How TCS is Tackling AI Security Risks in Healthcare

The global market for wearable healthcare devices is experiencing rapid growth, with analysts projecting its value to reach US$69.2bn by 2028. These devices, ranging from smartwatches to implantable sensors, are revolutionising patient care by providing continuous health monitoring and early detection of medical issues.

However, the proliferation of these devices brings significant cybersecurity challenges. Each wearable acts as a potential entry point for malicious actors seeking to access sensitive patient data or disrupt healthcare systems. A successful attack could compromise not only patient privacy but also the integrity of medical decisions based on the data collected.

Recent incidents have highlighted these risks. After a ransomware attack on US health insurance giant UnitedHealth Group earlier in 2024, a US federal committee heard that a third of US citizens might have had personal data exposed on the dark web.

The stakes are particularly high as healthcare providers increasingly integrate artificial intelligence (AI) and quantum computing into their systems. These technologies offer unprecedented opportunities for improving patient outcomes but also introduce new vulnerabilities that traditional security approaches may struggle to address.

In response to these challenges, technology firms are developing innovative solutions to safeguard patient data while enabling the benefits of advanced healthcare technologies.

Tata Consultancy Services (TCS), an Indian multinational information technology services company, is at the forefront of this effort, developing new security measures for healthcare technologies as its clients adopt AI and quantum computing.

According to Nitin Kumar, VP of Healthcare at Tata Consultancy Services: “Wearable devices enable proactive management and timely interventions. These devices collect vast amounts of sensitive personal data, including heart rate, sleep patterns, location and blood sugar levels. Since this data is transmitted wirelessly, it becomes vulnerable to interception and manipulation.”

As Nitin told Healthcare Digital, TCS is addressing these vulnerabilities through the use of TinyML, a technology that enables data processing directly on devices. This approach reduces the need to transmit sensitive information to external servers.

“By enabling wearables and remote monitoring systems to process data directly on the device, we reduce the need to send sensitive information to external servers. The data collected for analysis is encrypted and managed within a secure zone of the device, accessible only by authorised entities through stringent access control mechanisms,” he explains.

Federated learning enhances data security

The company is also experimenting with federated learning, a machine learning technique that trains algorithms on decentralised data. This method allows healthcare providers to utilise real-world data from devices for predictive and preventive care without compromising data security.

Nitin adds: “We are experimenting with federated learning technologies that ensure data owners have complete control of the data. The resulting AI models are utilising multiple local and secured datasets instead of having to move the data to centralised location thereby enhancing the security aspects of sensitive data.”

Quantum-resistant algorithms in development

As AI adoption increases in healthcare, so does the potential attack surface for cybercriminals. TCS is researching quantum-resistant algorithms and encryption techniques to protect against future threats.

“Among our customers, we see an increased adoption of AI and in any AI initiative, there is a vast amount of sensitive data that is required,” Nitin states. “These include patient records, medical images, genomic information besides others. To effectively safeguard this data, our customers are keen on exploring innovative approaches such as homomorphic encryptions.”

Homomorphic encryption is a form of encryption that allows computations to be performed on encrypted data without decrypting it first.

TCS is focusing on balancing security, privacy, latency, usability and accuracy in its AI systems for healthcare. As Nitin concludes: “Secure and Privacy by design for AI systems for healthcare is the mantra with a fine balance between security vs privacy vs latency vs usability vs accuracy.”

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