In this video, Oliver Knapp, Certified Splunk Architect and Enterprise Security Admin at Somerford Associates, continues the series on machine learning in Splunk for security by focusing on how to develop your own machine learning use cases.
Building on the concepts introduced in earlier episodes, this session explores how organisations can take existing ideas and apply them to their own environments using the Splunk Machine Learning Toolkit (MLTK) within Splunk Enterprise Security (ES).
Oliver walks through the full use case development lifecycle, including identifying opportunities for machine learning, planning data and alerting strategies, selecting appropriate models, and testing and training them effectively. The video also highlights the importance of reducing false positives, validating performance before production, and continuously refining use cases to maximise value.
This session is ideal for security teams and Splunk users looking to move from theory to practice by designing, testing, and implementing custom machine learning-driven security use cases.
📊 Contact Somerford to learn more:
https://www.somerfordassociates.com/contact-us/
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