AI Agentic Workflow for Autonomous Business Email Compromise (BEC) Detection

Executive impersonation bypasses spam filters, targeting finance teams with urgent, legitimate-seeming wire requests. A custom agentic workflow automates detection by analyzing email headers, language sentiment, and sender-recipient history against known BEC patterns. This reduces manual review load and prevents fraudulent transfers by quarantining high-risk messages and alerting security teams within seconds, directly mitigating financial loss and reputational damage. Integration with Microsoft 365 or Google Workspace APIs is foundational.

Implementation requires orchestrating specialized agents for header spoofing, linguistic anomaly detection, and financial keyword analysis within a framework like LangGraph. The workflow must integrate with financial systems (e.g., SAP, Oracle) to place holds on suspicious payment requests. Critical controls include human-in-the-loop approval for high-stakes actions, immutable audit trails for compliance, and continuous model retraining on new attack patterns. This architecture reduces analyst triage time by over 70% and contains threats before funds are released.

The workflow's value is operational and financial: it automates the detection of sophisticated executive impersonation and fraudulent payment requests that bypass traditional filters, directly preventing wire fraud losses. Savings come from eliminating manual SOC review of thousands of emails and stopping fraudulent transactions before they clear. The architecture is triggered by inbound/outbound email events from Microsoft 365 or Google Workspace, initiating a parallel analysis pipeline where specialized agents assess headers, language, and financial intent.

Implementation integrates with email security platforms via API and connects to financial systems (SAP, Oracle) to place payment holds. Controls are critical: a high-confidence threshold triggers automated containment, while lower-confidence signals route to a human-reviewed queue. The orchestrator, built on frameworks like LangGraph, manages agent execution, state, and audit trails. Observability layers track detection rates, false positives, and mean time to containment, ensuring the system improves while maintaining necessary governance over automated financial actions.

Phase 1 establishes foundational detection by integrating with Microsoft 365 or Google Workspace via Graph API to ingest email metadata and content. A primary orchestrator, built with LangGraph, runs lightweight agents to analyze headers for spoofing, language for urgency/social engineering, and financial terms. All high-confidence detections are routed to a mandatory human review queue in the SOC's SOAR platform (e.g., Splunk, ServiceNow), creating a closed-loop system for validation and analyst feedback without automated action. This initial phase proves detection accuracy and refines agent logic.

Phase 2 introduces conditional automation, where SOC-approved BEC alerts trigger an agent to place an automatic hold on the suspicious email within the gateway (e.g., Mimecast, Proofpoint) and log the event to the financial system of record (SAP, Oracle). Phase 3, deployed only after rigorous testing, enables direct integration with treasury or payment platforms to flag or block initiated transactions. Each phase is governed by immutable audit logs, rollback procedures, and executive dashboards showing detection rates and false positives, ensuring operational control scales with automation trust.

A production BEC detection workflow automates the correlation of weak signals—anomalous sender domains, subtle language shifts, and mismatched financial requests—that human analysts or rules engines miss. The business value is direct: preventing six- and seven-figure wire fraud by intercepting requests before treasury acts. Implementation requires integrating with Microsoft Graph or Gmail APIs for real-time ingestion, a vector store for historical communication patterns, and a rules engine for policy-based holds on suspicious transactions flagged for review.

The orchestration layer, built with LangGraph or a custom state machine, sequences specialized agents for header analysis, NLP, and financial entity recognition. Each agent outputs a confidence-scored risk signal. The decision engine aggregates these into a final action: deliver, quarantine, or block. Critical controls include human-in-the-loop review for medium-confidence cases, immutable audit logs of all agent reasoning, and integration with financial platforms like SAP or Oracle for automated transaction holds. This architecture reduces false positives while ensuring high-risk emails never reach an executive's inbox.

This workflow automates the detection of executive impersonation and fraudulent payment requests that bypass traditional email filters. It analyzes email headers, language sentiment, sender-recipient history, and financial request patterns in Microsoft 365 or Google Workspace. The operational upside is preventing multi-million dollar wire fraud, reducing SOC alert fatigue on BEC, and creating a defensible, auditable detection layer. Implementation requires integrating with email security APIs, financial system holds (like SAP or Oracle), and identity providers for real-time verification.

Governance is enforced through configurable risk thresholds, immutable audit logs of all agent decisions, and mandatory human approval for any automated action that would block a senior executive's email or place a hold on a financial system. Rollout should be phased, starting with monitoring-only mode in a pilot department, then adding quarantines, and finally enabling financial holds after validating low false-positive rates. Monitoring dashboards must show detection rates, false positives, and mean time to human review to ensure the system reduces operational risk without creating new bottlenecks.