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Healthcare and AI Security Use Case

Despite the fact that the healthcare industry generates approximately 30% of global data, healthcare providers continue to struggle with data security. The US Department of Health and Human Services reported data breaches affecting more than 40 million people in 2021, with over 3.7 million people affected in the first two months of 2022. All estimates predict that data breaches in healthcare will continue to rise in the near future. What needs to be addressed are the following issues for healthcare and AI:
  • Data privacy challenges while implementing AI applications
  • Sharing medical data
  • Protecting cross-border data transfers of personal data
  • Healthcare regulations and compliance (HIPAA, GDPR)
  • Data breaches

The Current Status

Multiple hospitals, for example, may need to share MRI data with research institutions. In this case, a "man in the middle" (the hacker) sits between the hospital and the research center, waiting for that data to appear and breach it at the appropriate time.

The Problems

The use of AI in healthcare is rapidly expanding for the use of medical devices and other technologies. Healthcare is becoming more automated in order to improve efficiency (for both physicians and medical facilities), as medical applications commonly use AI as a diagnostic or treatment advisor to medical practitioners. However, combining healthcare and AI can be a double-edged sword: the more data you need to be accurate, the more vulnerable you are. For cybercriminals, this is as simple as it gets. If there is a data breach, surgeons will be unable to accurately predict MRI results, and patients will suffer greatly.

The Solution

The solution HUB Security offers is the Secure Compute Platform, built to secure health data in AI-driven applications across healthcare, so doctors can make faster and more accurate diagnoses. HUB Security utilizes a new security paradigm centered on confidential computing to create a secure enclave for AI models and data, that brings a competitive advantage to healthcare providers working with machine learning and AI. By providing secure, isolated environments to protect the integrity and privacy of the AI models and data, this approach also allows multi-party analytics and collaboration. Protect medical data and applications with the Secure Compute Platform