Best Practices for Generative AI Risk Management and Prevention
Generative AI holds great promise and many enterprises are under pressure to build applications that can give them a competitive edge. However, obstacles to its widespread adoption exist today - namely a lack of confidence in the third-party large language models (LLMs) and the security, ethical, and operational risks they present.
Executives recognize that a new paradigm is needed to mitigate AI risk but aren’t well equipped to navigate this new world of generative AI without stifling innovation. In this webinar, representatives from 451 Research (S&P Global), Robust Intelligence, and Databricks share best practices and practical lessons for managing and preventing AI risk.
Nick Patience
Co-founder & Managing Analyst, Data, AI, InfoSec & Risk
451 Research, S&P Global Market Intelligence
Nitin Wagh
Lead ML Product Specialist
Databricks
Kojin Oshiba
Co-founder
Robust Intelligence
Generative AI holds great promise and many enterprises are under pressure to build applications that can give them a competitive edge. However, obstacles to its widespread adoption exist today – namely a lack of confidence in the third-party large language models (LLMs) and the security, ethical, and operational risks they present.
Executives recognize that a new paradigm is needed to mitigate AI risk but aren’t well equipped to navigate this new world of generative AI without stifling innovation. In this webinar, representatives from 451 Research (S&P Global), Robust Intelligence, and Databricks share best practices and practical lessons for managing and preventing AI risk.
Nick Patience
Co-founder & Managing Analyst, Data, AI, InfoSec & Risk
451 Research, S&P Global Market Intelligence
Nitin Wagh
Lead ML Product Specialist
Databricks
Kojin Oshiba
Co-founder
Robust Intelligence
