# SI-18(1)_automation-support

> Correct or delete personally identifiable information that is inaccurate or outdated, incorrectly determined regarding impact, or incorrectly de-ident

- Skill: `cyberstrikeus/si-18-1-automation-support` (Agent Skill)
- Install (CLI): `npx skillmds@latest add cyberstrikeus/si-18-1-automation-support`
- Raw SKILL.md: https://api.skillmd.com/api/skills/cyberstrikeus/si-18-1-automation-support/raw
- Safety review: PASS (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: cyberstrikeus (https://skillmd.com/u/cyberstrikeus)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/cyberstrikeus/si-18-1-automation-support

---


# SI-18(1) Automation Support

> **Enhancement of:** SI-18

## High-Level Description

**Family:** System and Information Integrity (SI)
**Framework:** NIST SP 800-53 Rev 5

The use of automated mechanisms to improve data quality may inadvertently create privacy risks. Automated tools may connect to external or otherwise unrelated systems, and the matching of records between these systems may create linkages with unintended consequences. Organizations assess and document these risks in their privacy impact assessments and make determinations that are in alignment with their privacy program plans.

As data is obtained and used across the information life cycle, it is important to confirm the accuracy and relevance of personally identifiable information. Automated mechanisms can augment existing data quality processes and procedures and enable an organization to better identify and manage personally identifiable information in large-scale systems. For example, automated tools can greatly improve efforts to consistently normalize data or identify malformed data. Automated tools can also be used to improve the auditing of data and detect errors that may incorrectly alter personally identifiable information or incorrectly associate such information with the wrong individual. Automated capabilities backstop processes and procedures at-scale and enable more fine-grained detection and correction of data quality errors.

## What to Check

- [ ] Verify SI-18(1) Automation Support is documented in SSP
- [ ] Confirm control is operating effectively
- [ ] Review evidence of continuous monitoring for SI-18(1)
- [ ] Verify enhancement builds upon base control SI-18

## How to Test

### Step 1: Review Documentation

Examine the System Security Plan (SSP) and related artifacts for SI-18(1) 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

Correct or delete personally identifiable information that is inaccurate or outdated, incorrectly determined regarding impact, or incorrectly de-identified using [organization-defined].

### Implementation Guidance

The use of automated mechanisms to improve data quality may inadvertently create privacy risks. Automated tools may connect to external or otherwise unrelated systems, and the matching of records between these systems may create linkages with unintended consequences. Organizations assess and document these risks in their privacy impact assessments and make determinations that are in alignment with their privacy program plans.

As data is obtained and used across the information life cycle, it is important to confirm the accuracy and relevance of personally identifiable information. Automated mechanisms can augment existing data quality processes and procedures and enable an organization to better identify and manage personally identifiable information in large-scale systems. For example, automated tools can greatly improve efforts to consistently normalize data or identify malformed data. Automated tools can also be used to improve the auditing of data and detect errors that may incorrectly alter personally identifiable information or incorrectly associate such information with the wrong individual. Automated capabilities backstop processes and procedures at-scale and enable more fine-grained detection and correction of data quality errors.

## Risk Assessment

| Finding                                     | Severity | Impact                                      |
| ------------------------------------------- | -------- | ------------------------------------------- |
| SI-18(1) Automation Support not implemented | High     | System and Information Integrity            |
| SI-18(1) partially implemented              | Medium   | Incomplete System and Information Integrity |

## CWE Categories

| CWE ID | Title                     |
| ------ | ------------------------- |
| CWE-20 | Improper Input Validation |

## References

- [NIST SP 800-53 Rev 5 - SI-18(1)](https://csrc.nist.gov/projects/cprt/catalog#/cprt/framework/version/SP_800_53_5_1_1/home?element=si-18.1)
- [NIST SP 800-53A Rev 5 (Assessment Procedures)](https://csrc.nist.gov/pubs/sp/800/53/a/r5/final)
- [NIST SP 800-53 Rev 5 Full Catalog](https://csrc.nist.gov/pubs/sp/800/53/r5/upd1/final)

## Checklist

- [ ] Control documented in SSP
- [ ] Implementation evidence collected
- [ ] Operating effectiveness validated
- [ ] Continuous monitoring in place
- [ ] Related controls (PM-18, RA-8) reviewed