Back to Repository
Cybersecurity & GRC·Cybersecurity·Global
AI Phishing & Business Email Compromise Detection
Detect socially-engineered email attacks beyond signature-based filters.
NLPLLMProvenHigh riskMedium complexityHigh valueTime-to-value: 3–6 months~270% est. ROI
Overview
LLM-based content and behavioral analysis scores email risk inline, with auto-quarantine and user banner warnings for high-risk messages.
Business Problem
BEC and spear-phishing bypass signature-based filters and cost enterprises millions per incident.
AI Solution
LLM-based content + behavioral analysis scores email risk in-flow, with auto-quarantine and user banner warnings.
Business Value
Catches 30–50% more advanced phishing than signature filters with low false-positive rates.
Target Users
Director of Security Engineering
Sector Focus
Email Security
Data Requirements
- •Email metadata
- •Historical incidents
- •Org graph
- •Threat intel
AI Technologies Involved
- •Azure OpenAI
- •On-Prem / Private LLM
Implementation Steps
- •Calculate AI ROI
Expected ROI Areas / KPIs
- •BEC catch rate
- •False-positive rate
- •User-reported phish reduction
Governance & Controls
- •Quarterly model evaluation, quarantine appeal process, content-scanning notice.
- •Quarterly model evaluation
- •Quarantine appeal process
Risks & Mitigations
- •High overall risk · Medium complexity
- •Inline integration with mail gateway
- •Encrypted feature store
Cybersecurity
- •Inline integration with mail gateway
- •Encrypted feature store
Privacy
- •Content scanning notice
- •Retention limits on email bodies