Python and AI Best Practices (1 CPE)

This is an archived session from 7-15-26, on Python and AI Best Practices.

Python and AI projects are becoming increasingly common across audit, analytics, and business environments, creating new opportunities as well as new risks. This session with Claire Worledge, will explore practical development standards and governance practices that help improve the reliability, security, maintainability, and auditability of Python and AI solutions. Participants will learn recommended approaches for project structure, naming conventions, configuration management, dependency documentation, and protection of sensitive information, while also gaining insight into key risks auditors should evaluate when reviewing Python and AI initiatives. By understanding these best practices, attendees will be better prepared to assess the quality and governance of AI-enabled projects within their organizations.

Learning Objectives:

• Identify leading practices for developing, documenting, and maintaining Python and AI projects.
• Apply recommended naming conventions for Python and AI solutions.
• Evaluate key governance, security, and operational risks associated with Python and AI projects, including hard-coded values, dependency management, and protection of sensitive credentials.

Duration: 1 hour (approximate)
CPEs Available: 1
Knowledge Level: Intermediate
Category: Data Analytics