Detection at Scale

About Detection at Scale

The Detection at Scale Podcast is dedicated to helping security practitioners and their teams succeed at managing and responding to threats at a modern, cloud scale. Hosted by Jack Naglieri, Founder and CTO at Panther, every episode is focused on actionable takeaways to help you get ahead of the curve and prepare for the trends and technologies shaping the future.

Publisher

Panther Labs

Category

technology

Language

en

Explicit

No

First episode

23 Aug 2021

Latest episode

24 Feb 2026

Latest episodes

37 of 77 episodes have a transcript.

1. 

   24 Feb 2026TRANSCRIPT

   Google's Michael Sinno on Autonomous Detection at 7 Trillion Logs Per Day

   Director of Detection & Response at Google Michael Sinno details Google's autonomous security operations, processing seven trillion logs daily with strategic AI implementation, and shares metrics like reduced incident notifications, in this interview.

2. 

   10 Feb 2026TRANSCRIPT

   Block's CISO James Nettesheim on How 40% of Their Detections Are Now Written with AI

   What if the real risk isn't adopting AI agents, but refusing to? James Nettesheim (https://www.linkedin.com/in/james-nettesheim-7149a511/), CISO & Head of Enterprise Technology at Block (https://block.xyz/), argues that principled risk-taking beats playing it safe. James shares Block's journey co-designing the Model Context Protocol with Anthropic and building Goose, their open-source general-purpose agent that enables anyone in the company to write security detections using natural language. James also explores Block's Binary Intelligent Triage system achieving 99.9% accuracy, their data safety levels framework, and practical strategies for balancing autonomous AI capabilities with human oversight. James offers candid insights about implementing AI security principles, the evolution from tool experts to domain experts, and why open source remains fundamental to Block's mission of economic empowerment and technological innovation. Topics discussed: Co-designing of MCP with Anthropic and developing of Goose as an open-source general-purpose AI agent Implementing prompt injection defenses and adversarial AI concepts to harden Goose against malicious instructions and attacks Rolling out AI responsibly through data safety levels modeled after CDC bio-contamination protocols for sensitive data handling Democratizing detection engineering by enabling anyone at Block to write detections using natural language Achieving 40% of new detections created with AI assistance through recipes, playbooks, and automated tuning capabilities Building Binary Intelligent Triage system that analyzes historical alerts and investigations to achieve 99.9% automated triage accuracy Balancing autonomous AI capabilities with human oversight, requiring PR reviews and maintaining accountability for agent-generated code Transitioning from tool expertise to domain expertise as the future skill set needed for detection and response professionals Block's commitment to open source development driven by economic empowerment mission and desire to build accessible financial tools Listen to more episodes: Apple  (https://podcasts.apple.com/us/podcast/detection-at-scale/id1582584270) Spotify  (https://open.spotify.com/show/6xa9t5dty4eH0UXDQXIew9?si=1df5eac89b294b14) YouTube (https://youtube.com/playlist?list=PLjYWlPBgNuD4f-hPjTyq3iPC-nT64ckFr&feature=shared) Website (https://panther.com/resources/podcasts)

3. 

   27 Jan 2026TRANSCRIPT

   Compass' Ryan Glynn on Why LLMs Shouldn't Make Security Decisions — But Should Power Them

   Ryan Glynn, Staff Security Engineer at Compass, shares a practical AI implementation strategy, explaining how machine learning models reduce on-call burden from phishing triage by combining traditional ML techniques with LLM-powered semantic understanding in this interview.

Inside a recent episode

Google's Michael Sinno on Autonomous Detection at 7 Trillion Logs Per Day

Published 24 Feb 2026 · Transcript excerpt

[…] So we handle about a million tickets a year, of which, you know, less than 1 % are handled by humans. So we've already had a lot of fundamentals of detection as code and a number of other things that to keep the noise down for the analysts. But now, you know, with the advent of using AI in this manner, it helps us really take the routine and just automate it away using AI. But in many cases, there's, you know, we have to do a critical evaluation too, or should we use AI? Or do we just need a script and some SQL, right? You know, in many cases, a lot of products, I say, are, you know, things that are, that should don't necessarily need to be AI, but they're trying to catch up. […]

Who has been a guest on Detection at Scale

Names identified in recent episode analyses. Showing up to five guests.

• Michael Sinno
• Ryan Glynn
• Mike Vetri
• Gary Hunter
• Vjaceslavs Klimovs

Reach and audience

Public platform figures. Ratings count people who left a rating, not total listeners.

YouTube views

113,700

Score snapshot 15 Sept 2025

Apple Podcasts (US)

5.0 / 5

11 ratings

Spotify

4.7 / 5

15 ratings

Podcast Authority Score: 71 / 100

A composite of feed quality, social presence, YouTube performance and engagement. Read the methodology.

Quality

57

Social presence

94

YouTube

82

Engagement

82

Contact Detection at Scale

Guest appearances

Books guests

Based on episode analysis; this does not confirm that the show is currently accepting guests.

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