Context-Aware Log Analysis
Synthesize millions of telemetry tokens into clear incident reports — intent reconstructed from raw noise, zero alert fatigue.
Agentic AI · Autonomous Threat Copilot
Autonomous AI Copilot for Enterprise Threat Detection
HuangSec harnesses advanced LLM reasoning to process telemetry data, detect zero-day exploits, and automate security patch generation in real time.
-92%
Alert noise
<3.4s
MTTD reduction
24/7
Autonomous watch
09:41:02TELEingest → telemetry + audit + k8s + repo events
09:41:04WARNanomalous IAM assume-role burst · principal=svc-deploy
09:41:05CRITzero-day candidate correlated · score 0.94
09:41:07PATCHauto-fix generated → PR #3142 opened
09:41:08DONEincident report synthesized
Core Capabilities
An AI copilot that reasons across your entire stack
Three agentic engines working in concert — synthesize, pinpoint, remediate. Built for the speed of modern cloud-native security teams.
Agentic Vulnerability Triage
Pinpoint vulnerabilities in codebases and recommend automated code fixes — every finding shipped with a context-aware patch.
Real-Time AI Incident Response
Actively suggest remediation steps to cut down mean time to detect (MTTD) — actioned before an analyst opens the ticket.
AI Pipeline
One agentic loop, three stages
Telemetry in, mitigation out. Every stage is autonomous, explainable, and under your policy controls.
01
Data Telemetry
Stream logs, metrics, cloud audit trails, and repository events into a single normalized, searchable context plane.
02
Deep Reasoning
Advanced LLM reasoning correlates weak signals, reconstructs attack intent, and surfaces zero-day candidates with confidence scoring.
03
Automated Incident Mitigation
Generate patches, trigger response runbooks, and open remediation PRs — closing the loop without human latency.