Conifers CognitiveSOC

Conifers CognitiveSOC is an AI SOC agents platform designed to automate and augment security operations center workflows. It uses adaptive learning, institutional knowledge ingestion, and a telemetry pipeline to handle multi-tier incident investigation and resolution at scale. The platform operates through a six-stage pipeline: use case classification with institutional knowledge, investigation planning, investigation and enrichment, incident resolution, result reporting back to the ticketing system, and a telemetry feedback loop for continuous improvement. When an incident is detected, CognitiveSOC receives the event from the existing ticketing system, runs SIEM queries for additional context, reviews historical tickets for patterns, applies knowledge base remediation steps, queries threat intelligence feeds, and fetches enrichment data from EDR and identity platforms. It then autonomously determines whether the event is a false positive (auto-closed) or a true positive (escalated to analysts with full investigation notes). The platform uses a multi-model analytic approach combining LLMs, SLMs, ML, statistical analysis, and static analysis, all fine-tuned to the organization's assets, risk tolerance, business patterns, and processes. Key operational features include: - Adaptive, risk-based decision-making with a feedback loop to reduce noise and improve detection accuracy - Strategic dashboard providing visibility into investigation quality, response proactiveness, and overall risk reduction - Staged implementation framework (side-by-side mode and full production mode) - Support for both enterprise and MSSP environments with multi-tenancy capabilities The platform is designed to be non-disruptive, integrating with existing tools, processes, and ticketing systems without requiring workflow changes.