| name | SI-19(5)_statistical-disclosure-control |
| description | Manipulate numerical data, contingency tables, and statistical findings so that no individual or organization is identifiable in the results of the an |
| category | input-validation |
| version | 5.2.0 |
| author | cyberstrike-official |
| tags | ["nist","sp800-53","rev5","si-19-5","si","enhancement"] |
| tech_stack | ["aws","azure","gcp","linux","windows"] |
| cwe_ids | ["CWE-20"] |
| chains_with | [] |
| prerequisites | ["SI-19"] |
| severity_boost | {} |
SI-19(5) Statistical Disclosure Control
Enhancement of: SI-19
High-Level Description
Family: System and Information Integrity (SI)
Framework: NIST SP 800-53 Rev 5
Many types of statistical analyses can result in the disclosure of information about individuals even if only summary information is provided. For example, if a school that publishes a monthly table with the number of minority students enrolled, reports that it has 10-19 such students in January, and subsequently reports that it has 20-29 such students in March, then it can be inferred that the student who enrolled in February was a minority.
What to Check
How to Test
Step 1: Review Documentation
Examine the System Security Plan (SSP) and related artifacts for SI-19(5) implementation details. Verify the organization has documented how this control is satisfied.
Step 2: Validate Implementation
# For cloud environments, use cloud-audit-mcp tools
# For on-premises, review system configurations directly
# Example: Check if account management policies exist
grep -r "account.management\|access.control" /etc/security/ 2>/dev/null
Step 3: Test Operating Effectiveness
Verify the control is actively functioning, not just documented. Check logs, configurations, and operational evidence.
Tools
| Tool | Purpose | Usage |
|---|
| cloud-audit-mcp | Check integrity monitoring | cloud_audit_monitoring |
| AWS CLI | Review GuardDuty/Inspector | aws guardduty list-detectors |
Remediation Guide
Control Statement
Manipulate numerical data, contingency tables, and statistical findings so that no individual or organization is identifiable in the results of the analysis.
Implementation Guidance
Many types of statistical analyses can result in the disclosure of information about individuals even if only summary information is provided. For example, if a school that publishes a monthly table with the number of minority students enrolled, reports that it has 10-19 such students in January, and subsequently reports that it has 20-29 such students in March, then it can be inferred that the student who enrolled in February was a minority.
