Cycode

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.

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.

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.

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

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.

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.

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.

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.

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.

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 drift between the infrastructure-as-code definition and the resource actually deployed in the cloud, including out-of-band console changes, and remediates it by regenerating the IaC script or reverting the resource, keeping code as the source of truth.

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.

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.

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

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

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

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

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.

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

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