StrongestLayer’s detection pipeline is built on an LLM-native architecture that emulates human analyst reasoning at machine scale. By combining autonomous threat intelligence, behavioral inference, and contextual understanding, we detect novel phishing threats designed to bypass traditional defenses
We ingest a broad range of structured and unstructured data - email content, metadata, browser activity, behavioral patterns, and environmental context - to build a foundation for analysis that goes far beyond pattern-matching or static rules.
Our intelligence engine continuously enriches and correlates IOCs using passive DNS, infrastructure clustering, screenshot similarity, and AI-guided threat hunting - surfacing related artifacts and mapping zero-day threats before they appear in public feeds.
Our LLM-native architecture performs analyst-style reasoning by assessing sender-recipient relationships, linguistic tone, behavioral deviations, and broader business context - identifying novel threats with no prior signature or known pattern.
Our detection engine synthesizes contextual insights, threat intel, and inferred attacker intent to deliver a final verdict—complete with MITRE TTP mapping, confidence scoring, and transparent reasoning paths. Decisions are enforced instantly (inline controls, banners, quarantine) with full explainability for SOC validation and forensic investigations.

Traditional pattern-matching was dead - attackers were using the same LLMs to generate unique threats faster than defenders could adapt.
The problem: While criminals weaponized AI for offense, cybersecurity was still fighting with yesterday's tools. Rules, signatures, and behavioral analysis couldn't keep up with AI that generates never-before-seen attacks.
Our breakthrough: Our solution thinks like expert security researchers - analyzing intent, correlating global threat intelligence, and predicting campaigns weeks before they launch. Finally, AI powerful enough to outsmart criminal AI.
Tomorrow's Threats. Stopped Today.