Automated Application Security Triaging in Software

AI triage systems analyze vulnerability findings from GitHub Advanced Security, Snyk, and Checkmarx against your actual application architecture - pulling dependency graphs, production deployment status, and customer-impact context from Datadog - to rank findings by real exploitability rather than generic CVSS scores. The system automatically deduplicates findings across scanners, enriches each with remediation guidance and affected service inventory, then routes only high-signal findings to engineers with pre-populated context. The change engineers feel first: near-zero time spent on low-risk or duplicate alerts, with MTTR and triage-hour reduction targets modeled from your own alert volumes during scoping.

Yes. The system we deploy runs inside your own environment under your existing permissions, and implements zero-retention policies for AI processing - your vulnerability data, dependency graphs, and deployment metadata are processed in-memory and never stored in external model training sets. We support air-gapped deployments for FedRAMP and HIPAA customers, running the triage engine entirely within your AWS/GCP/Azure VPC. All data flows directly from your GitHub, Jira, and infrastructure systems through encrypted channels, with audit logging designed to support your GDPR and CCPA data-handling obligations.

Plan for a working system inside the first 100 days. The process breaks into three phases: weeks 1-3 cover system architecture design and GitHub/Jira/Datadog integration setup; weeks 4-8 involve training the AI model on your historical vulnerability findings and establishing human review workflows; weeks 9-14 include staged rollout to pilot teams, accuracy validation, and full production launch. A rollout like this is scoped to show measurable MTTR improvements and alert noise reduction within 60 days of go-live as the system begins learning your organization's risk patterns.

It sits between your scanners and your engineering workflow. Findings flow in from GitHub Advanced Security, Snyk, Checkmarx, and Dependabot; the engine deduplicates them across tools, checks each against your dependency graph and production deployment status, and attaches remediation guidance plus the list of affected services. Only high-signal findings reach engineers, in a single Jira-native queue. Your team approves ticket creation and overrides rankings when business context demands it - and every override teaches the model your risk appetite.

Three that an engineering leader can measure. Noise: duplicates and non-exploitable findings never reach an engineer, so the daily queue shrinks from dozens of alerts to the handful that matter. Speed: P1 findings arrive with context already attached - affected services, estimated fix effort, remediation guidance - so the response clock stops burning on research. Capacity: the hours that went to manual sorting return to feature work, which is the budget line this system is actually judged against.