Onit provides AI-native exposure management that automates triage, prioritization, and ownership of security tickets, turning repetitive remediation tasks into a few high‑level decisions. By integrating with existing security stacks, its agentic system continuously enforces those decisions as operating rules, collapsing thousands of exposures into actionable judgments and reducing manual remediation time from weeks to minutes.
Funding
Funding not disclosed
Founders
Product
Problem
Security teams spend extensive time repeatedly triaging the same vulnerability exposures, guessing ownership, and manually remediating issues, leading to slow ticket resolution and inconsistent coverage across tools.
Solution
Onit delivers an AI-native exposure management platform that transforms repetitive triage and manual remediation into decision-based automation. By analyzing large volumes of exposure cases, the system identifies recurring human actions and consolidates them into a small set of upstream decisions. These decisions are encoded as living operating rules that are enforced across the existing security stack, enabling AI agents to continuously execute remediation actions. The platform operates without requiring a replacement of current tools, allowing organizations to close tickets in minutes rather than weeks while maintaining consistent coverage.
Target Audience
Primary customers are security operations teams, vulnerability management groups, and SOCs that need to streamline exposure triage and accelerate remediation across their existing security infrastructure.
Features
- AI-driven analysis of thousands of exposure cases to extract repeatable remediation patterns
- Decision-based rule engine that converts concise human judgments into executable operating rules
- Agentic automation that continuously enforces rules across integrated security tools, preventing coverage gaps
- Seamless integration with existing security stack, avoiding rip‑and‑replace migrations
- Real-time ticket closure and prioritization, reducing manual effort and ownership ambiguity