Stream: Virtual Room A
Time: 16:15 - 17:00
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.
Henri Kuiper is a pre Y2K Mainframe junkie. Started taking apart computers at a very young age (BBC Micro's Anyone?) and never stopped his love for 'taking things apart' (and mostly putting them back together too). He's an 8 year consecutive IBM Champion for z Systems, owner of zDevOps.com pro-deo teacher at 'codeuur.nl' (teaching primary school kids how to program), co-founder and CTO at Mainframe Society and loves all things IT :)
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