Claude 24f816b6a3
Consolidate 22 sibling repos into layered organism structure
Place useful parts of the surrounding repos into sica-fondt by layer, per the
body model (Ada = membrane; brain/endocrine/capabilities/knowledge non-Ada):

- brain/        LLM reasoning + providers (dapr, hermes, MoMoA)
- capabilities/ REPRAG sidecars: hermes tools/skills, dapr tools, parallel
                dispatch, A51 channels, and the OSINT cluster
- knowledge/    LORAG corpus: 754 cyber-skills, agency personas, secure-coding,
                MITRE ATT&CK data
- reference/    defensive threat-reference (C3, shhbruh doc) + AdaYaml parser

License handling: AGPL sources (worldosint, advanced_evolution, mercury,
Reticulum) and GPL DeTTECT are SPEC-only clean-room/port descriptions — no
copyleft code copied. MIT/Apache/data parts copied as working trees.

Safety: shhbruh escape/persistence material and C3 covert-C2 kept as reference
only, not wired into the running organism. See CONSOLIDATION.md.

https://claude.ai/code/session_01UehUqEXXJJCsHoA4voCU5c
2026-06-10 06:53:01 +00:00

3.0 KiB

name description domain subdomain tags version author license nist_csf
performing-cloud-forensics-with-aws-cloudtrail Perform forensic investigation of AWS environments using CloudTrail logs to reconstruct attacker activity, identify compromised credentials, and analyze API call patterns. cybersecurity cloud-security
cloud-security
aws
cloudtrail
forensics
incident-response
dfir
boto3
s3
1.0 mahipal Apache-2.0
PR.IR-01
ID.AM-08
GV.SC-06
DE.CM-01

Performing Cloud Forensics with AWS CloudTrail

When to Use

  • When investigating suspected AWS account compromise
  • After detecting unauthorized API calls or credential exposure
  • During incident response involving cloud infrastructure
  • When analyzing S3 data exfiltration or IAM privilege escalation
  • For post-incident forensic timeline reconstruction

Prerequisites

  • AWS account with CloudTrail enabled (management and data events)
  • IAM permissions for cloudtrail:LookupEvents, s3:GetObject, athena:StartQueryExecution
  • boto3 Python SDK installed
  • CloudTrail logs delivered to S3 with optional Athena table configured
  • AWS CLI configured with appropriate credentials

Workflow

  1. Scope Investigation: Identify timeframe, affected accounts, and compromised credentials.
  2. Query CloudTrail: Use boto3 lookup_events or Athena to retrieve relevant API events.
  3. Filter by Indicators: Search for suspicious user agents, source IPs, and event names.
  4. Reconstruct Timeline: Build chronological sequence of attacker actions from API calls.
  5. Analyze Access Patterns: Identify data access, IAM changes, and resource modifications.
  6. Identify Persistence: Check for new IAM users, access keys, roles, or Lambda functions.
  7. Generate Report: Produce forensic timeline with findings and remediation steps.

Key Concepts

Concept Description
LookupEvents CloudTrail API to query management events (last 90 days)
Athena Queries SQL queries against CloudTrail logs in S3 for historical analysis
User Agent Analysis Identify tool signatures (AWS CLI, SDK, console, custom)
AccessKeyId Track activity by specific IAM access key
EventName AWS API action name (e.g., GetObject, CreateUser, AssumeRole)
sourceIPAddress Origin IP of API call for geolocation analysis

Tools & Systems

Tool Purpose
boto3 CloudTrail client Programmatic CloudTrail event lookup
AWS Athena SQL-based analysis of CloudTrail S3 logs
AWS CLI Command-line CloudTrail queries
jq JSON processing for CloudTrail event parsing
CloudTrail Lake Advanced event data store with SQL query support

Output Format

Forensic Report: AWS-IR-[DATE]-[SEQ]
Account: [AWS Account ID]
Timeframe: [Start] to [End]
Compromised Credentials: [Access Key IDs]
Suspicious Events: [Count]
Source IPs: [List of attacker IPs]
Actions Taken: [API calls by attacker]
Data Accessed: [S3 objects, secrets, etc.]
Persistence Mechanisms: [New users, keys, roles]