Abundant Security provides an exposure remediation platform that uses AI agents to verify security findings, identify asset owners, and drive fixes to validated closure. The platform prioritizes verified remediation over raw detection, helping security teams reduce real risk and demonstrate measurable progress to leadership.
- Artificial Intelligence
- AI Agents
- Cybersecurity
- Software Only
Funding
Founders
Product
Problem
Security teams are overwhelmed by a high volume of unverified findings, making it difficult to distinguish real threats from false positives. Even when genuine exposures are identified, determining ownership and driving fixes to completion remains a slow, manual process that leaves organizations vulnerable.
Solution
Abundant Security delivers an exposure remediation platform where AI agents confirm which findings are real, identify the responsible owner, and carry each fix through to verified resolution. The platform de-duplicates and ranks findings by meaningful severity, so engineers can focus on what actually matters. Fixes are delivered directly into the tools security teams already use, reducing friction and closing the backlog faster. For CISOs, the platform provides clear metrics on verified remediation counts, exposure age, and where blockers exist, enabling leadership to track real risk reduction.
Target Audience
Primary customers are security engineers and CISOs at mid-to-large enterprises who need to reduce real risk efficiently and demonstrate measurable remediation progress to stakeholders.
Features
- AI agents that validate findings as genuine exposures and automatically determine asset ownership
- De-duplication and severity ranking of findings to prioritize meaningful risk over noise
- Automated fix execution and tracking to verified closure, not just detection
- Integrations with existing security tools and workflows for seamless adoption
- Leadership dashboards showing verified remediation counts, exposure age, and blocker analysis
- Science-backed AI models grounded in published research on adversarial machine learning and malware detection