Serion AI provides an autonomous, cloud‑based penetration‑testing platform that continuously scans source code, binaries, and configuration files across multiple programming languages. Its deep‑learning engine and predictive zero‑day models generate real‑time vulnerability scores and risk‑based remediation guidance, with API and SIEM integrations for security and DevSecOps teams.
Funding
$25M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
BGFounders
Product
Problem
Enterprises rely on periodic manual penetration tests and signature‑based scanners, which are labor‑intensive, often miss language‑specific flaws, and cannot keep pace with emerging zero‑day exploits. The resulting gaps leave critical assets exposed between assessment cycles.
Solution
Serion AI delivers an autonomous penetration‑testing platform that continuously scans codebases and runtime environments across multiple programming languages. Advanced neural networks evaluate source code, binaries, and configuration files, generating vulnerability scores in real time. Predictive zero‑day models extrapolate from known exploit patterns to flag previously unseen weaknesses. The system updates its detection models after each scan, creating a self‑reinforcing learning loop that improves accuracy without human intervention. Automated reports prioritize findings by risk, map remediation steps, and expose actionable insights through a RESTful API and native SIEM connectors. By operating 24/7 in the cloud, the platform provides near‑instant threat intelligence, reducing the window between vulnerability emergence and mitigation.
Target Audience
The primary customers are security operations teams and DevSecOps groups within mid‑size to large enterprises, as well as managed security service providers that need automated, high‑coverage vulnerability assessment across diverse technology stacks.
Features
- Deep‑learning detection engine achieving ~95 % vulnerability identification accuracy across supported languages (Python, JavaScript, Java, PHP) with roadmap for C/C++ and 20+ additional runtimes
- Zero‑day predictive analytics that leverage graph‑based exploit modeling to anticipate novel attack vectors before exploitation
- Continuous model retraining pipeline that ingests scan results and threat feeds, enabling autonomous improvement of detection heuristics
- Automated, risk‑based reporting with CVSS scoring, remediation guidance, and export formats for ticketing systems (Jira, ServiceNow)
- Scalable cloud architecture with multi‑tenant isolation, supporting unlimited concurrent scans and sub‑hour detection latency
- Open API and native integrations for SIEM, SOAR, and DevSecOps toolchains (GitHub Actions, Jenkins, Azure DevOps)
- End‑to‑end encryption and role‑based access control to meet ISO 27001 and SOC 2 compliance requirements