How does Vigil differ?

LLM-native architecture with 13 specialized agents vs traditional rule-based SOCs.

Alert processing 45 to 3 min, false positives 90% to 15%, detection 65% to 89%.

DeepTempo Launches Vigil: First Open-Source AI SOC with LLM-Native Architecture

DeepTempo launched Vigil at RSA 2026, the first open-source AI SOC built with LLM-native architecture. Features 13 specialized AI agents, 7,200+ detection rules, and 30+ integrations. Benchmarks show 15x faster alert processing and 89% threat detection coverage.

Vigil: The First Open-Source AI SOC Built with LLM-Native Architecture

On March 23, 2026, AI security startup DeepTempo officially launched Vigil at RSA Conference 2026 — the industry's first open-source AI Security Operations Center (SOC) built from the ground up with an LLM-native architecture. Licensed under Apache 2.0, Vigil represents a paradigm shift from traditional rule-based detection systems toward AI-driven intelligent security operations.

Why We Need an LLM-Native SOC

Traditional Security Operations Centers face severe efficiency bottlenecks. Security analysts process thousands of alerts daily, with over 90% being false positives. SIEM systems aggregate log data but lack deep understanding of attack context. Existing SOAR platforms provide some automation through pre-written playbooks, but these static workflows cannot adapt to constantly evolving attack techniques.

Vigil's core innovation lies in using LLMs as the SOC's reasoning engine rather than simply bolting AI features onto a traditional architecture. This means the entire workflow — from alert classification and threat assessment to incident investigation and response execution — is driven by collaborative LLM-powered agents.

The approach differs fundamentally from what major vendors like Microsoft (Security Copilot) and Google (Chronicle SOAR) have been doing. While those products add AI capabilities to existing architectures, Vigil starts with LLM reasoning as its foundation and builds security operations around it.

13 Specialized AI Agents in Collaborative Architecture

Vigil employs a multi-agent architecture comprising 13 purpose-trained AI agents, each responsible for different aspects of security operations:

Detection Layer Agents: Alert Triage Agent classifies and prioritizes alerts with business context understanding; Threat Detection Agent performs behavioral analysis-based detection beyond signature matching; Anomaly Detection Agent combines statistical models with LLM reasoning for pattern identification.

Investigation Layer Agents: Investigation Agent automates incident investigation by correlating multi-source data to build attack timelines; Threat Hunting Agent conducts proactive threat hunting through hypothesis-driven analysis; Forensics Agent handles digital forensic analysis with evidence extraction.

Response Layer Agents: Incident Response Agent coordinates response workflows; Remediation Agent executes remediation actions; Playbook Agent dynamically generates and adjusts response playbooks.

Support Layer Agents: Knowledge Agent manages threat intelligence; Compliance Agent ensures regulatory compliance; Reporting Agent generates analysis reports; Orchestrator Agent coordinates all agent workflows.

The multi-agent design allows each agent to specialize in its domain while the Orchestrator ensures coherent end-to-end security operations. Unlike single-model approaches, this architecture can handle parallel investigations, escalate between agents when domain expertise changes, and maintain separate context windows for different aspects of an incident.

7,200+ Detection Rules and 30+ Integrations

Vigil ships with over 7,200 detection rules covering the major tactics and techniques in the MITRE ATT&CK framework. These are not static signatures but dynamic detection logic enhanced with LLM reasoning capabilities that adaptively adjust detection thresholds based on contextual factors.

On the integration front, Vigil provides 30+ pre-built integration connectors supporting mainstream security tools: Splunk, Elastic Security, CrowdStrike Falcon, Microsoft Sentinel, Palo Alto Cortex XSOAR, and others. Through standardized API interfaces and MCP support, Vigil can rapidly integrate with existing security infrastructure.

Performance Benchmarks

DeepTempo presented performance results at the RSA launch: mean alert processing time dropped from 45 minutes to 3 minutes, false positive rate from 90%+ to 15%, threat detection coverage improved from 65% to 89%, and incident response time from 4 hours to 25 minutes.

The 15x reduction in alert processing time directly addresses analyst fatigue and alert overload. By automating initial triage and investigation, Vigil allows human analysts to focus on critical cases requiring human judgment.

Open-Source Strategy and Business Model

Released under Apache 2.0, with core engine and all 13 agents fully open-source. DeepTempo's business model is based on enterprise value-added services: managed cloud hosting, premium threat intelligence feeds, 24/7 expert support, and industry-specific compliance modules.

Industry Implications

Vigil's launch coincides with RSA 2026's theme of "Securing AI Agents." Industry analysts predict that LLM-native SOCs will become a mainstream trend within 2-3 years. Gartner places "AI-Native Security Operations" in the Peak of Inflated Expectations, with projected productivity plateau around 2028.

Furthermore, this development signifies a broader transformation in the global technology landscape. As governments and enterprises worldwide continue to increase their investments in AI technologies, the accompanying legal and regulatory frameworks are also evolving rapidly. Regulators face the ongoing challenge of balancing innovation promotion with user protection.

From an industry perspective, these changes are driving increased cross-sector collaboration and integration. The convergence of traditional industries with AI technologies is creating new business models and value chains. Simultaneously, talent development and technology standardization have become critical factors for industry advancement.

Looking ahead, we can anticipate continued rapid development in this field, bringing forth more innovative applications and solutions. Companies must maintain acute market awareness and adjust their strategic directions promptly to adapt to emerging technological trends. User experience enhancement and data security assurance will become core competitive elements.

The implications extend beyond immediate technical considerations to encompass broader socioeconomic impacts. Organizations must prepare for significant shifts in operational paradigms while ensuring sustainable and responsible implementation of these technologies.