siren: semantic phishing forensics
A high-performance forensic engine that leverages Large Language Models to automate the analysis of complex social engineering attacks.
Average time to perform full semantic forensic analysis on a suspicious email.
Success rate in identifying sophisticated business email compromise (BEC) attempts.
Serverless architecture designed to handle massive phishing campaigns without latency.
the problem: the human bottleneck
traditional phishing triage relies on static indicators (IPs, domains, hashes). sophisticated attackers bypass these using clean infrastructure and social engineering. analysts spend hours reading emails to understand intent.
high-fidelity forensics
intent analysis
the llm identifies the primary goal of the sender, even when hidden behind layers of professional corporate language or urgency tactics.
header forensics
automatically extracts and analyzes metadata structures, correlating them with the body content for absolute validation.
automated reasoning
produces a detailed forensic report explaining its verdict, allowing analysts to trust and verify the decision instantly.
sentinel integration
findings are automatically tagged and injected back into microsoft sentinel, enriching the incident telemetry with high-signal forensic data.
operational impact
siren transforms the phishing triage process from a reactive, manual task into a proactive, ai-accelerated forensic workflow. by eliminating the manual burden of email reading, it allows soc managers to focus their most valuable human capital on complex threat hunting instead of routine triage.