SOAR & Playbook Orchestration Agents
Problem Statement
When a cloud instance (AWS EC2 / Azure VM) exhibits signs of compromise, incident responders must preserve forensic evidence immediately by taking EBS snapshots, dumping RAM, and detaching the server from network routes before the attacker wipes logs or terminates the instance.
Goal
Build an automated SOAR agent that triggers cloud disk snapshots, captures volatile memory metadata, and isolates cloud instances upon detection of high-confidence alerts.
Outcome Measurement
- Evidence Preservation: Forensic disk snapshot initiated within 2 seconds of alert.
- Chain of Custody: 100% compliance with automated forensic metadata hashing and timestamping.
How the Code was Built
Constructs AWS SDK / Boto3 parameter blocks dynamically for snapshot execution.
Executable Code
from pydantic import BaseModel, Fieldfrom langchain_openai import ChatOpenAIclass CloudForensicsExecution(BaseModel): instance_id: str region: str create_snapshot_command: str isolate_security_group_command: str evidence_tagging: dictdef plan_forensic_acquisition(instance_id: str, cloud_region: str, alert_reason: str) -> CloudForensicsExecution: llm = ChatOpenAI(model="gpt-4o", temperature=0) structured_llm = llm.with_structured_output(CloudForensicsExecution) prompt = f"Generate AWS Boto3 automation parameters to isolate cloud instance '{instance_id}' in '{cloud_region}' due to alert: {alert_reason}" return structured_llm.invoke(prompt)if __name__ == "__main__": res = plan_forensic_acquisition("i-0abc1234def56789a", "us-east-1", "Unauthorized Reverse Shell Detected") print(f"Instance: {res.instance_id}") print(f"Snapshot Action: {res.create_snapshot_command}") print(f"Isolation SG: {res.isolate_security_group_command}")
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