Unified Detection Platform for Hybrid SIEM and Data Lakes

COMPANY OVERVIEW

Unified detection and triage platform for hybrid SIEM and data lake environments. Runs on Splunk, Sentinel, Snowflake, Databricks, and Azure Data Explorer without replatforming. Delivers detection-as-code, agentic AI workflows, and cross-stack correlation to modernize SOC operations while reducing costs by 80% compared to legacy SIEMs. SOC2 certified with deployments across Fortune 500 enterprises including eBay, T-Mobile, SAP, and Siemens.

CORE FOCUS

Scales detection engineering and alert triage across fragmented security toolsets using AI-powered automation. Correlates multi-stage attack scenarios across endpoint, identity, cloud, and network telemetry streams without duplicating logic. Reduces detection build time by 5-6x and alert volume by 90% through tuning agents that continuously optimize rule sets. Deploys in hybrid moderunning detections simultaneously across legacy SIEMs and modern data lakes during migration. Eliminates vendor lock-in by abstracting detection logic into SPL, KQL, or SQL query languages agnostic to underlying storage platforms.

PRODUCTS & TOOLS

AI SOC Platform Detection-as-code engine with agentic workflows for building, testing, and deploying threat scenarios.

• Visual threat scenario builder correlates attack patterns across data sources mapped to MITRE ATT&CK by tactic, technique, and threat group
• Drag-and-drop filter components or natural language prompts extract behaviors and generate SPL/KQL/SQL logic instantly
• Prebuilt detection library with thousands of scenarios customized by industry vertical and data source
• CI/CD integration for version-controlled detection deployment pipelines

AI-Assisted Triage Automated investigation workflows with prebuilt timelines, MITRE mapping, and verdict enrichment.

• Single investigation panel consolidates multi-domain telemetry with triage protocol recommendations
• Analyst decisions convert into repeatable playbooks that scale expertise across SOC tiers
• Cuts 45% of alert noise with 98% confidence in benign classification
• Saves 70 analyst hours per day through automated triage workflows

Tuning Agents Machine learning models that continuously monitor detection effectiveness and optimize rule performance.

• Assesses alert data lake to identify false positive patterns and recommend tuning adjustments
• Reduces triage fatigue by suppressing benign alerts while maintaining coverage
• 98% accuracy in benign alert identification

Hybrid Detection Architecture Unified detection layer supporting dual-run across SIEM and data lake platforms.

• Correlates signals across Splunk, Sentinel, Snowflake, Databricks, Azure Data Explorer simultaneously
• Enables phased SIEM modernization without full replatforming or coverage gaps
• 90%+ cost savings via data lake storage optimization

TARGET MARKET

Enterprise security teams managing hybrid detection stacks across legacy SIEMs and modern data lakes. Organizations seeking SIEM cost reduction through data lake adoption while maintaining detection continuity. SOC teams requiring scalable detection engineering workflows for lean analyst headcount. Cloud-native startups and mid-market companies building detection strategies from scratch.

DIFFERENTIATORS

Detection-as-code abstraction layer prevents vendor lock-inwrite logic once, deploy anywhere across Splunk, Sentinel, Snowflake, or Databricks. Agentic workflows automate the full detection lifecycle from scenario design through tuning, eliminating manual rule maintenance overhead. Hybrid deployment model supports dual-run architectures during SIEM migration, ensuring zero coverage degradation. 60-80% reduction in detection engineering effort through AI-assisted development and prebuilt scenario libraries mapped to threat intelligence frameworks. Fortune Cyber 60 recognition for two consecutive years validates market-leading innovation in AI SOC modernization.