Implementing Gen AI in Highly Regulated Environments

If 2023 was the year of gen AI experimentation, 2024 is the year of gen AI implementation.

But implementing gen AI in the enterprise comes with a host of challenges, which can be broadly categorized into two areas:

• Gen AI Ops (operations) – Getting gen AI from pilot to production, ensuring accuracy of your LLMs for your unique industry and use case, optimizing performance while minimizing cost, GPU provisioning, etc.
• De-risking Gen AI – Providing essential monitoring, governance, data privacy and compliance measures to ensure your gen AI apps perform as expected and don’t introduce unnecessary risk into the organization.

For highly regulated industries such as Financial Services and Telecommunications, implementing gen AI in practice comes with an additional array of unique challenges.

These include:

• Working with highly sensitive data
• Deploying gen AI in on-prem environments
• Adhering to strict regulations
• The need to use a combination of gen AI with traditional machine and deep learning techniques.

In this session, we discussed the unique challenges of implementing gen AI in highly regulated environments, as well as some innovative ways to mitigate them. We shared our approach for building a ‘Gen AI Factory’ that enables users to scale their gen AI initiatives responsibly across the enterprise. As always, we showed real-life examples from the Financial Services and Telecommunications industries and got to answer some of your questions live!