Anthropic-Cybersecurity-Skills/skills/performing-cloud-native-threat-hunting-with-aws-detective/SKILL.md

name, description, domain, subdomain, tags, version, author, license, nist_csf, mitre_attack

name	description	domain	subdomain	tags	version	author	license	nist_csf	mitre_attack
performing-cloud-native-threat-hunting-with-aws-detective	Investigate AWS security incidents using Amazon Detective's behavior graphs, built from CloudTrail, VPC Flow Logs, GuardDuty, and EKS audit logs, to trace entity timelines and profile IAM users, roles, EC2 instances, and IP addresses for lateral movement. Use when triaging GuardDuty findings, investigating a suspected AWS compromise, or reconstructing an attacker's activity timeline across AWS accounts.	cybersecurity	cloud-security	aws-detective threat-hunting cloud-security guardduty behavior-graph aws iam ec2 incident-investigation	1.0	juliosuas	Apache-2.0	PR.IR-01 ID.AM-08 GV.SC-06 DE.CM-01	T1078.004 T1530 T1537 T1580 T1071

Performing Cloud-Native Threat Hunting with AWS Detective

Overview

AWS Detective automatically collects and analyzes log data from AWS CloudTrail, VPC Flow Logs, GuardDuty findings, and EKS audit logs to build interactive behavior graphs. These graphs enable security analysts to investigate entities (IAM users, roles, IP addresses, EC2 instances) across time, identify anomalous API calls, detect lateral movement between accounts, and correlate GuardDuty findings into coherent attack narratives — all without manual log parsing.

Prerequisites

• AWS account with Detective enabled (requires GuardDuty active for 48+ hours)
• AWS CLI v2 configured with appropriate IAM permissions (detective:*, guardduty:List*)
• Python 3.9+ with boto3
• IAM policy: AmazonDetectiveFullAccess or custom policy with detective:SearchGraph, detective:GetInvestigation, detective:ListIndicators

Key Concepts

Concept	Description
Behavior Graph	Data structure linking CloudTrail, VPC Flow, GuardDuty, and EKS logs for an account/region
Entity	Investigable object: IAM user, IAM role, EC2 instance, IP address, S3 bucket, EKS cluster
Finding Group	Correlated set of GuardDuty findings linked to the same attack campaign
Entity Profile	Timeline of API calls, network connections, and resource access for a specific entity
Scope Time	Investigation window (default 24h, max 1 year) for behavioral analysis

Steps

Step 1: List Available Behavior Graphs

aws detective list-graphs --output table

Step 2: Investigate a Suspicious IAM User

# Get entity profile for an IAM user
aws detective get-investigation \
  --graph-arn arn:aws:detective:us-east-1:123456789012:graph:a1b2c3d4 \
  --investigation-id 000000000000000000001

Step 3: Search Entities Programmatically

#!/usr/bin/env python3
"""Search AWS Detective for suspicious entities."""
import boto3
import json
from datetime import datetime, timedelta

detective = boto3.client('detective')

def list_behavior_graphs():
    """List all Detective behavior graphs."""
    response = detective.list_graphs()
    return response.get('GraphList', [])

def get_investigation_indicators(graph_arn, investigation_id, max_results=50):
    """Get indicators for a specific investigation."""
    response = detective.list_indicators(
        GraphArn=graph_arn,
        InvestigationId=investigation_id,
        MaxResults=max_results
    )
    return response.get('Indicators', [])

def investigate_guardduty_findings(graph_arn):
    """List high-severity investigations correlated by Detective."""
    response = detective.list_investigations(
        GraphArn=graph_arn,
        FilterCriteria={
            'Severity': {'Value': 'CRITICAL'},
            'Status': {'Value': 'RUNNING'}
        },
        MaxResults=20
    )

    for investigation in response.get('InvestigationDetails', []):
        print(f"Investigation: {investigation['InvestigationId']}")
        print(f"  Entity: {investigation['EntityArn']}")
        print(f"  Status: {investigation['Status']}")
        print(f"  Severity: {investigation['Severity']}")
        print(f"  Created: {investigation['CreatedTime']}")
        print()

if __name__ == "__main__":
    graphs = list_behavior_graphs()
    for graph in graphs:
        print(f"Graph: {graph['Arn']}")
        investigate_guardduty_findings(graph['Arn'])

Step 4: Analyze Finding Groups for Attack Campaigns

# List investigations with high severity
aws detective list-investigations \
  --graph-arn arn:aws:detective:us-east-1:123456789012:graph:a1b2c3d4 \
  --filter-criteria '{"Severity":{"Value":"HIGH"}}' \
  --max-results 10

Step 5: Check Entity Indicators

# Get indicators for a specific investigation
aws detective list-indicators \
  --graph-arn arn:aws:detective:us-east-1:123456789012:graph:a1b2c3d4 \
  --investigation-id 000000000000000000001 \
  --max-results 50

Expected Output

The list-investigations command returns investigation metadata:

{
  "InvestigationDetails": [
    {
      "InvestigationId": "000000000000000000001",
      "Severity": "CRITICAL",
      "Status": "RUNNING",
      "State": "ACTIVE",
      "EntityArn": "arn:aws:iam::123456789012:user/suspicious-user",
      "EntityType": "IAM_USER",
      "CreatedTime": "2026-03-15T14:30:00Z"
    }
  ]
}

Indicators are retrieved separately via list-indicators and include types such as TTP_OBSERVED, IMPOSSIBLE_TRAVEL, FLAGGED_IP_ADDRESS, NEW_GEOLOCATION, NEW_ASO, NEW_USER_AGENT, RELATED_FINDING, and RELATED_FINDING_GROUP.

Verification

1. Confirm behavior graph has data: aws detective list-graphs returns non-empty list
2. Validate investigation results contain entity timelines with API call sequences
3. Cross-reference Detective findings with raw CloudTrail logs for accuracy
4. Verify finding group correlations match manual investigation conclusions
5. Confirm automated alerts trigger for HIGH/CRITICAL severity investigations