Healthcare data is highly sensitive, and any breach of privacy can have severe consequences for patients. It can lead to identity theft, medical fraud, and even harm to patients if their medical records are altered or deleted. Unlike a password, a medical record cannot be reset once it is stolen. The numbers from the past two years speak for themselves: 2025 set a new annual record with 772 large healthcare data breaches reported in the United States alone, while the 2024 ransomware attack on Change Healthcare exposed the data of an estimated 192.7 million individuals, which makes the largest healthcare data breach of all time. Healthcare breaches now cost an average of $7.42 million per incident, the costliest of any industry. Healthcare organizations need to take data security seriously and implement robust security measures to protect patient data.
In the US, the proposed update to the HIPAA Security Rule would make encryption and multi-factor authentication mandatory rather than optional, “addressable” safeguards. A final rule was given in May 2026 and is now overdue, but the direction of travel is clear: these protections are becoming mandatory.
AI is the reason this can no longer wait
AI startups and researchers at the world’s leading universities are seeing significant advancements in early disease detection using AI and ML. For such research to be effective, access to patients’ data is crucial. In January 2026, The Lancet published the final results of the MASAI trial, the first randomized controlled trial of AI in breast cancer screening, involving over 105,000 women in Sweden.
AI-supported mammography detected 29% more cancers, reduced interval cancers by 12%, and cut radiologists’ screen-reading workload by 44% without increasing false positives. Predictive models based on AI and ML can also be used to identify individuals who are at high risk of developing certain diseases. By analyzing multiple factors such as age, genetics, lifestyle, and environmental factors, these models can identify individuals who are most likely to develop a particular disease. This allows healthcare professionals to intervene early and provide personalized preventive care.
AI and ML can also improve data security itself. These technologies can help healthcare organizations identify and respond to potential security threats in real time by analyzing user behavior and detecting suspicious activity, such as unauthorized access attempts, abnormal data transfers, or unusual data access patterns. They can also automate security management tasks, such as monitoring access logs, tracking changes in user permissions, and enforcing data retention policies, reducing the risk of human error.
However, each breakthrough requires processing patient data. Encryption protects data at rest. Secure protocols protect data in transit. Neither protects data at the moment it is actually being used, when it is decrypted in memory to train a model or run a prediction. That is exactly where the most valuable data now lives.
Confidential computing: protecting the data in use
The only viable method of such protection now is the use of trusted execution environments (TEEs) based on confidential computing technology. You are protecting your data during processing by isolating the part of the CPU where the processing takes place. This is exactly what a TEE (often called an enclave) does. The problem is that, to provide such a level of data security, even the operating system cannot access the TEE.
An example would be a university research group working on early disease-detection AI that needs access to hospital patient data to train and test its models. With a trusted execution environment deployed in the cloud, such collaboration becomes possible while ensuring full data privacy. TEEs can also be used within the hospital’s network to ensure the data never leaves the premises.
When I first wrote about this technology, the most famous example was Intel SGX, and it was new and difficult to use. Three years later, the landscape has matured dramatically. Intel SGX has largely given way to VM-level technologies like Intel TDX and AMD SEV-SNP, which let organizations run entire virtual machines. Gartner named confidential computing a Top 10 Strategic Technology Trend for 2026 and predicts that 75% of workloads in untrusted infrastructure will be secured by it by 2029. The Confidential Computing Consortium reports growing adoption across AI, finance, and healthcare, and market forecasts put annual growth above 25% at the most conservative estimate.
Confidential computing offers numerous benefits, including enhanced data privacy and security. With confidential computing, sensitive data is protected from unauthorized access even during processing, reducing the risk of data breaches. It also allows users to determine who has access to their data and how it is processed. Additionally, confidential computing can facilitate secure data sharing between organizations, enabling collaboration without compromising data privacy. This means hospitals can now collaborate on AI models without exposing patient data, with only model updates shared between institutions.
Health tech has the potential to improve healthcare by making it more efficient and accessible. The MASAI trial has shown that AI can already save lives at scale. However, the security of sensitive patient data remains a top concern for healthcare providers, policymakers, and patients, and the record-breaking breaches of the past two years show why. Encryption technology, two-factor authentication, and access controls have solved the problems of data at rest and in transit. However, to derive greater value from the application of AI and ML in healthcare, it is necessary to effectively protect data in use. By investing in secure data-sharing protocols, cloud-based platforms, and especially confidential computing, healthcare organizations can ensure that patient data remains confidential and protected from cyber threats. That’s where we build the next generation of life-saving AI without sacrificing the privacy of the patients it is meant to serve.
* Tetiana Rak is the Chief Operations Officer (COO) at We Are Innovation. A journalist and freedom activist with 8 years of experience, Tania has worked with renowned media outlets including CNN, TechCrunch, Fox News, HackerNoon, the BBC, and Radio Free Europe, among others. Her unwavering dedication to championing the ideas of technological advancements and global digital transformations has earned her a distinguished reputation in the field. Through her work, Tania promotes the ideas of liberty and individual rights as a cornerstone of any rights-respecting society. Strengthened by the experience of war in Ukraine, Tania’s beliefs also stand for promoting technological advancements as a transformative tool to advance liberty, giving people the opportunity to speak, act, and pursue happiness without unnecessary external restrictions.
Source: We Are Innovation
