Spring Health operates a global mental health platform where sensitive clinical data flows through machine learning models that match individuals to care - therapy, coaching, medication management. The attack surface is significant: protected health information crossing 40+ countries, processed by algorithms validated in over 30 peer-reviewed publications, scaled to cover more than 20 million lives. That's the perimeter a security team is defending.
The company, founded in 2016 and valued at $3.3 billion after its Series E round, sells to employers and health plans. Over 450 companies rely on the platform, which claims members recover 8 weeks faster than traditional approaches. The core product, Precision Mental Healthcare, ingests individual data and runs it through clinical ML pipelines to route people to appropriate interventions. From a security standpoint, this means PHI in transit across jurisdictions, ML model integrity, and authentication across a multi-language, multi-region deployment spanning 20+ languages.
Technical domains include machine learning, clinical care navigation, and mental health technology. The regulatory and privacy stakes are layered - employer-based healthcare data, clinical outcomes, algorithmic matching - all requiring rigorous access controls, encryption, and compliance posture. Security work here isn't peripheral; it sits directly between the data and the people who depend on it.
