Security AI Copilot
SAI is an LLM-powered Agentic SOC, automating alert investigation, triage, and response through integrated AI agents.
Key Features
Core Values
Technical Architecture
The SAI architecture is a 4-layer security AI stack:
Infrastructure layer: Supports X86/ARM and NVIDIA/Ascend hardware.
Model base layer: Built on security-LLM, with dedicated security sub-models for traffic detection, defense, data security, etc.
AI Agents layer: 4 capability matrices covering Identification, Defense, Monitoring and Reaction security scenarios.
Integration layer: Connects to third-party components via API to deliver end-to-end agentic SOC capabilities.
Customer Success Stories
Ethio Telecom's security operations team faced a crippling threefold crisis across personnel, processes and technology before SAI deployment: Critical skills gap: The security team lacked specialized SIEM operation expertise, with no standardized ticketing management system to track incident handling, making it completely impossible to measure team performance or standardize operation workflows. Overwhelming manual process burden: Daily security work relied entirely on burdensome manual processes. Analysts spent most of their working hours sifting through massive false alerts, repeating trivial investigation steps, and had no bandwidth to handle high-risk threats or optimize security strategies. SIEM system management crisis: The legacy SIEM system was stuck in long-standing licensing issues, flooded with irrelevant logs and false positive alerts, and severely lacked customized security detection rules. The system itself had become a source of operational pain rather than a security tool. We deployed the SAI Agentic SOC platform as an "always-awake AI security investigator" for Ethio Telecom. SAI directly integrates with Ethio Telecom's existing SOC tool stack without complicated system transformation, providing end-to-end automated operation capabilities: 24/7 automatic alert monitoring and classification, full-chain incident investigation and evidence collection, standardized ticketing management, built-in security LLM, customizable knowledge base, and multi-functional security agents covering threat detection, code audit, penetration testing and other scenarios. Project Achievements 83% reduction in Mean Time to Identify (MTTI): Cut threat detection latency from 2.8 minutes to near real-time, eliminating the delay caused by manual alert sifting. 67% reduction in Mean Time to Respond (MTTR): Shortened incident response cycle from 1.5 hours to minutes-level rapid response, greatly reducing threat exposure window. 65.9% alert noise reduction: Filtered out most false positive alerts and merged similar events, completely eliminating analyst fatigue caused by massive invalid alerts. 96.08% security operation automation rate: Automatically processed security operation tickets, freeing analysts from repetitive trivial work. 7×24 round-the-clock unattended operation: Filled the gap of off-hours and holiday security monitoring, realizing non-stop security protection without increasing headcount. Rapid team capability empowerment: Equipped the team with professional penetration testing, threat hunting and operation evaluation capabilities through AI agents, rapidly improving the overall security team capability without additional talent recruitment.