Securing Healthcare with AI – Case Study

Traditional Digital Healthcare stack is being disrupted with the introduction of AI in diagnostics and data analytics platforms 📊. Medical professionals are unknowingly exposing patient data through insecure symptom summarization tools and AI based workflows ⚠️. We analyzed the tech stack at a large medical research company with over $200B in market capitalization, based in the San Francisco Bay Area 🌉. In this case study, we demonstrate how easy it is to expose and modify critical patient data and detail how Pervaziv AI can help thwart these issues 🔐. We are also releasing our comprehensive 13 page business white paper that can be downloaded below 📘.

Core Challenges 🚨

Software used in the healthcare industry is based on legacy programming languages that are prone to vulnerabilities
Several security blind spots exist in the interoperability of software layers.
Data integrity and trust gaps exist due to a dearth of software testing, compliance and regulations.
Lack of comprehensive Software Component Analysis results in the inability to detect issues early.
In addition, healthcare professionals are increasingly using AI in conjunction with their traditional software stack.

Healthcare Technology Stack 🏥

Information flow in this healthcare company spans disease prevention, diagnostics, confirmation, cure and management. Patient data and diagnostic information traverses several layers of legacy software provided by numerous vendors with varied security protocols.

The endpoint systems involve various sensors, devices and instruments running legacy C/C++ software and low level firmware 🧬. The Laboratory Information Management System (LIMS) and Electronic Medical Records (EMRs) use various web applications that rely on insecure Javascript code ⚠️. Health Analytics platforms use Hadoop for data storage, Spark, and Pytorch for data analysis and summarization 📉.

Pervaziv AI Solution 🤖

Pervaziv AI provides end-to-end visibility and proactive vulnerability detection across the software supply chain — empowering healthcare organizations to move fast, innovate responsibly, and remain compliant with stringent data protection mandates ✅.

During this study, we explored the critical importance of securing complex, interconnected environments like those found in modern healthcare technology stacks 🧠🔗. Our platform combines AI-driven scanning, vulnerability detection and automated remediation to protect software systems at every layer — from embedded device firmware to cloud-based analytics pipelines ☁️.

Using a combination of Software Composition Analysis (SCA) and Software Bill of Materials (SBOM) generation, Pervaziv AI identifies vulnerabilities in third-party libraries and dependencies while also scanning proprietary source code for logic flaws, misconfigurations and hardcoded secrets 🔍.In conclusion, Pervaziv AI provides a powerful, AI-first platform purpose-built to address these challenges across the entire software supply chain 🔄. We welcome you to download the detailed white paper 📘 to learn more or write to us to view the demo.

Thanking the team who were part of the effort: Rajesh Raina, Pallak Srivastava, Fivos Allagiotis, Shreya Srirama, Ashwini Managuli. We also presented this demo to our advisory team: Adam Paulisick, Mahadev Satyanarayanan and Gopal Hegde earlier this summer. More exciting explorations and future releases are in the making with the help of a larger team at Pervaziv AI – Anirudh Joshi, David Gaviria, Bhavesh Bhatia, Shrawani SJ Pagar, Darshil Kikani, Arju Singh and Atharva Date.