Xygeni AppSec 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.

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.

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.

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

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.

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.

Scores dependency vulnerabilities by whether the vulnerable function is reachable in the actual application execution path, not just present in the dependency tree, reducing the actionable finding list to confirmed code-level exposures.

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.

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.

Prioritizes dependency vulnerabilities using exploitation signals such as EPSS probability and the CISA Known Exploited Vulnerabilities catalog, ranking findings by real-world exploitation likelihood rather than CVSS severity alone.

Imports or generates Vulnerability Exploitability eXchange documents asserting whether a known CVE actually affects a given product in its deployed context, including statements derived from reachability analysis so an SBOM ships with evidence-backed exploitability. Reduces false positives in downstream consumers of SBOMs.

Identifies hardcoded credentials, API keys, tokens, and private keys in source files. Operates on the repository and commit history, not at runtime.

Exports the dependency inventory as a machine-readable Software Bill of Materials in SPDX or CycloneDX format, consumable by downstream vulnerability scanners, compliance tools, and procurement workflows.

Defines open source policies (banned licenses, blocked packages, version floors, severity gates) as version-controlled rules applied automatically at scan time across repositories.

Opens PRs with upgraded dependency versions that resolve CVEs. Quality differentiation is whether the fix resolves transitive chains or only direct dependencies, and whether the PR is merge-safe without manual review.

Identifies OSS licenses in the dependency tree and flags conflicts with the project's target license or policy (GPL contamination, copyleft obligations, export-controlled components). Separate from vulnerability detection.

Identifies packages with known-malicious behavior (typosquatting, dependency confusion, backdoored releases), distinct from packages with CVEs in legitimate code.

Blocks or flags PRs in CI/CD pipelines based on policy-defined thresholds, configurable by severity, CVSS score, exploitability, fix availability, or CVE age. Prevents vulnerable code from merging without requiring zero-tolerance policies.

Determines whether a vulnerable function is actually reachable and invoked, not merely present in the dependency tree, cutting actionable CVEs down to those with real exploit paths. Delivered either statically, by call-graph analysis layered on dependency scanning, or at runtime, by instrumenting the workload to observe which components actually execute.

Traverses the full dependency graph to surface CVEs in indirect dependencies, packages required by your direct dependencies. Direct-only scanning misses the majority of vulnerable code paths in modern polyglot projects.

Detects code tampering and verifies build reproducibility by comparing released binaries against expected build behavior, surfacing supply chain compromises introduced between source and release.

Verification of build integrity and artifact provenance through signing, attestation, and change attribution.

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

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

Deep analysis of binaries and packages to detect tampering, malware, and hidden threats beyond manifest-based scanning.

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.

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

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

Detection of exposed secrets and credentials in build artifacts and software packages, with prioritized remediation that distinguishes active credentials from stale ones.

Scans infrastructure-as-code definitions across Terraform, CloudFormation, ARM and Bicep, Pulumi, Kubernetes YAML and Helm charts against security and compliance policies before deployment, so misconfigurations are caught in code rather than in production. Framework coverage and check depth per framework vary across products.

Detects credentials, API keys, tokens and certificates embedded in infrastructure-as-code files, variables and the repositories that hold them, flagging them in the pull request and pipeline before they are committed or deployed.

Integrates as a productized step in CI/CD pipelines and stops or fails a build when infrastructure-as-code findings exceed a policy-defined risk threshold, with severity thresholds and documented exceptions, so insecure infrastructure cannot reach deployment.

Lets teams author and version their own infrastructure-as-code policies in a policy language such as Open Policy Agent Rego or a vendor rules format, alongside prebuilt policy packs mapped to CIS and other benchmarks, so organization-specific guardrails are enforced with the same tooling as standard checks.

Surfaces infrastructure-as-code findings inside the developer's IDE and as inline pull-request comments with suggested fixes, so misconfigurations are corrected at authoring time rather than after a pipeline failure.