RACF environments often contain thousands of users, groups, permits, and naming conventions. Over time, complexity grows — and so does the risk of configuration drift, audit findings, and undocumented exceptions.

What if RACF compliance checks could be automated, repeatable, and version-controlled?

In this session, the creator of MFPandas (formerly known as pyRACF) demonstrates how to transform RACF data into structured datasets using Python and pandas, enabling automated compliance validation against:

Internal naming standards
Segregation-of-duties rules
External audit requirements
Security baselines and conventions

By treating RACF data as analyzable dataframes, we can implement “Compliance as Code” principles on z/OS — bringing DevOps-style validation to mainframe security.

The session includes live demonstrations and practical implementation patterns that attendees can adapt directly within their own environments.

Audience level: Attendees are expected to have a working knowledge of Python, basic familiarity with pandas, and a general understanding of RACF concepts and terminology. This is not an introductory RACF or Python session.