Maze is an AI‑native platform that uses autonomous agents to evaluate cloud and container vulnerabilities in context, automatically filtering out 70‑90% of false‑positive findings. By applying large‑language‑model reasoning, it prioritizes the few truly critical exploits and generates one‑click remediation actions integrated with ticketing, WAF policies, and Slack alerts, enabling security teams to shrink backlogs and respond rapidly.
Funding
Funding not disclosed
Founders
Product
Problem
Security teams are overwhelmed by rapidly growing vulnerability backlogs, with 70‑90% of findings being false‑positives that cannot be exploited in their specific cloud environments. Manual triage is slow, costly, and cannot keep pace with the accelerating rate of exploit development.
Solution
Maze provides an AI‑native platform that deploys autonomous agents to investigate each CVE in context, determining whether it is technically exploitable in the customer’s cloud stack. The agents ingest scanner outputs, correlate them with configuration, runtime, and dependency data, and apply large‑language‑model reasoning to assess exploitability, impact, and likelihood. By automatically filtering out non‑exploitable findings, Maze reduces vulnerability backlogs by up to 90%, allowing security teams to focus on the few truly critical issues. For those high‑risk findings, the platform generates actionable remediation steps and integrates with ticketing, WAF policy deployment, and Slack notifications, enabling rapid, one‑click mitigation by engineering teams.
Target Audience
Maze is designed for security engineering leaders, vulnerability management teams, and DevOps engineers in mid‑size to enterprise organizations that need to secure cloud workloads and containerized applications.
Features
- AI agents ingest findings from any scanner and automatically gather relevant cloud, container, and code‑base context.
- Large‑language‑model reasoning evaluates exploitability, required prerequisites, and real‑world impact for each vulnerability.
- Automatic false‑positive elimination removes 70‑90% of non‑exploitable findings, shrinking backlog dramatically.
- Prioritization engine surfaces the few critical, high‑likelihood, high‑impact vulnerabilities for immediate action.
- Intelligent remediation workflow creates verified fix recommendations (e.g., image rebuilds, dependency removal) and provides one‑click execution or ticket creation.
- Integrated incident response actions include WAF policy deployment, Slack alerts, and automated ticket generation.
- API and web dashboard deliver clear, context‑rich reports and allow seamless integration with existing security and DevOps tooling.