Detection Engineering That Scales: Practical Strategies for Resilient, Maintainable Security Operations

Modern threat detection teams face a critical challenge: developing, implementing, and maintaining effective detection rules efficiently and strategically amid a rapidly evolving threat landscape and resource limitations.

Meeting this challenge requires strong collaboration across analysts, engineers, and intelligence teams; smarter use of contextual data to refine decision-making; and structured, code-driven approaches that bring consistency and automation to detection workflows. By combining threat-informed design, continuous validation, and risk-based alerting, teams can reduce noise, enhance visibility, and ensure detections remain relevant as their environments evolve.

Watch the on-demand webinar as SANS Senior Instructor Erik Van Buggenhout, Splunk’s Director of Product Management Tim Nary, and NVISO Detection Engineering SME Stamatis Chatzimangou explore the strategies behind effective detection engineering.

This session also highlights where the field still struggles: detection maintenance, performance analytics, and risk-based alerting. A focus area will be how emerging technologies like AI can assist practitioners without replacing their expertise. The outcome: a clear, practical understanding of how to evolve detection operations from reactive and fragmented to proactive, measurable, and sustainable.

In this session, you’ll gain practical insight into how security teams are approaching detection engineering at scale, including how to:

• Build and maintain high-quality detections that remain effective as environments, data volumes, and threat landscapes grow
• Apply structured, threat-informed practices—including frameworks like MITRE ATT&CK—to improve detection relevance and reduce alert noise
• Measure and evolve detection effectiveness over time, using performance signals to continuously refine and sustain your detection program

Watch the on-demand webinar to learn how security teams are building detection engineering practices that scale and stay effective over time.

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