested, or formally verified as appropriate to your risk and context). ¹
2. Run it in software that is validated to correctly implement that algorithm, so you can show the tool does what it claims and does so consistently across versions and configurations. ¹
3. Prove it in your environment through documented tests and controlled configuration, then keep evidence. This is how you make “validated” real during an assessment.

Plain-English interpretation

SI-19(7) is an assurance requirement for de-identification. You are not compliant if you only:

• pick a popular method (masking, hashing, tokenization) without documenting why it is valid for the re-identification risk you face, or
• buy a tool and assume the vendor’s marketing equals validation, or
• run a validated method but change defaults, pipelines, or versions without re-testing.

A practical interpretation that works in audits:
Validated algorithm = you can point to a defined de-identification technique, document why it is appropriate for the data and use case, and show test outcomes that support its effectiveness.
Validated software implementation = you can show the specific tool/version/configuration you run has been tested to implement the technique correctly, and you control changes that could break that correctness. ¹

Who it applies to

Entity scope

• Federal information systems and contractor systems handling federal data implementing NIST SP 800-53 controls. ¹

Operational scope (where it shows up)

• Analytics environments that ingest production datasets containing PII.
• Dev/test pipelines seeded with production-like data.
• Data sharing with third parties (research partners, subcontractors, service providers).
• Privacy engineering workflows (synthetic data generation, tokenization services, de-id microservices).
• Cross-domain data replication (data lakes, warehouses, MDM exports).

If your organization never de-identifies data, document “not applicable” with a short rationale and keep it ready for assessors. If you do de-identify, SI-19(7) becomes a build-and-operate control with ongoing evidence.

What you actually need to do (step-by-step)

1) Build an inventory of de-identification use cases

Create a register of:

• systems/pipelines performing de-identification,
• data elements involved (direct identifiers, quasi-identifiers, linkable fields),
• purpose (testing, analytics, external sharing),
• recipient context (internal teams vs third party).

Output: “De-identification processing inventory” linked to system boundaries.

2) Define approved algorithms/techniques and decision criteria

Write a short internal standard that answers:

• which techniques are approved (by use case),
• what “validated” means in your program,
• what tests must pass before use.

Examples of technique categories you can standardize:

• tokenization with controlled mapping tables,
• generalization/suppression rules for quasi-identifiers,
• k-anonymity style transformations (if you use them),
• format-preserving masking for low-risk dev/test datasets,
• synthetic data generation (if used, define acceptance tests and leakage checks).

Keep it concrete: “If dataset is used outside the security boundary, require technique X and test Y.”

3) Select software with implementer validation evidence

For each de-identification tool (commercial, open-source, in-house):

• capture tool name, version, deployment model,
• obtain vendor documentation describing algorithm implementation and any validation/testing claims,
• for in-house code, define your own “validation packet” (below).

Procurement/TPRM hook: if a third party provides the de-identification service (SaaS tokenization, managed ETL), treat their validation evidence as required due diligence inputs.

4) Validate your specific deployment (the step most teams miss)

Even if the algorithm and tool are “validated,” your configuration can break it. Validate the actual running system:

• Configuration lock: record rulesets, keys, salts, token vault settings, suppression thresholds, deterministic vs randomized modes.
• Test harness: maintain a small controlled test dataset with known edge cases (nulls, unicode, long strings, rare categories).
• Correctness tests: confirm direct identifiers are transformed as designed, reversibility is controlled (if applicable), and outputs meet schema/format requirements.
• Resistance checks (risk-based): attempt re-identification pathways relevant to your environment (linkage to auxiliary datasets you hold, join keys that remain, small cell sizes). Keep it scoped and practical.

Document test cases, expected outputs, actual outputs, and sign-off.

5) Put de-identification behind change control

Treat de-identification like crypto: small changes matter. Minimum controls:

• version pinning for tools/libraries,
• peer review for ruleset changes,
• approval gates for changing keys/salts/tokenization parameters,
• re-validation triggers when upgrading versions or expanding data scope.

6) Operational monitoring and periodic re-validation

Define ongoing checks:

• pipeline health checks (did de-identification run, did it fail open/closed),
• sampling checks (verify fields transformed),
• drift checks (new columns appearing, schema changes),
• periodic re-validation after major changes.

7) Map ownership and evidence production

Assign:

• Control owner: typically Privacy Engineering, Data Platform Security, or Security Engineering.
• Accountable executive: CISO/Chief Privacy Officer depending on governance model.
• Evidence producer: the team running pipelines and CI/CD.

Daydream can help here as a practical control-ops layer: map SI-19(7) to a named owner, implementation procedure, and recurring evidence artifacts so audits do not become a scavenger hunt. ¹

Required evidence and artifacts to retain

Keep artifacts in an audit-ready folder tied to the system boundary:

Design & governance

• De-identification standard (approved techniques + when to use them)
• De-identification processing inventory (pipelines, datasets, purposes)
• Data flow diagram showing where de-id occurs

Validation packet ¹

• Algorithm description and rationale for selection
• Tool validation documentation (vendor docs or internal validation report)
• Configuration baseline (rulesets, parameters, key management references)
• Test plan, test cases, and test results (with timestamps)
• Sign-off record (ticket/approval) for initial go-live and major changes

Operational

• Change tickets for version upgrades and ruleset changes
• Monitoring logs or job run history showing de-id execution
• Exception handling records (failures, bypass approvals if any)

Assessors generally accept screenshots, exports, and signed change records if they are consistent, dated, and traceable to the exact pipeline/tool version.

Common exam/audit questions and hangups

Expect these questions:

1. “Which de-identification algorithms do you use, and how did you validate them?” ¹
2. “Show me the software/tool validation evidence for the exact version you run.” ¹
3. “How do you know configuration changes didn’t weaken de-identification?”
4. “Where is de-identification performed in the pipeline, and can the pipeline fail open?”
5. “Who approves changes to tokenization keys, salts, or rulesets?”
6. “What happens when new data fields appear?”

Hangups that slow audits:

• teams have a policy but no test results,
• vendor provides a generic whitepaper without version specificity,
• de-identification happens downstream after data already replicated broadly,
• no re-validation after upgrades.

Frequent implementation mistakes (and how to avoid them)

Mistake	Why it fails SI-19(7)	How to avoid
“We hash IDs, so we’re de-identified.”	Hashing may remain linkable; validation is not shown.	Document the technique, threat model, and test outcomes; control join keys.
Treating vendor claims as validation	“Validated” needs evidence tied to your use.	Require versioned documentation and run your own validation tests.
No configuration baselines	A correct algorithm can be misconfigured.	Export rulesets/parameters and store them with test results.
Changes bypass re-testing	Upgrades can change transforms.	Define re-validation triggers in change management.
De-id happens too late	Copies of identifiable data proliferate.	Move de-identification earlier in the flow; restrict pre-de-id access.

Enforcement context and risk implications

No public enforcement cases were provided in the source material for SI-19(7). The practical risk still matters: weak or incorrectly implemented de-identification can turn “shared safely” datasets into privacy incidents, contract noncompliance, and breach reporting events depending on your environment and obligations. Your safest posture is to make validation repeatable and provable, because audits focus on what you can demonstrate. ¹

Practical 30/60/90-day execution plan

First 30 days (stabilize and scope)

• Assign a control owner and write a one-page SI-19(7) procedure tied to your systems. ¹
• Build the processing inventory of where de-identification occurs.
• Identify the top de-identification pipeline by risk (external sharing, broad internal access, high-sensitivity PII).
• Collect existing vendor/tool documentation and current configs.

Days 31–60 (validate and evidence)

• Define “approved techniques” and minimum validation tests per technique.
• Create a validation packet for the top pipeline: test plan, test dataset, results, sign-off.
• Implement configuration baselining and version pinning.
• Add change-management triggers for re-validation.

Days 61–90 (scale and operationalize)

• Roll validation packets across remaining pipelines/tools.
• Add monitoring checks and exception handling (fail closed where feasible).
• Train engineering and data teams on the procedure and evidence expectations.
• Centralize evidence collection and reminders in Daydream so each de-id pipeline produces recurring artifacts without manual chasing. ¹

Frequently Asked Questions

What counts as “validated” for the algorithm under SI-19(7)?

You need a documented basis that the technique is appropriate for the intended use and risk, plus test results that show it behaves as expected in your environment. The control text requires validated algorithms and validated software implementation. ¹

We use a third-party SaaS tokenization service. Are we covered?

Only if you can obtain evidence that the service’s software implements the claimed algorithms correctly and you validate your specific configuration and integration. Treat it as third-party due diligence plus internal validation testing. ¹

Do we have to re-validate after upgrades?

Yes in practice, because SI-19(7) is about validated implementation, and version/config changes can alter behavior. Define explicit re-validation triggers in your change process and keep the re-test artifacts. ¹

How do we handle open-source de-identification libraries with limited vendor paperwork?

Create an internal validation packet: pin versions, document the algorithm and how the library implements it, run a repeatable test suite, and require peer review for changes. Your evidence becomes the validation record. ¹

Can masking in a data warehouse meet SI-19(7)?

It can, if the masking method is defined as an algorithmic technique in your standard, implemented by validated software, and proven through tests and controlled configuration. Auditors will focus on whether the masking actually reduces re-identification risk in your use case. ¹

What evidence is most persuasive to an assessor?

Version-specific tool documentation, configuration baselines, and a clear test plan with pass/fail results tied to the pipeline that runs in production. Add change tickets that show you re-validated after material changes. ¹

Footnotes

1. NIST SP 800-53 Rev. 5 OSCAL JSON