Security Operations Intelligence
Semantic intelligence for every security decision, from alert triage to agent governance to SOC threat forecasting, deployed on-premises at machine speed.
Four specialist models plus one SOC threat-intelligence foundation model bring semantic understanding to alert classification, secret detection, AI agent governance, pre-delivery compliance, and end-to-end SOC verdict/risk/stage/forecast over 64-event telemetry windows. Each deploys on your infrastructure at under 50ms. When a new threat vector appears, LEAP adapts the relevant specialist same-day; for the SOC foundation model a new business signal is a head, not a new model build. Your security posture evolves at the speed of the threat, not the speed of a vendor release cycle.
5 specialist models
How It Works
One specialist model per security function,
trained on your threat landscape — plus one SOC foundation model that reads the full event window in a single forward pass
Every Alert Triaged Without Hiring More Analysts
SOC teams receive 10,000+ alerts per day. Static SIEM rules have no semantic understanding. Only a fraction get investigated. Each false positive wastes 30 minutes of analyst time. A specialist LFM classifies every alert as Critical, Investigate, or Noise in under 50ms. One GPU triages 10,000 alerts in 10 minutes. Analysts stop drowning in noise and start hunting real threats. When new attack patterns emerge, the model adapts same-day via LEAP. Your triage evolves continuously, not on quarterly vendor update cycles.
Semantic Secret Detection That Understands Code Context
Developers commit credentials daily. Pattern-based secret scanners catch known formats but miss Base64-encoded keys, concatenated tokens split across variables, and obfuscated credentials in configuration files. A specialist LFM provides semantic secret detection that understands code context, not just regex patterns. Run it as a pre-commit gate at 50ms per file. New secret formats and obfuscation techniques adapt via LEAP in minutes. The model understands what a secret looks like in context, not just what it looks like as a string.
The Pre-Flight Check Every AI Agent Needs Before Execution
AI agents are executing tool calls in production: resetting passwords, granting access, running scripts. Most execute unchecked. Keyword filters block legitimate requests alongside threats. Cloud validation adds hundreds of milliseconds per call. A specialist LFM intercepts every tool call before execution and classifies it as allow, deny, or hold-for-approval in 15ms, faster than the call itself. It distinguishes routine operations from privilege escalation attacks semantically. The model governs your agents at a speed that does not degrade their performance.
Pre-Delivery Compliance at 15ms, Not Post-Delivery Detection
Financial firms spend millions on communications surveillance. Keyword-based detection runs fast but generates overwhelming false positives. Enterprise compliance platforms catch violations hours or days after the message was sent. A specialist LFM analyzes messages before delivery at 15ms, catching insider trading signals, material non-public information sharing, and market manipulation in real time. Violations are blocked, not just detected. False positive rates drop by an order of magnitude. New regulatory patterns adapt via LEAP same-day.
Ten SOC Signals from One Read of the Telemetry Window
SOC teams run separate detectors for verdict, risk, attacker stage, next-tactic forecast, and reviewer signals (identity compromise, lateral movement, exfil likelihood). Each is its own pipeline, refresh cadence, and false-positive surface — and cross-signal context (a discovery → credential-access trajectory implying lateral movement next) is invisible to any single classifier. The cyber encoder collapses ten of those signals into one shared model: a small cyber telemetry encoder turns 64 events × 15 normalized fields into 960 pseudo-tokens, the frozen LFM2.5-350M-Base backbone processes them with attention LoRA, and ten task heads predict in parallel in roughly 10 milliseconds warm on H100. Trained on 61K real and semi-real anchors from OTRF, Splunk Attack Data, and LogHub plus synthetic expansion. Held-out real-anchor eval: attack-presence 99.8%, lateral-movement 87.3%, exfil likelihood 90.9%, benign-admin confounder 94.5%. Adding persistence likelihood or a custom analyst label is a new head, not a new model.
Try each model
All Demos
SOC Alert Triage
Reduce alert fatigue with intelligent classification
95% of SOC alerts are noise — LFM filters them in real-time
Code Secret Scanner
Detect API keys, database credentials, and secrets in source code that regex misses
Regex catches the obvious API keys. LFM catches the Base64-encoded ones hiding in plain sight
Agentic Pre-Flight
Validate AI agent tool calls for security risks before execution at 15ms
Every AI agent tool call validated at 15ms — faster than the tool call itself
Compliance Filtering
Pre-delivery message compliance — block violations before they’re sent
Pre-delivery compliance — block violations before they’re sent, not 48 hours later
SOC Threat Intelligence Foundation Model
One model that reads a 64-event security telemetry window and returns ten SOC signals together: attack verdict, risk score, current attacker stage, likely next tactic, identity compromise, lateral movement, exfil likelihood, and analyst response actions — in a single forward pass on H100.
One encoder + frozen LFM2.5-350M + LoRA + ten SOC heads: verdict, risk, stage, forecast, and reviewer signals predicted in a single ~10ms forward pass on H100.
Ready to deploy in your environment?