DBT: Lead The Change- Privacy-Safe Data Sharing to Enable AI Use Cases
By LS DigitalSeptember 17, 2024
Attendees
- Puneet Bhardwaj : Group CDO, Zurich Insurance
- Chetan Trivedi : CIO, Vedanta Resources Plc
- Gowthaman Ragothaman: Founding CEO, Aqilliz
- Punam Shejale : CISO and Head Risk & Compliance, Citius Tech
- Bhagvan Kommadi : CIO, Capri Global Capital Ltd
Prasad Shejale: Founder & CEO, LS Digital Group (Moderator).
Executive Summary:
As AI continues to shape the future of business, the quality of data is becoming increasingly vital for leveraging AI use cases that provide a significant competitive advantage. However, the challenges of sharing data within an organization or with partners—particularly around compliance and privacy—are growing more complex. This DBT: Lead the Change session brought together CEO, CDOs, CTOs, CIOs, and CISOs to discuss these challenges and explore the opportunities they present.
Key topics explored:
- Data as a Strategic Asset: How data sharing can drive innovation, decision-making, and enhance customer experiences.
- Privacy Regulations: Navigating complex privacy laws like DPDP, GDPR and CCPA.
- Data Masking and Anonymization: Protecting sensitive information while sharing data.
- Federated Learning: Collaborating on AI models without sharing raw data.
- Data Trusts and Blockchain: Exploring frameworks and technologies for secure, ethical data sharing.
- Data Governance and Management: Ensuring privacy and security throughout the process.
Challenges and Considerations:
- Building trust and transparency with data-sharing partners.
- Tackling technical challenges and ensuring regulatory compliance.
- Managing data quality and consistency.
Excerpts
Unlocking AI’s Potential Through Privacy-Safe Data Sharing
In an era dominated by data, Artificial Intelligence (AI) has emerged as a powerful tool driving innovation across multiple industries. From healthcare to manufacturing, FinTech to insurance, AI is reshaping how businesses operate, make decisions, and engage with customers. However, the true potential of AI can only be realized if data sharing is both secure and privacy-compliant. The recent DBT: Lead the Change session, moderated by Prasad Shejale, Founder and CEO of LS Digital, brought together industry leaders to address a critical question: “How can organizations unlock AI’s full potential while ensuring privacy-safe data sharing?”
The panel included prominent voices from sectors that rely heavily on data, including Chetan Trivedi from the manufacturing industry, Puneet Bhardwaj from insurance, Bhagvan Kommadi representing FinTech, Punam Shejale representing MedTech and life sciences, and Gowthaman Ragothaman from advertising. Together, they shared valuable insights into how businesses can balance the need for seamless data collaboration with stringent privacy regulations, such as GDPR in Europe and DPDP in India.
The Complexities of Data Sharing in a Privacy-First World
The session kicked off with a discussion about the current landscape of data sharing across industries. For many organizations, the rapid growth of AI has introduced new challenges, especially around privacy and data governance. Chetan Trivedi shed light on this transformation, explaining how businesses have moved from largely uncontrolled data activities to more structured frameworks driven by regulatory pressures. “Data sharing was largely uncontrolled in the past, but today, we have frameworks in place to safeguard sensitive information,” Chetan Trivedi explained, emphasizing that the shift towards data governance was inevitable as the digital landscape evolved. In industries like manufacturing, data is the lifeblood of operational efficiency, safety compliance, and sustainability efforts, making privacy-safe sharing a top priority.
“With a growing focus on data governance, especially with regulations like DPDP, we now rely on virtual data rooms for safe external collaboration,” Chetan Trivedi
In the insurance sector, Puneet Bhardwaj added a new dimension to the conversation by discussing the complexities of sharing data across business units and global teams. He explained that many insurance companies, especially those operating internationally, often face challenges due to disconnected data systems. “We didn’t realize the high concentration of risk until it was too late. Now, sharing data between our global units is critical to mitigating such risks,” Puneet Bhardwaj stated, stressing the need for comprehensive data-sharing frameworks. His insights pointed to a growing trend: AI can only thrive if businesses can seamlessly share data across borders, ensuring that risk management becomes more proactive and data-driven. “With a growing focus on data governance, especially with regulations like DPDP, we now rely on virtual data rooms for safe external collaboration,” Chetan Trivedi, highlighting a crucial point: As regulatory frameworks become more complex, businesses must adapt quickly to avoid compliance issues while maintaining the flexibility to share valuable data.
Data Governance and Security: The Backbone of AI Use Cases
Data governance and security emerged as a central theme of the session, with all panellists agreeing that these elements are the foundation of successful AI use cases. The importance of consent management and legal frameworks was stressed by Bhagvan Kommadi, who outlined how technologies such as homomorphic encryption and differential privacy are becoming critical for enabling secure data sharing. “Consent management is the first step, followed by encryption techniques like fully homomorphic encryption, allowing operations on encrypted data without decryption,” Bhagvan Kommadi emphasized, offering a glimpse into how advanced encryption technologies can protect data while still enabling valuable analytics.
In FinTech, where trust and security are paramount, these technologies are being adopted at a rapid pace. Bhagvan Kommadi further elaborated on how encryption allows financial institutions to collaborate on data while maintaining privacy, which is crucial for industries that handle sensitive consumer information.
“Consent management is the first step, followed by encryption techniques like fully homomorphic encryption, allowing operations on encrypted data without decryption,” Bhagvan
In the healthcare sector, Poonam Shejale highlighted the added layer of complexity introduced by regulatory frameworks such as HIPAA. For healthcare providers, the challenge is not just about sharing data, but doing so in a way that complies with stringent regulations while ensuring patient privacy.
“I believe just to understand where all the data resides and how many different variants of it exist, in which all systems, along with that putting a data governance and risk framework around it- that should be the first priority for everyone”. Punam Shejale
“Interoperability is essential, but we face challenges in building secure data-sharing pipelines that comply with healthcare regulations,” noted Prasad Shejale, stressing the need for secure pipelines that allow data to flow between payers, providers, and life sciences companies without breaching privacy standards.
Another key insight from Bhagvan Kommadi focused on the practical applications of differential privacy, which adds statistical noise to data, ensuring individual privacy while still allowing organizations to extract useful insights.
“Privacy is crucial, and we have introduced differential privacy techniques that add statistical noise to customer data, ensuring individual privacy,” Bhagvan Kommadi said. This technique is particularly useful in industries such as finance and healthcare, where the need for privacy is balanced with the demand for accurate, actionable insights.
The Role of Technology in Privacy-Safe Data Sharing
The conversation then turned towards the role of emerging technologies in enabling privacy-safe data sharing. Gowthaman Ragothaman brought a unique perspective from the advertising sector, discussing how the industry has long leveraged user data without explicit consent, but now faces stricter regulations such as GDPR, DPDP, and CCPA. “The world is moving towards putting consent at the centre of the business,” Gowthaman noted, adding that differential privacy and federated learning are becoming essential tools for balancing privacy with business needs. Federated learning, in particular, allows businesses to aggregate insights across different data sets without directly sharing sensitive information. “Federated learning allows insights to be aggregated without sharing sensitive data directly, maintaining both privacy and analytical value,” Gowthaman explained, providing a clear example of how technology is evolving to meet regulatory demands while still enabling meaningful data analysis.
“Federated learning allows insights to be aggregated without sharing sensitive data directly, maintaining both privacy and analytical value,” Gowthaman
Blockchain technology and shared ledgers also surfaced as potential solutions for privacy-safe data sharing. Gowthaman mentioned that blockchain could offer transparent, concurrent data-sharing frameworks across enterprises, ensuring that trust and compliance are built into every transaction. “Blockchain offers a solution for concurrent data sharing across multiple enterprises, ensuring trust and compliance with a shared ledger,” he said, reflecting on the potential of distributed ledger technologies to revolutionize data-sharing practices across industries.
AI Use Cases Powered by Privacy-Safe Data Sharing
The panellists also discussed several real-world AI use cases that have been enabled by effective, privacy-compliant data sharing. Chetan Trivedi shared how his company is using AI-driven models to improve safety compliance and optimize metal recovery in manufacturing processes. “AI-driven models are helping us align real-time data with our golden batch standards, improving both safety and efficiency,” he elaborated, offering a glimpse into how AI is transforming operational processes in the manufacturing sector. By comparing real-time data with historical benchmarks, AI can identify inefficiencies and potential safety risks, allowing companies to address them before they become critical issues.
“AI-driven models are helping us align real-time data with our golden batch standards, improving both safety and efficiency,” Chetan
In the insurance industry, Puneet Bhardwaj highlighted the transformative role AI plays in risk engineering. By enabling real-time data sharing across IoT systems, AI helps companies predict and prevent losses rather than simply processing claims. “Our AI-driven risk engineering services help predict and prevent losses, shifting insurance from a reactive to a proactive approach,” Puneet Bhardwaj remarked, illustrating how data sharing can drive innovation in risk management and insurance processes.
The Future of Privacy-Safe Data Sharing
Looking towards the future, the panellists agreed that the road ahead will be paved with challenges, particularly around data silos and the lack of unified data-sharing frameworks. Bhagvan Kommadi noted that real-time analytics and AI-driven models will become more prominent, unlocking personalized experiences and enhancing fraud detection capabilities in sectors such as FinTech. “The next frontier is quantum AI, where privacy-preserving technologies like differential privacy and homomorphic encryption will reach new heights,” predicted Bhagwan Kommadi, suggesting that as AI continues to evolve, so too will the technologies that enable secure data sharing.
“The next frontier is quantum AI, where privacy-preserving technologies like differential privacy and homomorphic encryption will reach new heights,” Bhagvan
Concluding the session, Prasad Shejale summed up the collective vision for the future: “The future of data sharing will be defined by how well we balance the need for data access with privacy and security. AI will only be as good as the data we can share.”
The panellists agreed that while the challenges are significant, the rewards are even greater. By focusing on advanced technologies such as blockchain, homomorphic encryption, and federated learning, organizations can unlock the true potential of AI while maintaining privacy and compliance.
As businesses continue to integrate AI into their operations, privacy-safe data sharing will remain a cornerstone of innovation. “The data is being created for primarily human consumption today, but tomorrow it will be created for machines. What changes do we need to make to move towards that AI-driven future?” concluded Puneet Bhardwaj, hinting at the fundamental shifts needed to fully embrace the AI revolution.
About DBT: Lead The Change
In today’s fast-paced digital world, staying ahead of the curve is not just a competitive advantage—it is a necessity. The “DBT: Lead The Change” series, a fortnightly discussion session moderated by Prasad Shejale, Founder and CEO of LS Digital, is a novel initiative designed to equip industry professionals with the insights and strategies needed to thrive in this ever-evolving landscape. As a by-invite, closed-door virtual session, it brings together a curated group of industry leaders and thought leaders to tackle the most pressing business challenges head-on.
This property, conceived and produced by LS Digital, serves as more than just a platform for dialogue; it is a catalyst for innovation. Every two weeks, diverse perspectives converge to address critical areas such as Media, Creative Communications, Data Insights, Technology & Innovation, User Experience (UX), and Customer Experience (CX). By fostering meaningful dialogue among senior professionals from these key domains, “Lead The Change” aims to uncover actionable solutions that can drive significant impact.
As we navigate the complexities of modern business, this platform serves as a beacon for thought leadership, guiding participants through emerging trends and equipping them with the tools to lead the digital business transformation. The discussions held here are not just theoretical— They shape the roadmap for the future of digital business, crafted by those at the forefront of the industry.