Inside the Architecture of Trust: Somnath Banerjee on Securing Healthcare Data without Slowing Innovation

Healthcare companies across the United States are rapidly beginning to adopt technologies like cloud computing and AI to better manage data, automate manual tasks, and gain real-time insights into patient care. But because many of these tools rely on access to sensitive patient information protected by federal law, developers must work within strict privacy and compliance boundaries.

This creates a complicated challenge: how can healthcare organizations make meaningful use of their data without putting patient privacy at risk?

This is the question that data engineer Somnath Banerjee has spent years addressing. As a senior architect at one of the largest health insurance companies in the U.S., he has focused on developing intricate security frameworks that keep personal information private while giving internal teams access to the data they need.

Read on to learn how Banerjee is helping healthcare organizations use data responsibly without compromising patient trust.

How Somnath Banerjee Builds Safer Systems for Healthcare Data

Over the past two decades, Somnath Banerjee has been managing large-scale systems across IT and consulting, gradually shifting his focus toward healthcare data infrastructure. He now serves as a senior enterprise architect at a Fortune 25 U.S. health insurance company, where he oversees how data is organized, secured, and used throughout the organization.

A key part of this role involves designing frameworks that centralize patient data (such as medical records, diagnoses, and treatment histories) into a unified platform. These frameworks must also protect data classified as protected health information (PHI), which includes anything that can be used to identify an individual, such as names, addresses, phone numbers, and Social Security numbers.

Because PHI is governed by strict regulations like HIPAA, any system that handles it must meet strict legal and organizational privacy standards. Banerjee's work is indicative of a growing industry challenge, with recent reports showing the number of patient records exposed in PHI-related data breaches rose from 6 million in 2010 to over 170 million by 2024.

For Banerjee, that means his systems must ensure data is not only accurate and accessible for care teams, but also resilient against misuse and attack. "With healthcare data stored in petabyte-scale platforms, security and compliance are paramount," he explains.

Encrypting Health Information with a De-Identification Framework

One of Banerjee's most significant projects involved designing a privacy framework for a large-scale enterprise data lake that stores patient information, both structured (like health records, claims, and lab results) and unstructured (like clinical notes and scanned documents).

The project was complex. Banerjee needed to ensure that teams relying on patient data for tasks like testing, reporting, or tool development could access it without friction while also making sure that sensitive information, particularly PHI, remained fully protected.

"The challenge lay in balancing security with usability," Banerjee recalls. "How could we protect patient data while ensuring development and analytics teams had access to realistic datasets for testing?"

To achieve this, he led the development of a multi-layered de-identification framework that systematically removes or disguises personal identifiers at each stage of the data lifecycle. This included tokenization to replace names and IDs with neutral placeholders, data masking to substitute real details with realistic (but fictional) information for testing environments, and encryption protocols aligned with standards like HIPAA.

Banerjee also implemented automated tools that scan for privacy risks as data moves between systems, ensuring continuous oversight without manual supervision, and introduced validation tools to test de-identification outcomes and confirm that personal identifiers had been fully removed.

These protocols aimed to prevent sensitive information from circulating while preserving the structure and utility of the data, enabling teams to work with production-like datasets without risking unintended exposure.

Enabling Safer Development and Smarter Compliance

Now fully complete and in place, Banerjee's privacy framework has led to several improvements across the organization.

First, it gives teams access to high-quality, relevant data while protecting sensitive information. This made it possible to safely train and test AI tools like predictive machine learning models on datasets that closely reflect real-world conditions, speeding up development while staying fully compliant with privacy regulations.

The framework also strengthened internal audit controls by giving legal and security teams more reliable access to usage data, while its built-in safeguards automated much of the compliance tracking, greatly reducing the need for manual review and freeing teams to focus on higher-risk issues without losing visibility or control.

But beyond the technical gains, Banerjee sees his framework as a foundation for trust, both internally and across the organization's broader network of partners. "This initiative ensures that patient records remain protected while enabling advanced analytics and AI-driven healthcare insights," he says.

For Banerjee, safeguarding data is only part of the equation. Equally as critical is ensuring it remains usable in a responsible, controlled way: "As cybersecurity threats continue to evolve, my mission remains clear: building resilient, adaptable security models that protect patient data while driving innovation in healthcare technology."

Building the Future of Healthcare Security

Banerjee has been behind several initiatives that seek to advance healthcare infrastructure. He recently created a master data management (MDM) framework that consolidated data from across three disparate legacy systems and standardized it into a unified format, enabling a more complete view of patient records. Building on this work, he authored a paper exploring how MDM platforms can support the creation of unified patient profiles across healthcare systems.

Banerjee also stays active in the broader health tech community. He is a senior member of IEEE, a contributor to the Forbes Technology Council, and a mentor for programs like Startupbootcamp, Gener8tor, and ADPList, where he advises health founders on building secure, compliant systems. His contributions have earned him the Global Tech Award and the Stevie American Business Award.

Looking ahead, Banerjee aims to expand on his work in security and data infrastructure by helping develop AI systems that can continuously adapt to emerging risks. "By consolidating healthcare data and integrating AI-driven tools, I envision creating self-correcting systems capable of real-time decision-making, a transformative milestone for the industry," he concludes.

As digital systems become more central to care delivery, Somnath Banerjee's work offers a blueprint for how well-designed security frameworks can not only protect the data of millions but also accelerate progress, giving healthcare organizations the confidence to adopt new technologies and improve outcomes for physicians and patients alike.