Protect AI is a platform for securing machine learning and AI systems, covering model supply chain, vulnerability scanning and LLM runtime protection.
Protect AI addresses security across the AI/ML lifecycle. Its Guardian product scans models and AI artifacts for vulnerabilities and malicious code before deployment, while its platform offers an AI security posture management view of models, datasets and pipelines. Sentinel protects LLM applications at runtime from prompt injection and unsafe outputs.
The company also maintains open-source tooling and a vulnerability database for ML supply chain risks, and acquired Laiyer AI to strengthen LLM security capabilities. Together these give security teams the visibility and controls they need as AI moves into production.
The platform’s Guardian scanner inspects models and AI artifacts for malicious code and vulnerabilities before they enter production, and its security posture management inventories models, datasets and pipelines across the organization. Runtime protections for LLM applications defend against prompt injection and unsafe outputs, while open-source tools and an ML vulnerability database support the wider community. Policy gates integrate into CI/CD so risky models never reach deployment.
Protect AI offers free and open-source tools for individuals, with commercial platform plans for enterprise security teams.
Security and ML engineering teams responsible for the integrity and safety of AI systems in production.
Core scanning tools and open-source projects are free; the enterprise platform is a paid offering.
Yes — the platform integrates with MLOps pipelines via APIs, CI plugins and model registry integrations.
Its focus on the ML supply chain — models and datasets — complements application-layer LLM security tools.
Yes — Guardian scans models from any source, including open model hubs, for embedded malicious code and known vulnerabilities.
Protect AI integrates at the points where machine learning artifacts move. During development and CI, Guardian scans models downloaded from hubs or built in-house before they reach a registry, blocking artifacts containing suspicious code or known vulnerabilities. At deployment, policy gates verify that only approved models ship. In production, runtime protections monitor LLM applications for prompt attacks and unsafe outputs. Security teams get a unified inventory of models, datasets and pipelines, so AI risk can be governed through the same processes used for application security.