agentsclimarketplace

Detecting aws cloudtrail anomalies

Skill Mikaru0Mystic/sectinel/arsenal/Anthropic-Cybersecurity-Skills/skills/detecting-aws-cloudtrail-anomalies

Open-source security arsenal for AI coding agents: 784 cybersecurity skills, scanner integrations, and a security MCP for Claude Code, Cursor, opencode, Gemini CLI, Cline, and any agentskills.io agent. Mapped to OWASP, MITRE ATT&CK, NIST CSF, D3FEND, ATLAS.

Install
npx -y skills add Mikaru0Mystic/sectinel --skill detecting-aws-cloudtrail-anomalies

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 11 stars11 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

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.

The file declares its own license as Apache-2.0. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

2.2 KB, 354 tokens by cl100k_base, as published. Nobody here has run it

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.

What ships with it: 3 files

20.3 KB alongside SKILL.md, 1 of them executable

references/

scripts/

Keep looking

Skills are one crate of 327,069. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.