Identify and secure AI workloads in the cloud

Discover all AI workloads across AWS, Azure, and GCP, including compute, containers, Kubernetes, data stores, and model repositories. Use continuous monitoring to track every AI workload.

Automatically identify and classify sensitive data in AI training pipelines, inference endpoints, and vector databases. Know before data processing whether it contains PII, proprietary IP, or regulated info.

Surface critical AI exposures using Vulnerability Priority Rating (VPR) and correlate data sensitivity, infrastructure vulnerabilities, identity risks, and external exposure to focus on what attackers will exploit first, not just misconfigurations.

Scan AI workloads for vulnerabilities, misconfigurations, secrets exposure, anomaly detection, and weak identity policies. Categorize findings across cloud workload protection, identity and access management (IAM), network, compute, data, Kubernetes.

Enforce data protection and least-privilege policies as code across the AI lifecycle. Automatically align security guardrails with dynamic AI workloads to prevent exposure drift as infrastructure scales.