Cav automates compliance evidence collection and monitoring across hybrid multicloud environments (AWS, Azure, GCP, on-premises, air-gapped) using PyTorch and TensorFlow. The company is actively building ML models for risk detection and next-gen compliance automation — a shift from manual evidence gathering toward AI-driven continuous monitoring. Senior engineering leadership and a data-focused hiring mix reflect early-stage ML product development rather than mature SaaS operations.
Cav delivers compliance automation software for government agencies and large enterprises operating complex, mission-critical IT environments. The platform provides real-time visibility across 50+ compliance frameworks (including NIST, FedRAMP, SOC 2) and automates evidence collection, control monitoring, and reporting across cloud, on-premises, and air-gapped systems. Founded in 2017 by military veterans, the company addresses a core operational pain point in high-reliability organizations: the manual, expensive, error-prone work of continuous compliance. The platform is deployed by Department of Defense contractors and Fortune 500 companies managing distributed infrastructure.
Cav integrates with AWS, Azure, Google Cloud, and IBM Cloud, plus on-premises and air-gapped environments. FedRAMP compliance is supported.
Core ML/data: PyTorch, TensorFlow, Python, Pandas, NumPy. Infrastructure: AWS, Azure, GCP. Operations: Salesforce, Jira, ServiceNow, SQL.
Cav has 8 active roles focused on engineering (3), support (2), data (1), product (1), and marketing (1), with minimal posting velocity. Hiring is U.S.-based.
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