HACKING PROJECT · forensics · Advanced
Cloud & Container Forensics Framework
Enable defensible, automated evidence acquisition and analysis for ephemeral cloud and containerized workloads.
Traditional forensics assumes persistent disks and physical access, but ephemeral cloud workloads — auto-scaling instances, short-lived containers, and serverless functions — vanish before responders can image them. Cloud-native logs are fragmented across CloudTrail, GuardDuty, VPC Flow, Kubernetes audit logs, and provider-specific stores. This creates a critical evidence gap where compromised containers are terminated and their volatile state is irretrievably lost.
**What you'll build**
Build a cloud-forensics framework that automates evidence preservation across AWS/Azure/GCP and Kubernetes: snapshot volumes on alert, capture container filesystem/memory before termination, pull and normalize control-plane and audit logs, and reconstruct an attack narrative. Implement isolation-and-quarantine playbooks (cordon nodes, revoke IAM, snapshot then preserve) aligned to NIST 800-207 zero-trust containment.
**Objective** — Enable defensible, automated evidence acquisition and analysis for ephemeral cloud and containerized workloads.
**Expected outcome** — A framework/toolkit that, on trigger, preserves cloud evidence (volume snapshots, container captures, normalized logs) into a forensic bucket and produces a correlated cloud incident timeline.
**Difficulty** — Advanced — demands multi-cloud IAM/API fluency, container runtime internals, and forensic soundness under ephemerality constraints.
**Primary category** — DFIR; Secondary = Cloud Security, Incident Response
**Security domains** — Primary = Digital Forensics; Secondary = Cloud Security, Incident Response, DevSecOps, Blue Team
**Technical focus** — Cloud Forensics, Container Forensics, Kubernetes Security, Log Aggregation & Correlation, Evidence Preservation, IAM Containment, Serverless Forensics, Snapshot Automation
**Core technologies** — Languages: Python, Go, Bash; Frameworks/Libraries: boto3, Azure SDK, Google Cloud client libs, Kubernetes client-go/python; Security Tools: AWS CloudTrail/GuardDuty, Falco, kube-forensics, docker-explorer, Velociraptor; Cloud/Infra: AWS/Azure/GCP, EKS/AKS/GKE, Terraform; AI/Data: OpenSearch/Elastic for log analytics; Databases: S3/object storage (WORM), PostgreSQL
**Where it's valued** — Cloud-native SaaS, fintech, e-commerce, MSSPs, any regulated org running Kubernetes at scale
**Careers it builds toward** — Cloud Forensics Analyst, Cloud Incident Responder, Cloud Security Engineer, DFIR Consultant, DevSecOps Engineer
**Framework alignment**
• MITRE ATT&CK: Cloud/Containers matrices — Deploy Container (T1610), Escape to Host (T1611), Unused/Unsupported Cloud Regions (T1535), Modify Cloud Compute Infrastructure (T1578), Impair Defenses: Disable Cloud Logs (T1562.008)
• NIST CSF 2.0: Identify (asset inventory), Protect (PR.DS), Detect (DE.CM), Respond (RS.MI containment, RS.AN)
• OWASP: API Security Top 10 (broken object/function-level authorization for cloud APIs), Kubernetes-adjacent misconfig risks
• CIS Controls v8: 8 (Audit Log Management), 3 (Data Protection), 6 (Access Control), 13 (Network Monitoring)
**Innovation** — Innovative Product — cloud/container forensics tooling is still immature and fragmented; a unified multi-cloud preservation framework has real market whitespace. Scores: Innovation 8/10, Startup 8/10, Enterprise-Readiness 6/10, Patent = Possible.