external/anthropic-cybersecurity-skills/skills/performing-cloud-native-threat-hunting-with-aws-detective/SKILL.md
Hunt for threats in AWS environments using Detective behavior graphs, entity investigation timelines, GuardDuty finding correlation, and automated entity profiling across IAM users, EC2 instances, and IP addresses.
npx skillsauth add seikaikyo/dash-skills performing-cloud-native-threat-hunting-with-aws-detectiveInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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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.
detective:*, guardduty:List*)AmazonDetectiveFullAccess or custom policy with detective:SearchGraph, detective:GetInvestigation, detective:ListIndicators| 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 |
aws detective list-graphs --output table
# 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
#!/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'])
# 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
# 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
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.
aws detective list-graphs returns non-empty listdevelopment
拋棄式 HTML mockup 比稿:產出 2 到 3 個設計立場不同的變體(密度 / 版式 / 強調軸,不是換色),各附取捨說明,最後給有立場的對比結論。適用:「畫個草圖」「比較 A 版 B 版」「先看方向再做」「給我看幾種做法」。要 production 元件或設計已定案時不適用。
tools
需求不明時的意圖萃取訪談:一次一題、每題附上自己的猜測、聽出「真正想要 vs 覺得應該要」,直到能預測使用者反應(約 95% 信心)才動工。適用:需求缺少對象 / 動機 / 成功標準 / 約束,或使用者點名「訪談我」「先確認一下」「我們確定嗎」。明確自足的指示、純資訊查詢、機械性操作不適用。
development
對非平凡決策啟動新鮮 context 對抗審查(找碴不背書),在修正還便宜的時候抓出錯誤方向。適用:高風險改動(production、資安敏感邏輯、不可逆操作)、不熟的程式碼、要宣稱「這樣是安全的 / 可行的」之前。機械性操作與一行修改不適用。
testing
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