Asi detecting-aws-cloudtrail-anomalies

Detect unusual API call patterns in AWS CloudTrail logs using boto3, statistical baselining, and behavioral analysis to identify credential compromise, privilege escalation, and unauthorized resource access.

install

source · Clone the upstream repo

git clone https://github.com/plurigrid/asi

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/plurigrid/asi "$T" && mkdir -p ~/.claude/skills && cp -r "$T/plugins/asi/skills/detecting-aws-cloudtrail-anomalies" ~/.claude/skills/plurigrid-asi-detecting-aws-cloudtrail-anomalies && rm -rf "$T"

manifest: plugins/asi/skills/detecting-aws-cloudtrail-anomalies/SKILL.md

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.