Conifers provides CognitiveSOC™, an AI‑driven security operations platform that leverages an organization’s own historical tickets, institutional knowledge, and risk policies to automate multi‑tier incident investigations. The system integrates directly with existing ticketing, SIEM, EDR, and threat‑intel tools, enriching alerts, classifying false positives, and generating detailed investigation notes without disrupting current workflows, while continuously learning from analyst feedback to improve accuracy and throughput.
Funding
Funding not disclosed
WHFounders
Product
Problem
Security Operations Centers (SOCs) and Managed Security Service Providers (MSSPs) struggle with low investigation throughput, high false‑positive rates, and a shortage of skilled analysts, leading to delayed response and inconsistent threat handling.
Solution
Conifers offers CognitiveSOC™, an AI‑driven SOC platform that ingests an organization’s own institutional knowledge, historical tickets, and risk policies to automate multi‑tier incident investigations. The system integrates directly with existing ticketing, SIEM, EDR, and threat‑intel tools, pulling additional context, enriching alerts, and determining false‑positive versus true‑positive outcomes without requiring a separate workflow. Results are written back to the original ticket, providing detailed investigation notes and automated closure for benign events. A feedback loop continuously refines the AI models based on analyst input, improving accuracy and scaling investigation capacity. The platform is available in side‑by‑side or full‑production modes and can be deployed within a customer’s Azure tenant or as a hosted service, ensuring data isolation and compliance.
Target Audience
Primary customers are enterprise SOC teams and MSSPs that need to increase investigation efficiency, reduce false positives, and maintain consistent threat response across multiple environments.
Features
- Direct integration with existing ticketing and security toolchains (SIEM, EDR, threat‑intel feeds) to avoid workflow disruption
- Adaptive learning that incorporates each customer’s institutional knowledge, historical incidents, and risk tolerances
- Automated enrichment of alerts with SIEM queries, past ticket analysis, knowledge‑base references, and contextual telemetry
- Autonomous classification of alerts as false positives (auto‑close) or true positives (escalate with detailed notes)
- Telemetry feedback loop that updates AI models from analyst actions to improve future investigations
- Flexible deployment options: side‑by‑side validation mode or full production automation, on‑premise Azure tenant or hosted
- Strategic dashboard delivering KPIs on investigation quality, response proactiveness, and risk reduction for enterprise and MSSP leadership