12-05, 13:30–14:00 (UTC), LLM Track
As large language models (LLMs) become increasingly integrated into industries like finance, healthcare, and law, ensuring their responsible deployment is critical—particularly in highly regulated environments. These industries face unique challenges, including data privacy, compliance with strict regulations, and minimizing the risks of biased or untrustworthy outputs.
This session will explore the complexities of using LLMs in regulated industries and present a governance framework to address these challenges. We'll cover practical solutions for deploying LLMs while adhering to industry-specific regulations, ensuring transparency, reducing bias, and maintaining data privacy. Attendees will learn how to implement governance best practices at various stages of the LLM lifecycle—from model training and validation to deployment and ongoing monitoring.
Drawing on real-world examples and lessons learned, this talk will equip data scientists, machine learning engineers, and AI leaders with actionable strategies for navigating regulatory compliance and minimizing risks, while still harnessing the full potential of LLMs to drive innovation.
As large language models (LLMs) gain traction across industries, their adoption in regulated sectors like finance, healthcare, and law presents unique challenges. These industries operate under stringent compliance frameworks that demand careful handling of sensitive data, accountability in decision-making, and adherence to industry-specific standards. The need to ensure trust, transparency, and fairness becomes even more critical when using AI systems that can generate outputs at scale.
This session delves into the specific hurdles faced by regulated industries when integrating LLMs, such as:
Compliance with Data Privacy Regulations: Handling personal data while adhering to standards like GDPR, HIPAA, or financial regulations.
Bias and Fairness: Mitigating bias in LLM outputs to prevent discrimination, especially in sensitive areas like hiring, lending, or medical advice.
Auditability and Transparency: Ensuring that LLMs' decision-making processes are transparent and auditable for regulatory scrutiny.
Ongoing Risk Management: Developing strategies for continuous monitoring and risk mitigation as LLMs evolve over time.
Through real-world case studies, the session will offer a practical governance framework for mitigating these risks. We'll explore how businesses can implement robust AI governance practices, from the initial stages of model design and training to deployment and post-deployment monitoring, ensuring that LLMs operate within the confines of legal and ethical guidelines.
Attendees will walk away with:
A deeper understanding of the unique challenges of using LLMs in regulated environments.
A toolkit of governance best practices to manage AI risks and ensure compliance.
Practical examples of how LLM governance can enhance business outcomes without compromising regulatory standards.
Whether you're a data scientist, AI engineer, or business leader, this talk will provide the insights and frameworks needed to safely deploy LLMs in industries where regulations matter most.
No previous knowledge expected
Vyoma Gajjar is an AI Technical Solution Architect with over a decade of experience in AI governance, generative AI, and machine learning. She has worked extensively on developing scalable AI solutions and governance frameworks for global industries, focusing on highly regulated sectors like finance and healthcare. Vyoma is passionate about ethical AI practices and responsible innovation, frequently speaking at major conferences and serving as a mentor to aspiring AI professionals. She holds a patent in AI and actively contributes to shaping the future of trustworthy AI technologies.