Automation Workflow for Generating Phishing Simulations from Dark Web and Threat Actor Monitoring

This workflow eliminates the manual, days-long process of researching threats and authoring scenarios, directly converting attacker intelligence into measurable workforce resilience. It automates the ingestion of structured and unstructured feeds from platforms like Recorded Future or dark web scrapers, sanitizes malicious elements, and formats content for integration with simulation platforms like KnowBe4 or Cofense. The operational upside is a continuous, adaptive training loop that keeps pace with evolving TTPs, reducing the organizational risk window and improving security culture metrics like click-through and report rates.

Implementation requires a secure, containerized pipeline with specialized agents for scraping, NLP-based content parsing, and PII-safe synthesis. Critical controls include a mandatory human-in-the-loop approval gate before any campaign launch, immutable audit logging for compliance (e.g., NIST CSF), and ephemeral handling of source threat data to prevent re-exposure. The architecture integrates via APIs with email gateways, security awareness platforms, and SIEMs, creating a closed-loop system that feeds simulation results back into risk scoring and targeted micro-training generation.

This custom workflow directly addresses the operational bottleneck of manually authoring timely, credible phishing scenarios. It automates the collection of real-world attacker TTPs from dark web forums, leak sites, and threat actor communications. The business value is a continuous reduction in manual research and drafting effort—from days to minutes per campaign—while measurably improving workforce resilience by training employees against the exact lures currently in use by adversaries. Savings come from higher-quality training at lower operational cost.

Implementation requires a secure, air-gapped data pipeline where scraping agents operate in isolated containers. The sanitization pipeline uses signature matching and LLM classification to strip malicious code while preserving narrative authenticity. Orchestration via LangGraph manages state between agents, approval gates, and integration with simulation platforms like KnowBe4 or Proofpoint. Critical controls include immutable audit logs, PII-safe synthesis, and a mandatory human review loop for any content flagged by the sanitization agent to prevent accidental exposure.

Phase 1 establishes the secure data ingestion and sanitization pipeline, the highest-risk component. We deploy isolated scraping agents using Tor or commercial dark web APIs to collect phishing kits, lures, and actor communications. A core orchestration layer, built on LangGraph, routes this raw intelligence through a sanitization pipeline that strips malware, anonymizes PII, and tags TTPs. This phase delivers a validated library of clean, categorized threat artifacts, proving the core automation loop before any simulation deployment.

Phase 2 focuses on automated scenario assembly, using the sanitized library with a template engine to generate credible simulations. Phase 3 introduces the controlled deployment loop, integrating with platforms like KnowBe4 or Cofense via API. A mandatory approval gate and scheduler allow security teams to review and target specific cohorts. This phased approach de-risks the build, validates each component's output, and creates a continuous, automated pipeline that reduces manual intelligence-to-training lag from weeks to hours.

This automation workflow directly converts raw threat actor intelligence into sanctioned training, eliminating weeks of manual research. The operational upside is a 90% reduction in scenario creation time and training that is always current with real-world attacks. However, deploying unsanitized malicious content poses significant risk, necessitating a robust control architecture with approval gates, content sanitization agents, and immutable audit logging to every simulation platform action.

Implementation follows a phased rollout: start with non-personalized, low-fidelity email lures in a single department, monitored by a dedicated analyst. Phase two introduces spear-phishing personalization using synthetic personas, with all outbound communications requiring manager approval. The final phase activates autonomous, multi-channel campaigns, but only after confidence scoring thresholds are met and exception routing to a SOC playbook is validated. This controlled approach balances agility with necessary oversight.