ScanDog provides an application security platform that automates vulnerability management by turning raw findings into context‑aware recommendations, allowing teams to spend less than an hour per week on ASPM. Its smart prioritization engine enriches alerts with open‑source intelligence, reachability, exploitability and business impact, while an LLM‑powered security copilot flags issues, explains impact and suggests fixes, accelerating remediation and reducing false‑positive noise.
Funding
Funding not disclosed
Founders
Product
Problem
Application security teams are overwhelmed by large volumes of vulnerability findings from multiple scanners, many of which are false positives or low‑impact issues, leading to excessive manual effort and missed critical threats.
Solution
ScanDog provides an application security platform that aggregates findings from diverse scanners and enriches them with context‑aware data such as reachability, exploitability, EPSS/KEV scores, and business impact. Its AI‑driven prioritization engine surfaces the top 5 % of critical vulnerabilities, allowing teams to focus on real threats. Integrated LLM‑based security copilot generates concise impact analyses and auto‑fix suggestions with minimal diffs, while the automation engine routes fixes to the appropriate developers and tracks remediation progress in a live dashboard. The platform supports seamless deployment, CI/CD integration, and connections to ticketing, pipeline, and notification systems, enabling end‑to‑end vulnerability management with less than an hour of weekly effort.
Target Audience
Primary customers are application security teams, DevSecOps engineers, and compliance officers in mid‑size to large enterprises that need to manage vulnerability data across multiple development pipelines.
Features
- Language‑aware static analysis rules for modern stacks (TypeScript, Go, Python, Java, IaC) that map risky flows and insecure APIs
- Reachability analysis that prioritizes findings actually executable in runtime paths
- Smart prioritization using open‑source intelligence (EPSS, KEV), exploitability, and business impact to surface <5 % critical issues
- LLM security copilot that provides context‑rich impact explanations and generates safe, minimal code diffs for auto‑fixes
- Automated remediation engine that creates pull requests, assigns them to the right engineers, and updates ticketing systems (Jira, Linear, Azure Boards)
- Centralized live Remediation Center with dashboards for MTTR tracking, progress visualization, and stakeholder‑specific views
- One‑click integration with major CI/CD platforms (GitHub, GitLab, Azure DevOps) and notification channels (Slack, MS Teams, Google Chat)
- Comprehensive coverage including SAST, SCA, DAST, IaC scanning, secret scanning, and SBOM generation