Secure AI Labs

Secure AI Labs (SAIL) is a technology company with roots at MIT, established to address critical data privacy and accessibility challenges in healthcare research. Founded in 2017 by CEO Anne Kim, an MIT alumna, and Manolis Kellis, a professor at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), the company originated from Kim's graduate research on clinical trial data sharing and Kellis's extensive experience with the difficulties researchers face in accessing vital health data. The venture gained early traction through its participation in the delta v startup accelerator at the Martin Trust Center for MIT Entrepreneurship in 2018.

The company's core business revolves around providing a high-assurance federated learning platform that enables machine learning and analytics on sensitive health data without the data ever leaving its original location. This approach resolves the significant bottleneck of sharing proprietary or confidential information, such as patient records and clinical trial results, which often stalls crucial research due to security and compliance concerns. SAIL's platform utilizes secure hardware enclaves, which act as protected computing sandboxes, to run algorithms on encrypted data, ensuring that both the data and the intellectual property of the algorithm remain private. This patented technology allows for decentralized analysis across multiple data silos, effectively creating a unified patient registry from hundreds of sources.

SAIL primarily serves pharmaceutical companies, medical device manufacturers, academic medical centers, and patient advocacy groups. Its business model allows these clients to collaborate and extract insights from vast, combined datasets—including molecular, genomic, and clinical trial data—without compromising patient privacy or data ownership. This capability accelerates the discovery process for new treatments and diagnostics by making previously siloed data available for analysis. For instance, the platform helps overcome biases in clinical research by pooling data from diverse populations. In November 2022, the company announced a $4.7 million seed funding round led by Asset Management Ventures, bringing its total funding to $9 million, to expand its clinical data registry network.

Keywords: federated learning, data privacy, healthcare analytics, confidential computing, clinical research, life sciences, secure computation, machine learning, data security, patient data