Hemant Seth's Blog Posts

Hemant Seth

Hemant is a Principal Product Manager at Splunk, leading the Kubernetes Monitoring offering within Splunk Observability Cloud. Prior to this role, he focused on Splunk Observability Platform administration, including identity management and license usage. Hemant brings over a decade of experience in the observability domain and holds a Master’s degree in Electrical Engineering with a specialization in Telecommunications.

Threat Actors: Common Types & Best Defenses Against Them
Learn
5 Minute Read

Threat Actors: Common Types & Best Defenses Against Them

Learn about threat actors, the person, persons, or entities responsible for causing cybersecurity incident or more generally posing a risk.
Sequenced Event Templates via Risk-based Alerting
Security
3 Minute Read

Sequenced Event Templates via Risk-based Alerting

Splunker Haylee Mills explains how to convert sequenced events into actionable insights using SPL techniques to enhance anomaly detection and improve security analytics.
Cardinality Metrics for Monitoring and Observability: Why High Cardinality is Important
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4 Minute Read

Cardinality Metrics for Monitoring and Observability: Why High Cardinality is Important

In this blog post we’ll define cardinality and high cardinality, and explore the role of cardinality in monitoring and observability.
What Is Cyber Hygiene? Introduction, Best Practices, & Next Steps for Organizations
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8 Minute Read

What Is Cyber Hygiene? Introduction, Best Practices, & Next Steps for Organizations

One approach to a cybersecurity strategy, cyber hygiene is the way of creating a structured, intelligent environment that reduces the risk of contamination.
SOAR: Transforming Security and IT
Security
2 Minute Read

SOAR: Transforming Security and IT

Splunker Kassandra Murphy explains how to streamline workflows and boost efficiency across your organization with intelligent orchestration and automation.
Model Drift: What It Is & How To Avoid Drift in AI/ML Models
Learn
5 Minute Read

Model Drift: What It Is & How To Avoid Drift in AI/ML Models

Model drift is the term for an AI or ML model’s tendency to lose its predictive ability over time. The model has drifted away from its original goal or purpose.