detecting-aws-cloudtrail-anomalies

Files (4)

• LICENSE
• SKILL.md
• api-reference.md · references/
• agent.py · scripts/

SKILL.md (first lines)

# 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.

From adriannoes/awesome-agentic-ai, Apache-2.0.

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