Detecting AWS CloudTrail Anomalies

Overview

AWS CloudTrail records API calls across AWS services. This guide covers querying CloudTrail events with boto3's lookup_events API, building statistical baselines of normal API activity, detecting anomalies such as unusual event sources, geographic anomalies, high-frequency API calls, and first-time API usage patterns that indicate compromised credentials or insider threats.

Prerequisites

• Python 3.9+ with boto3 library
• AWS credentials with CloudTrail read permissions (cloudtrail:LookupEvents)
• Understanding of AWS IAM and common API patterns
• CloudTrail enabled in target AWS account (management events at minimum)

Steps

Step 1: Query CloudTrail Events

Use boto3 CloudTrail client's lookup_events to retrieve recent API activity with pagination.

Step 2: Build Activity Baseline

Aggregate events by user, source IP, event source, and event name to establish normal behavior patterns.

Step 3: Detect Anomalies

Flag unusual patterns: new event sources per user, first-time API calls, geographic IP changes, high error rates, and sensitive API usage (IAM, KMS, S3 policy changes).

Step 4: Generate Detection Report

Produce a JSON report with anomaly scores, top suspicious users, and recommended investigation actions.

Expected Output

JSON report with event statistics, baseline deviations, anomalous users/IPs, sensitive API calls, and error rate analysis.

Verification Criteria

Confirm successful execution by validating:

• [ ] All prerequisite tools and access requirements are satisfied
• [ ] Each workflow step completed without errors
• [ ] Output matches expected format and contains expected data
• [ ] No security warnings or misconfigurations detected
• [ ] Results are documented and evidence is preserved for audit

Compliance Framework Mapping

This skill supports compliance evidence collection across multiple frameworks:

• SOC 2: CC6.1 (Logical Access), CC6.6 (System Boundaries), CC7.1 (Monitoring)
• ISO 27001: A.8.1 (Asset Management), A.13.1 (Network Security), A.14.1 (System Acquisition)
• NIST 800-53: AC-3 (Access Enforcement), SC-7 (Boundary Protection), CM-7 (Least Functionality)
• NIST CSF: PR.AC (Access Control), PR.DS (Data Security), DE.CM (Continuous Monitoring)

Claw GRC Tip: When this skill is executed by a registered agent, compliance evidence is automatically captured and mapped to the relevant controls in your active frameworks.

Deploying This Skill with Claw GRC

Agent Execution

Register this skill with your Claw GRC agent for automated execution:

# Install via CLI
npx claw-grc skills add detecting-aws-cloudtrail-anomalies

# Or load dynamically via MCP
grc.load_skill("detecting-aws-cloudtrail-anomalies")

Audit Trail Integration

When executed through Claw GRC, every step of this skill generates tamper-evident audit records:

• SHA-256 chain hashing ensures no step can be modified after execution
• Evidence artifacts (configs, scan results, logs) are automatically attached to relevant controls
• Trust score impact — successful execution increases your agent's trust score

Continuous Compliance

Schedule this skill for recurring execution to maintain continuous compliance posture. Claw GRC monitors for drift and alerts when re-execution is needed.