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.
---
name: detecting-aws-cloudtrail-anomalies
description: Query AWS CloudTrail events with boto3, build statistical baselines of normal API activity, and detect anomalies such as unusual event sources, geographic anomalies, high-frequency API calls, and first-time API usage patterns.
license: Apache-2.0
---
# 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.
