Stream: Magny-Cours
Time: 15:15 - 16:00
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.
There is currently no attachment for Continuous Compliance: Security Automation with MFPandas
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 Technical Coordinator for the Dutch region of Guide Share Europe, IBM Champion for z Systems, owner of zDevOps.com pro-deo teacher at 'codeuur.nl' (teaching primary school kids how to program) and loves all things IT :)
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