TRACE: The Reasoning Engine That Changes Everything.
The first AI system that truly reasons about threats rather than simply recognizing them
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How TRACE Works:
Six LLM-Powered Engines
Like having a team of expert security researchers analyzing every email
Detects hidden intent behind emails—such as credential harvesting, financial manipulation, or behavioral coercion—by reasoning through language, context, and user-specific signals.
Modern malware hides in plain sight. We use AI-guided inspection—combining static, behavioral, and contextual signals—to detect threats that bypass traditional sandboxes.
Identifies intent-level anomalies—signs of compromise or coercion—without profiling or exporting user data.
Analyzes language, tone, and sentiment to detect emotional manipulation—like urgency, fear, or trust abuse—using AI models trained to reason like human security analysts.
Knows when users are about to make a mistake—and intervenes. Detects risky clicks, replies, and logins, triggering real-time coaching right in the moment of risk.
Delivers real-time coaching autonomously, tailored to the attack type, user role, and behavior. Learns local patterns to provide private, adaptive guidance at scale.
The Three Pillars of TRACE
Surfaces attacker infrastructure before launch—correlating domain patterns and early signals to block threats days in advance.
Detects malicious intent without historical baselines, securing users from their first message and eliminating the need for a “learning” window.
Applies analyst-like reasoning across signals to identify evolving tactics and adapt in real time—beyond what static rules or black-box models can catch.
True Reasoning Engine vs Traditional Approaches
See why TRACE's LLM-native reasoning fundamentally outperforms pattern-matching and machine learning approaches
Reasoning Engine
Approaches
Detection Method
Learning Period
AI-Generated Attack Defense
Novel Threat Response
Human Training Integration
Deployment Complexity
Under 15 minutes
Weeks to Months
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Frequently Asked Questions
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