Full-spectrum security for code, AI, and everything in between

AI-tuned static analysis that detects exploitable logic flaws in custom code — including vulnerabilities introduced by AI-generated code — directly in the IDE, PR, or CI pipeline.

Call-graph reachability and exploitation scoring cut through CVE noise so teams prioritize the open source risk that matters. Includes full SBOM generation and malicious package protection.

Scan containers, base images, and layered dependencies across the full software supply chain — with automated SLA enforcement and policy governance from development through production.

AI-generated fix suggestions in the IDE, PR, and CI — giving developers the exact change needed, not just the problem. Pairs with Mend Renovate for fully automated dependency updates.

Detect and enforce open source license obligations automatically. Policy rules block non-compliant components before merge and generate clean audit trails for legal review.

Dynamic testing extends coverage into running applications, with API security across REST, GraphQL, and legacy endpoints — catching runtime exposure that SAST and SCA can’t see.

A continuously updated inventory of every AI component — models, frameworks, agents, RAG pipelines, MCPs, and inference providers. Surfaces Shadow AI that conventional tools miss, and exports a governed AI-BOM for security and compliance.

AIWE (AI Weakness Enumeration) scoring evaluates prompt-layer weaknesses against a standardized framework. Identifies injection points, over-permissive instructions, and disclosure risks before they ship — a repeatable engineering practice, not ad hoc testing.

CI/CD-integrated adversarial simulation across eight attack vectors — prompt injection, jailbreaking, PII leakage, model manipulation, sensitive data exposure, insecure model output, retrieval poisoning (RAG), and agent tool misuse. Runs every sprint, not just quarterly, and stress-tests agentic workflows and multi-step reasoning chains.

Real-time behavioral enforcement between users and AI models in production. Monitors live interactions and blocks unsafe outputs before they reach users. Deployed inside your own infrastructure, so sensitive data never leaves your environment.

Audit-ready AI-BOM documentation proving every active agent has been inventoried, stress-tested, and evaluated against the OWASP Top 10 for LLMs. Aligned to EU AI Act requirements — verifiable technical evidence, not self-attestation.

Connects AI component inventory, behavioral risk findings, compliance obligations, and remediation status in one governed workflow — a unified view across the AI layer for board reporting, audits, and internal risk governance.

Auto-creates pull requests for outdated dependencies across 90+ ecosystems including private packages. Each PR includes age, adoption rate, passing tests, and full changelogs.

Merge Confidence ratings predict whether each update will pass before merge. Group, filter, and auto-merge high-confidence updates so low-risk changes never bottleneck the team.

Zero-config scanning across hundreds of repositories, with automatic job scheduling and real-time webhook handling that triggers updates the moment a repo event fires.

Central policy management with team-level override controls. Define update cadence, grouping rules, and approval gates once via renovate.json applied consistently across every repo and monorepo.

Continuous, governed updates keep dependencies current as part of the normal release cycle, not a periodic clean-up project. Reduces both technical debt from outdated packages and security debt from unpatched vulnerabilities.