SOAR & Playbook Orchestration Agents
Problem Statement
When credentials leak on dark web forums or code repositories, manual revocation takes hours. Security teams must invalidate active SSO sessions, reset passwords, rotate API tokens, and notify impacted employees without causing unnecessary operational downtime.
Goal
Build an automated SOAR agent that receives credential leak telemetry and executes multi-platform token invalidation across Okta, AWS IAM, and GitHub.
Outcome Measurement
- Revocation Latency: Credential rotation completed within 15 seconds of leak detection.
- Account Hardening: 100% of affected accounts forced to undergo re-authentication with MFA.
How the Code was Built
Defines step-by-step orchestrations for token revocation payloads using structured output models.
Executable Code
from pydantic import BaseModel, Fieldfrom langchain_openai import ChatOpenAIclass RevocationPlan(BaseModel): user_email: str okta_action: str = Field(description="Clear sessions & expire password") aws_action: str = Field(description="Deactivate matching IAM access keys") notification_message: strdef plan_credential_remediation(leaked_user: str, leak_source: str) -> RevocationPlan: llm = ChatOpenAI(model="gpt-4o", temperature=0) structured_llm = llm.with_structured_output(RevocationPlan) prompt = f"Design an automated revocation and user notification workflow for compromised user '{leaked_user}' identified on '{leak_source}'." return structured_llm.invoke(prompt)if __name__ == "__main__": plan = plan_credential_remediation("developer@company.com", "Public GitHub Repository Commit") print(f"Target User: {plan.user_email}") print(f"Okta Remediation: {plan.okta_action}") print(f"AWS IAM Remediation: {plan.aws_action}") print(f"User Notification:\n{plan.notification_message}")
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