Legit ASPM Platform

Maintains a registry of all applications in scope, their associated scan coverage, and their AppSec tool assignments, surfaces applications with no active scanning.

Ingests, deduplicates and normalizes signals from security tools across DevSecOps pipelines and runtime environments (SAST, DAST, SCA, container scanning, secrets scanning, runtime and cloud telemetry) into a single finding model with a consistent severity scale across sources.

Groups findings from multiple tools that refer to the same underlying vulnerability in the same code location, presenting one actionable finding instead of multiple redundant alerts.

Scores aggregated findings using multiple contextual factors (exploitability, reachability, internet exposure, threat intelligence, and business criticality) rather than individual tool severity ratings, producing a single actionable priority queue across all AppSec signals.

Integrates and triggers AppSec scanners across the pipeline, controlling which tests run at each stage (pull request, build, release) according to organizational policy rather than leaving each tool to run on its own schedule.

Pushes prioritized findings to developer ticketing (Jira, GitHub Issues, Linear), and IDEs with remediation context, removing the security team from the routing path.

Maps aggregated AppSec findings and scan coverage to regulatory and framework controls (PCI DSS Requirement 6, ISO 27001 Annex A.8.28, SOC 2), and generates audit-ready evidence and compliance reports across the application portfolio.

Acts as the application security control plane: evaluates all applications against organization-wide policies, risk thresholds and remediation expectations, then automates enforcement through build gates, release blocks and escalation rather than only flagging non-compliant applications.

Links each finding to the specific code, component, or pipeline that introduced it and traces it from source through build to the deployed runtime, so teams can fix the underlying cause and see which projects contribute the most risk.

Classifies aggregated findings with an AI model as real vulnerability, likely false positive, or needs review, and assigns a remediation urgency, so the priority queue is filtered by verdict rather than by tool severity alone.

Assessment and policy enforcement of CI/CD pipeline configuration, access, and integrity.

Risk context for open-source dependencies including reachability, exploitability, and upgrade impact.

Live visibility into code, components, pipelines, and developer activity across the software development lifecycle.

On-demand generation of software, firmware, and hardware bills of materials (SBOM, FBOM, HBOM) for endpoints, servers, and network devices, extending component inventory below the application layer.

Assessment of developer and machine identity access and permissions across source control and pipelines.

Governs third-party software consumption to apply consistent software supply chain security policy.

Detection and provenance tracking of AI and ML components, models, and LLM usage within the software supply chain.

Identifies commits and pull requests written or materially changed by AI coding agents and assistants, attributes them to the responsible developer and repository, and routes them for closer security review, so that the volume of agent-generated code is verified at creation time rather than discovered after deployment.