SI-19(5): Statistical Disclosure Control

SI-19(5): Statistical Disclosure Control requires you to modify analytical outputs (tables, aggregates, statistics) so no individual person or organization can be identified from the results. Operationalize it by defining release rules for statistical products, applying proven disclosure controls (suppression, aggregation, noise, top-coding), and retaining review evidence for each release. ¹

Key takeaways:

• Treat this as an “output release” control for analytics, dashboards, and research, not a database security control.
• Create a repeatable SDC runbook: classify outputs, apply approved transformations, and require a documented pre-release review.
• Keep an evidence bundle per release (inputs, method selected, approvals, final output, and exceptions) to pass audits and customer diligence.

SI-19(5) shows up when your system produces statistics from sensitive data, then shares those statistics outside the smallest necessary audience. That could be internal: an enterprise KPI dashboard used across business units. It could be external: a report to a regulator, a dataset to a research partner, or a public transparency report. The risk is disclosure through the analysis itself: small cells, unique combinations, or outliers can identify people or organizations even if direct identifiers were removed.

This requirement is practical: you must “manipulate numerical data, contingency tables, and statistical findings so that no individual or organization is identifiable in the results of the analysis.” ¹ That language is broad on purpose. Your job as a CCO/GRC lead is to turn it into concrete release rules and a defensible operating process: who approves statistical outputs, which techniques are allowed, what thresholds trigger suppression or aggregation, and what evidence proves the control ran.

If you already have a de-identification or privacy review process, SI-19(5) often becomes the analytics-specific extension: it addresses re-identification risk from summary outputs, not just from raw record sharing.

Regulatory text

Requirement (verbatim): “Manipulate numerical data, contingency tables, and statistical findings so that no individual or organization is identifiable in the results of the analysis.” ¹

Operator meaning (what you must do):

• Put guardrails on statistical outputs, not only on raw data.
• Apply statistical disclosure control (SDC) techniques before publishing or sharing results so recipients cannot infer an identity (person or organization) from a table, chart, model output, or narrative statistic.
• Make this repeatable: defined methods, trained reviewers, documented approvals, and retained evidence.

Plain-English interpretation (what counts as “identifiable”)

An output is “identifiable” if someone viewing the results can reasonably pinpoint a specific person or organization, or narrow to a tiny set, using:

• small counts in a table (e.g., a rare condition in a small location),
• unique combinations across dimensions (location + job role + time period),
• extreme values (highest revenue customer, largest claim),
• differencing attacks (comparing two similar reports to isolate one subject),
• linkage with outside knowledge (news, public records, known events).

This control applies even when you never disclose names. The failure mode is “the statistic becomes the identifier.”

Who it applies to

Entity types

• Federal information systems and programs using NIST SP 800-53 as the control baseline. ²
• Contractor systems handling federal data where 800-53 controls flow down via contract, ATO requirements, or security clauses. ²

Operational contexts where SI-19(5) matters most

• BI/analytics platforms producing enterprise dashboards from sensitive program data.
• Data science teams releasing model performance metrics by subgroup.
• Research collaborations where you share cross-tabs or extracts of aggregated findings.
• Incident, fraud, or compliance analytics distributed to broad audiences.
• Any “transparency” reporting (internal or external) derived from regulated or sensitive datasets.

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

1) Assign ownership and define the “statistical release” scope

• Name a control owner (often Privacy, Data Governance, or Security Engineering with analytics platform ownership).
• Define “statistical products” in scope: dashboards, ad hoc queries shared outside the originating team, scheduled reports, ML evaluation reports, contingency tables, and exports of aggregated datasets.
• Identify “release channels”: email distribution lists, shared drives, Slack/Teams, customer portals, public websites, regulator submissions.

Practical tip: Scope by distribution, not by tool. The same table is low-risk in a restricted workspace and high-risk when emailed broadly.

2) Create an SDC control card (your runbook)

Write a one-page, operator-ready control card that includes:

• Objective: prevent identification of individuals/organizations via statistical outputs.
• Trigger events: new dashboard/report, recurring publication, external sharing, high-risk ad hoc request, dataset refresh.
• Required reviewers/approvers: analytics owner + privacy/security reviewer; add legal if mandated by your governance.
• Allowed SDC techniques (your approved toolbox).
• Exception path: urgent releases, executive requests, regulator deadlines.
• Evidence to retain and where it lives.

This is the minimum artifact auditors want when they ask, “Show me how SI-19(5) operates.”

3) Standardize your approved SDC techniques

Pick techniques your teams can apply consistently, then document decision rules. Common options include:

• Aggregation/generalization: reduce granularity (time buckets, geography, categories).
• Cell suppression: suppress small cells; consider complementary suppression to prevent back-calculation.
• Top-/bottom-coding and winsorization: reduce outlier identifiability for extreme values.
• Rounding/binning: avoid exact values when precision creates uniqueness.
• Noise injection / perturbation: add controlled noise to counts or metrics.
• k-anonymity-style thresholds for tables: require minimum group sizes for any reported slice (define your minimums internally and document them as policy).

Your policy should say which techniques are permitted for which output types, and what requires escalation (for example, releasing any subgroup metrics for protected classes, or any reporting on a small program population).

4) Implement pre-release review gates in the workflow

Operationalize this as a release control:

• For scheduled dashboards/reports: add a review step to the publication pipeline (ticketing workflow, pull request review, or BI platform approval).
• For ad hoc requests: require a lightweight “SDC check” form before sharing outside the requestor’s team.
• For external sharing: require explicit approval and a stored evidence bundle (see below).

Make it hard to bypass. The control fails when review is optional and socialized as “only for sensitive things.”

5) Build “differencing” defenses for recurring reports

Recurring publications create a specific risk: someone compares versions to isolate changes for a single subject.

• Keep consistent suppression rules across periods.
• Avoid publishing overlapping cuts that allow subtraction (e.g., total by region and total by region excluding one segment).
• Track which dimensions are released together.

This is less about math theory and more about disciplined release management.

6) Train analytics producers and reviewers

Train the people who create and approve outputs:

• What “small cell” means in your policy.
• Common re-identification patterns (unique combos, outliers, differencing).
• How to apply each approved technique in your actual tools (BI settings, SQL templates, notebook functions).

Keep attendance and the training material as evidence.

7) Monitor control health and remediate

Put the control on a recurring health check:

• Sample recent releases and verify the SDC checklist and approvals exist.
• Track exceptions and confirm compensating controls.
• Capture issues as remediation items with owners and closure evidence.

This is where teams often fail audits: they can show a policy but not sustained operation.

Required evidence and artifacts to retain

Keep evidence at two levels: program-level and release-level.

Program-level evidence (baseline)

• SI-19(5) control card/runbook (owner, triggers, steps, exception rules).
• SDC standard / procedure: approved techniques and decision rules.
• Tooling configuration screenshots or settings notes (BI suppression settings, query templates, export controls).
• Training deck + attendance/completions.
• Periodic control health check results and remediation tracking.

Release-level evidence ¹

• Source dataset description and sensitivity classification.
• The pre-release SDC checklist (completed).
• The chosen technique(s) and rationale (brief, but explicit).
• Reviewer approval (name, date, scope of approval).
• Final released artifact (table/report/export) and distribution scope.
• Any exception approval with compensating measures.

Common exam/audit questions and hangups

Auditors and assessors typically probe four areas:

1. Scope clarity: “Which outputs are covered, and how do you know you didn’t miss ad hoc sharing?”

• Have a definition of “statistical product” and a workflow trigger tied to distribution.

2. Method consistency: “How do you decide suppression vs aggregation vs noise?”

• Provide your technique matrix and a couple of completed examples.

3. Evidence per release: “Show me the last X releases and proof of review.”

• Keep a release log with links to approval and final artifacts.

4. Exception discipline: “What happens when leadership asks for a one-off cut?”

• Show an exception template with compensating controls and a sign-off trail.

Frequent implementation mistakes (and how to avoid them)

Mistake 1: Treating SDC as the same as de-identifying raw data.
Fix: Write separate procedures for (a) raw data sharing and (b) statistical output release. SI-19(5) is about the latter. ¹

Mistake 2: Only suppressing small cells without addressing back-calculation.
Fix: Add complementary suppression or restructure tables so suppressed values cannot be derived from totals.

Mistake 3: Leaving out organization identifiability.
Fix: Apply SDC to company-level outputs too (for example, a single contractor’s performance metrics in a small cohort). The text explicitly includes “organization.” ¹

Mistake 4: No ownership, no cadence, no evidence trail.
Fix: Assign a named owner, define triggers, and keep a minimal evidence bundle for each release. This is a common gap in real audits. ¹

Mistake 5: Over-sharing “safe” dashboards internally.
Fix: Internal distribution can still create disclosure. Gate broad-access dashboards the same way you gate external reports.

Enforcement context and risk implications

No public enforcement cases were provided in the source materials for this requirement, so you should plan around assessment and contractual risk rather than citing specific penalties. ²

Practically, SI-19(5) failures show up as:

• privacy incidents from re-identification via published analytics,
• ATO/control assessment findings (control not implemented or not operating),
• customer diligence gaps for contractors handling federal data (can’t demonstrate output disclosure controls),
• reputational harm when public statistics reveal sensitive participation or performance.

A practical 30/60/90-day execution plan

First 30 days (stand up the control)

• Name the control owner and approvers; document responsibilities.
• Inventory statistical products and map distribution channels.
• Draft the SI-19(5) control card and release checklist.
• Identify the highest-risk recurring outputs (small populations, many dimensions, outlier exposure) and put them behind manual review immediately.

By 60 days (standardize methods and evidence)

• Publish the SDC procedure: approved techniques, decision rules, exception path.
• Implement workflow gating in your reporting lifecycle (ticketing, PR review, or BI approval).
• Define the minimum evidence bundle and retention location.
• Train analysts and reviewers; store training artifacts.

By 90 days (operate, test, and prove)

• Run a control health check on a sample of releases; document results and remediation.
• Tune the technique matrix based on real findings (common suppressions, frequent exceptions).
• Add automation where it reduces bypass risk (templates, BI guardrails, automated checks for disallowed small-group slices).
• Prepare an assessor-ready package: policy/procedure, runbook, release log, and a few complete evidence examples.

Where Daydream fits: Daydream can hold the SI-19(5) control card, route approvals, and enforce a consistent evidence bundle per reporting cycle so you can answer audits with links instead of screenshots and email threads.

Frequently Asked Questions

Does SI-19(5) apply if we never release raw data, only dashboards?

Yes. The requirement is explicitly about statistical findings and tables, which includes dashboards and summary reports. Your risk is re-identification through small cells, unique combinations, or outliers in the output. ¹

Do we need differential privacy to comply?

The requirement does not mandate a specific method. You need a documented, consistently applied approach that prevents identification from the results, with evidence of review and approval. ¹

What’s the minimum viable “release review” that will satisfy an assessor?

A checklist tied to each release, a named reviewer approval, and a retained final artifact with the applied SDC method noted. Pair that with a program-level runbook that defines triggers, techniques, and exceptions. ¹

How do we handle ad hoc executive requests for a very specific slice?

Treat it as an exception event: document the request, run the SDC checklist, apply stronger aggregation or suppression, and record explicit approval plus distribution limits. If you can’t make it non-identifying, do not release that slice.

Can we rely on access controls instead of manipulating outputs?

Access controls reduce exposure but do not satisfy the requirement by themselves because SI-19(5) calls for manipulating statistical results to prevent identifiability. Use access controls as a compensating measure, not the primary control. ¹

How do we prove “no one is identifiable” without overpromising?

Avoid absolute guarantees in your documentation. State that you apply defined SDC techniques and review steps designed to reduce identifiability risk, then show consistent operation through evidence bundles and health checks. ¹

Footnotes

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

2. NIST SP 800-53 Rev. 5