RA-3(4)_predictive-cyber-analytics

RA-3(4) Predictive Cyber Analytics

Enhancement of: RA-3

High-Level Description

Family: Risk Assessment (RA) Framework: NIST SP 800-53 Rev 5

A properly resourced Security Operations Center (SOC) or Computer Incident Response Team (CIRT) may be overwhelmed by the volume of information generated by the proliferation of security tools and appliances unless it employs advanced automation and analytics to analyze the data. Advanced automation and analytics capabilities are typically supported by artificial intelligence concepts, including machine learning. Examples include Automated Threat Discovery and Response (which includes broad-based collection, context-based analysis, and adaptive response capabilities), automated workflow operations, and machine assisted decision tools. Note, however, that sophisticated adversaries may be able to extract information related to analytic parameters and retrain the machine learning to classify malicious activity as benign. Accordingly, machine learning is augmented by human monitoring to ensure that sophisticated adversaries are not able to conceal their activities.

What to Check

• Verify RA-3(4) Predictive Cyber Analytics is documented in SSP
• Confirm control is operating effectively
• Review evidence of continuous monitoring for RA-3(4)
• Verify enhancement builds upon base control RA-3

How to Test

Step 1: Review Documentation

Examine the System Security Plan (SSP) and related artifacts for RA-3(4) 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

Remediation Guide

Control Statement

Employ the following advanced automation and analytics capabilities to predict and identify risks to [organization-defined]: [organization-defined].

Implementation Guidance

A properly resourced Security Operations Center (SOC) or Computer Incident Response Team (CIRT) may be overwhelmed by the volume of information generated by the proliferation of security tools and appliances unless it employs advanced automation and analytics to analyze the data. Advanced automation and analytics capabilities are typically supported by artificial intelligence concepts, including machine learning. Examples include Automated Threat Discovery and Response (which includes broad-based collection, context-based analysis, and adaptive response capabilities), automated workflow operations, and machine assisted decision tools. Note, however, that sophisticated adversaries may be able to extract information related to analytic parameters and retrain the machine learning to classify malicious activity as benign. Accordingly, machine learning is augmented by human monitoring to ensure that sophisticated adversaries are not able to conceal their activities.

Risk Assessment

CWE Categories

References

• NIST SP 800-53 Rev 5 - RA-3(4)
• NIST SP 800-53A Rev 5 (Assessment Procedures)
• NIST SP 800-53 Rev 5 Full Catalog

Checklist

• Control documented in SSP
• Implementation evidence collected
• Operating effectiveness validated
• Continuous monitoring in place
• Related controls (none) reviewed