adriannoes/awesome-agentic-ai/detecting-aws-cloudtrail-anomalies

Detecting AWS CloudTrail Anomalies

Overview

AWS CloudTrail records API calls across AWS services. This skill 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.

When to Use

• When investigating security incidents that require detecting aws cloudtrail anomalies
• When building detection rules or threat hunting queries for this domain
• When SOC analysts need structured procedures for this analysis type
• When validating security monitoring coverage for related attack techniques

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.