Guests:

Topics:

"10X SOC" sounds great. But for an organization stuck in "SIEM 1.0" with poor data quality and manual workflows, is "AI-native MDR" a "leapfrog" opportunity or a recipe for disaster?
We've seen the rise of "Decoupled SIEM" and security data lakes. Does a "Modern SIEM" even need to exist if an MDR platform has an agentic layer doing the heavy lifting?
You've argued for AI-native over AI-bolted-on. For an end user, what are the tangible differences of using "AI inside a legacy SIEM" versus using an "AI-native separate product"?
What is the one task you thought AI would handle by now that still requires a senior human analyst to step in?
If a CISO is using an AI MDR, "Mean Time to Detect" (MTTD) starts to look like a vanity metric because the machine is instant. What is the new golden metric for an AI-powered SOC? Is it "Time to Context," "Reduction in Human Toil," or something else?
How do you help a skeptical SOC Manager—who has been burned by false positives for a decade—trust an autonomous agent to perform a "containment" action at 3:00 AM?

Resources:

EP271 Can AI-Native MDR Actually Fix Your Broken SOC Workflows or Just Automate the Mess?

Cloud Security Podcast by Google