Multi-Agent based Automation of Phishing Campaign Attribution and Deception

This workflow automates the insertion of canary tokens and honeypot credentials into simulated phishing pages, capturing users who attempt to interact with deceptive forms. It transforms a basic click-rate metric into actionable intelligence on which users pose the highest internal risk by attempting to submit data. The operational upside comes from precise risk-tiering for targeted remediation and measurable reduction in potential credential exposure, directly supporting SOC and identity governance teams.

Implementation requires a multi-agent system, often built on LangGraph or similar, where specialized agents manage the lifecycle of deception assets and securely log interactions to a SIEM like Splunk or Sentinel. Critical controls include ephemeral asset hosting, PII-safe correlation logic, and integration gates with IAM platforms (e.g., Okta, Azure AD) for risk profile updates. The architecture must include approval workflows for deception scope and exception handling for false positives to maintain trust in the training program.

This workflow automates the deployment of deceptive elements like canary tokens and honeypot credentials within phishing simulations. It moves beyond simple click-rate metrics to capture employees who attempt to interact further, such as entering data on a fake login page. This provides security teams with richer behavioral data, identifying higher-risk individuals and processes. The operational upside comes from converting simulation failures into actionable threat intelligence, improving the precision of security awareness programs and feeding hunting teams with verified internal risk signals.

Implementation requires a stateful orchestrator, like LangGraph, to manage the lifecycle of deception assets and correlate interaction data with user identities from HR systems. Agents handle deployment via APIs to simulation platforms and logging to the SIEM. Critical controls include strict governance on data usage, ephemeral asset lifetimes, and mandatory human review gates for high-risk attributions before any disciplinary action. This creates a closed-loop system that measures deeper susceptibility while preserving auditability and ethical boundaries.

Phase 1 establishes the core orchestration and data foundation. We deploy a LangGraph-based orchestrator agent integrated with your security awareness platform (e.g., KnowBe4, Proofpoint) via API. This agent ingests campaign schedules and target lists, then triggers specialized builder agents to generate deceptive assets like canary tokens and honeypot credential forms. All generated artifacts and their metadata are logged to a dedicated system of record, creating the initial audit trail and state management required for subsequent phases.

Phase 2 activates interaction tracking and correlation. When an employee interacts with a deception asset—such as entering data on a fake login page—a tracker agent captures the event, tying it to the unique artifact ID. A correlation engine then merges this interaction data with user identity from your IAM system (e.g., Okta, Azure AD) to calculate a behavioral risk score. Phase 3 operationalizes this intelligence, feeding high-risk attributions into a SOC dashboard and your SIEM (e.g., Splunk, Sentinel) via automated alerts, while routing edge cases for human analyst review, closing the loop from simulation to actionable threat intelligence.

This workflow automates the deployment of deceptive elements—like canary tokens and honeypot credentials—within phishing simulations to identify employees who not only click but attempt to interact further, such as entering data on a fake landing page. It transforms a basic click-rate metric into actionable behavioral intelligence, revealing which users are most susceptible to multi-stage attacks. This data directly feeds threat-hunting teams and refines security awareness training, creating measurable resilience gains by targeting high-risk behaviors before real attackers exploit them.

Implementation requires a multi-agent architecture, typically built on LangGraph or CrewAI, where specialized agents manage the lifecycle of deception assets, monitor interactions, and correlate data with user identities from the corporate directory. Strict governance controls are mandatory to ensure deception is contained within simulations and all data is ephemeral. Integration points include security awareness platforms (like KnowBe4), SIEMs (Splunk, Sentinel), and IAM systems, with approval gates for campaign targeting and comprehensive audit trails for compliance with frameworks like NIST.

This workflow automates the deployment of deceptive elements like canary tokens and honeypot credentials within phishing simulations. It moves beyond measuring click rates to identify employees who attempt to interact further, such as entering data on a fake landing page. This provides security teams with high-fidelity behavioral data, revealing which users are most susceptible to credential harvesting or multi-stage attacks. The operational upside is a more precise risk profile for targeted coaching and enriched threat intelligence for SOC analysts.

Implementation requires a multi-agent architecture where specialized agents manage the lifecycle of deception assets, correlate interaction data with user identities from Active Directory or Okta, and feed intelligence to Splunk or ServiceNow. Key controls include ephemeral hosting for fake pages, strict PII-safe data handling, and approval gates for simulation targeting. This creates a closed-loop system that reduces manual analysis of simulation results and provides actionable data to harden the human attack surface continuously.