Case Health AI goes through rigerous security testing and certification processes to ensure your data is protected. Audited by certified 3rd party security firms.
Case Health AI automates the highest-volume workflows in utilization management, care management, and member experience — with AI agents that work inside existing systems.
Audited annually across security, availability, and confidentiality controls.
Built with privacy-by-design principles and validated through regular third-party audits.
Certified against healthcare's most prescriptive risk management framework.
Every AI model used in Case Health AI operates under a formal governance policy covering intended use, risk classification, human oversight requirements, and prohibited applications.
All AI outputs are logged, versioned, and traceable to their inputs — so compliance teams have a complete record of what the model produced and why.
Model performance is monitored continuously for drift, accuracy degradation, and unexpected behavior, with automated alerts for out-of-bounds outputs.
Access to AI outputs, case records, and configuration settings is governed by role-based controls — no user sees data outside their assigned scope.
Case Health AI is deployed on AWS — using the same cloud infrastructure trusted by the largest health systems, payers, and government agencies in the world.
All data is stored and processed exclusively on HIPAA-eligible AWS services, with Business Associate Agreements in place covering every service in our stack.
Data is encrypted at rest and in transit, environments are network-isolated by default, and deployment architecture is designed for 99.99% availability.
Case Health AI undergoes annual penetration testing conducted by independent third-party security firms — not internal teams — with findings tracked to remediation.
Annual black-box and grey-box penetration tests conducted by certified third-party security firms with no affiliation to Case Health AI.
Testing scope covers application layer, API endpoints, authentication flows, and data access controls — aligned with OWASP methodology.
All findings are logged, prioritized by severity, and tracked through to verified remediation before each report is finalized.
Case Health AI operates an active bug bounty program — because external researchers who find vulnerabilities are an asset, not a threat.
Whitehat researchers can responsibly disclose vulnerabilities through a structured submission program with clearly defined scope.
Valid findings are triaged, acknowledged, and rewarded — with severity-based bounties for critical, high, and medium findings.
All disclosed vulnerabilities are remediated and verified before bounty payment is issued.
Researchers who report valid findings are recognized in our security acknowledgments unless they prefer anonymity.
Built for enterprises—handle millions of requests with zero downtime.
