AI Threat Detection Is Broken | Zack Korman

AI Threat Detection Is Broken | Zack Korman

Everyone is selling AI security — so when the threats are AI-generated and never look the same twice, can the tools built to match known attacks even see them? In this episode of The Pluralsight Podcast, Zack Korman — co-founder of AI-native security startup Embroidery and former CTO — argues that the answer is no, and that most of what's being sold to close that gap doesn't work the way the marketing claims. Zack spends much of his time proving, hands-on, what AI agents can be tricked into doing, which makes him unusually clear-eyed about what actually protects an organization and what just looks like it does. From a law degree to leading security tech and product teams, he's built a following on a simple habit: cutting through the hype to find what's real. We dig into why defending against AI-driven attacks requires AI-native detection, and why a system prompt is not a security control — you can't write your way to safety with a stern enough prompt when an agent is running with credentials it should never have had. We also take a hard look at what leaders are getting wrong right now: assuming they have visibility into their AI agents when the audit logs barely exist, handing agents their own operator credentials instead of least privilege, and trusting vendor claims that fall apart the moment you follow the incentives behind them. Topics covered: * Why AI-driven threats outpace signature-based detection — and what AI-native detection actually requires * The Microsoft Copilot audit-log gap and why most organizations have far less visibility than they think * How to tell genuine AI security from "AI-washed" tools and vendor hype * How to weigh risk when deploying AI agents — and what responsible deployment looks like * How to build and lead a security team ready for the AI era Chapters: 00:01:14 Welcome & Why a Skeptic Founded an AI Security Company 00:04:25 "Also Me Being Mad" 00:07:00 What AI-Native Threat Detection Actually Means 00:11:10 An AI-Native Threat in Practice: Hide the Vulnerability 00:13:42 "Our Product Uses AI": Marketing Claim vs. Reality 00:16:21 The Microsoft Copilot Audit-Log Discovery 00:20:10 Visibility, Confidence, and Evaluating Agentic AI 00:24:43 The Limits of Sandboxing 00:26:50 Pulling Back the Curtain on the Vendor Space & MCP 00:31:12 Running Agents in Production & What a Ready Team Looks Like 00:34:29 Where Veteran Security Leaders Fit in an AI-First World 00:36:49 Skills, Hiring, and Where to Start 00:43:01 Rapid Fire 00:45:15 What Zack Is Building Toward & Closing Takeaway Stay up to date on everything happening in cloud, AI, and security — subscribe to our weekly newsletter at https://www.pluralsight.com/technews/ [https://www.pluralsight.com/technews/] Connect with Zack Korman: LinkedIn: https://www.linkedin.com/in/zacharyakorman/ [https://www.linkedin.com/in/zacharyakorman/] YouTube: https://www.youtube.com/@ZackKorman [https://www.youtube.com/@ZackKorman] X: https://x.com/ZackKorman [https://x.com/ZackKorman] Questions or comments? podcast@pluralsight.com www.pluralsight.com [http://www.pluralsight.com/]

Why I Stopped Writing Code | Wayne Hoggett

Wayne Hoggett — prolific Pluralsight author of 40+ courses across cloud, Kubernetes, and AI — joins Josh Burkhead to talk about what changes when AI moves from a tool you reach for to something embedded in every part of how you work. Wayne shares a striking admission: despite a heavy cloud-engineering background, he hasn't written code by hand in nearly a year — and what that shift signals for the teams technology leaders are building today. We dig into how the engineering role is being redrawn around AI: why architecture, judgment, and business context are becoming the durable human skills, why cutting junior talent to offset AI spend quietly shuts down your future pipeline, and how leaders should rethink hiring and team design when everyone on the team can generate working code in minutes. Wayne makes the case for building automation-first, AI-first workflows — then deciding deliberately where humans still need to be in the loop. We also get practical on the harder questions: how to prove ROI on AI and L&D investment, how to surface and govern "Shadow AI" instead of banning it, and why self-deployed agentic AI raises the stakes on security, defense in depth, and how fast your team can patch critical systems. Whether you lead a technology org, own an L&D strategy, or are an engineer trying to stay valuable as AI absorbs more of the execution work, this conversation is a grounded look at where the work is actually heading. Topics covered: * Redesigning teams and hiring for an AI-first world — and why architecture and business context are the skills that last * The real cost of cutting junior talent to fund AI investment, and the talent-pipeline gap it creates * Proving ROI on AI and L&D initiatives by starting with low-hanging-fruit workflows * Governing "Shadow AI" — enabling the tools your people already use instead of blocking them * Securing self-deployed agentic AI: defense in depth, automated patching, and responding fast to new vulnerabilities Chapters: 00:40 Welcome & Guest Intro 01:15 Inside Wayne's World: Learning, Building, Teaching 03:58 Is AI a Natural Fit or a Forced One? 04:39 Don't Skip the Fundamentals: Building T-Shaped Skills 07:01 Linux, Windows, and France's Government Switch 08:31 "I Haven't Written Code in a Year" 10:33 The Moment AI Changed How Wayne Works 12:05 Hiring Entry-Level Talent for Business Awareness 13:37 Fewer Developers? Where the Headcount Logic Breaks Down 14:44 Building Automation-First, AI-First Teams 15:25 What an "AI-First View" Actually Looks Like 16:17 When AI Code Creates More Work Than It Saves 18:38 Rethinking Cloud Expertise on Your Team 20:00 Why Self-Deployed Agentic AI Worries Wayne 21:23 Staying Valuable as an Engineer: Architecture & Patterns 22:32 The Return of the Business Analyst Mindset 25:35 How AI Is Reshaping Cloud Infrastructure Itself 27:19 Shadow IT to Shadow AI: Enable, Don't Block 28:36 Reliability, Determinism, and New Job Titles 32:32 Proving ROI: Start with Low-Hanging Fruit 34:09 Security & the Mythos Headlines: Defense in Depth 36:42 Giving Teams a Safe Sandbox to Experiment 37:59 Closing Thoughts Want more insights on AI, security, and cloud? Subscribe to our newsletters: https://plrsg.ht/3MZ78ya [https://plrsg.ht/3MZ78ya] Follow Pluralsight on LinkedIn: https://www.linkedin.com/company/pluralsight/ [https://www.linkedin.com/company/pluralsight/] Wayne Hoggett on Pluralsight - https://www.pluralsight.com/authors/wayne-hoggett [https://www.pluralsight.com/authors/wayne-hoggett] Connect with Wayne on LinkedIn: https://www.linkedin.com/in/waynehoggett/ [https://www.linkedin.com/in/waynehoggett/] Questions or comments? podcast@pluralsight.com www.pluralsight.com [http://www.pluralsight.com/]

The Human Edge in Cloud Infrastructure | Ned Bellavance

What does it mean to be irreplaceable on a cloud infrastructure team when AI can write your Terraform, parse your logs, and troubleshoot your architecture — all before your second cup of coffee? In this episode of The Pluralsight Podcast, Ned Bellavance — infrastructure engineer, Pluralsight author, and host of the Day 2 DevOps podcast — argues that the answer isn't about the tools you know. It's about the judgment, institutional knowledge, and operational experience that no model can replicate. From scripting VMware deployments on a three-person IT team to building reusable Terraform libraries across cloud clients, Ned traces how infrastructure as code evolved and why understanding what's happening underneath the tools has never mattered more. We dig into where AI genuinely accelerates infrastructure work and where it introduces serious risk — overprivileged credentials, non-deterministic pipelines, and assumptions that only fail once you're in production. We also take a hard look at what leaders are getting wrong right now: cutting junior engineers to offset AI investment costs, undervaluing institutional knowledge that doesn't show up on a balance sheet, and handing AI agents access they were never designed to have responsibly. Topics covered: * Infrastructure as code fundamentals and why declarative thinking changes everything * How Terraform shifted Ned's approach to infrastructure work * Where AI helps in IaC workflows — and where it creates real risk * Why LLMs should never run your deployment pipeline * The principle of least privilege applied to AI agents * Why institutional knowledge is the hardest thing to replace and the easiest to lose * The junior engineer pipeline problem leaders aren't seeing yet * Skills to prioritize right now: networking, identity, storage, compute, security, and observability Chapters: 00:02:08 What Is Infrastructure as Code? 00:04:52 From Consulting to IaC: Building Reusable Libraries 00:07:09 Terraform and the Declarative Shift 00:13:05 Where AI Helps (and Doesn't) in IaC Workflows 00:22:53 The Real Risk: AI Agents and Overprivileged Credentials 00:25:59 Why LLMs Should Never Run Your Deployment Pipeline 00:27:02 Infrastructure Engineers as Decision Makers 00:29:28 The Value of Institutional Knowledge 00:31:16 What Leaders Risk When They Cut Experienced Engineers 00:34:43 The Junior Engineer Pipeline Problem 00:41:58 Opportunities in AI Infrastructure for Early-Career Engineers 00:45:11 The IaC Landscape: Terraform, OpenTofu, and What's Coming 00:50:51 Skills to Prioritize Right Now 00:51:49 Final Question: AI Doing More with a Smaller Team 00:52:38 Closing Takeaway Want more insights on AI, security, and cloud? Subscribe to our newsletters: https://plrsg.ht/3MZ78ya [https://plrsg.ht/3MZ78ya] Follow Pluralsight on LinkedIn: https://www.linkedin.com/company/pluralsight/ [https://www.linkedin.com/company/pluralsight/]Ned Bellavance on Pluralsight - https://www.pluralsight.com/authors/edward-bellavance [https://www.pluralsight.com/authors/edward-bellavance] Connect with Ned on LinkedIn: https://www.linkedin.com/in/ned-bellavance/ [https://www.linkedin.com/in/ned-bellavance/] Check out the Day 2 DevOps Podcast: https://packetpushers.net/podcast/day-two-devops/ [https://packetpushers.net/podcast/day-two-devops/] Questions or comments? podcast@pluralsight.com www.pluralsight.com [http://www.pluralsight.com/]

What Good Instruction Really Looks Like | Amy Coughlin

What does good instruction really look like? Amy Coughlin has authored nearly 50 courses on Pluralsight covering Azure, AI, and cloud architecture — and she's spent years figuring out exactly what makes technical training land versus what makes learners tune out. In this episode, Amy pulls back the curtain on her approach to course design: why storytelling and real-world experience beat slide decks every time, what organizations consistently get wrong when they try to build training in-house, and why the best instruction is always built around the problem, not the tool. She also makes a sharp distinction that every L&D and technology leader should hear: AI chat tools are task tools, not learning tools. And confusing the two has consequences. In this episode: * What separates instruction that sticks from training that gets forgotten * Why themes, stories, and even corny puns make technical content more effective * The hidden risks of pulling your best SMEs to run internal training * What AI sycophancy means for developers who rely on it too heavily * Why focusing on the problem, not the tool, is the future of content design Chapters: 2:03 — Amy's unconventional path into tech 7:13 — From data platform architect to course author: how Amy found her calling 9:52 — Making complex cloud topics relatable: themes, storytelling, and board games 12:19 — Real-world examples and the value of learning from mistakes 14:15 — What makes good technical content stick (and what falls flat) 18:25 — The human touch in learning: why podcasts and infotainment still win 21:23 — Hands-on labs and why doing beats reading 22:37 — Why organizations struggle when they try to build training in-house 25:48 — Hidden risks of using internal SMEs as instructors 28:08 — Tech debt, vibe coding, and the real cost of underskilled teams 30:24 — Is AI a legitimate learning tool? The sycophancy problem explained 34:55 — The future of content delivery: problem-focused, short-form, and refreshable 37:26 — Advice for parents opening doors to tech careers for their kids 41:11 — Closing thoughts and how to find Amy on Pluralsight Want more insights on AI, security, and cloud? Subscribe to our newsletters: https://plrsg.ht/3MZ78ya [https://plrsg.ht/3MZ78ya] Follow Pluralsight on LinkedIn: https://www.linkedin.com/company/pluralsight/ [https://www.linkedin.com/company/pluralsight/] Amy Coughlin on Pluralsight - https://www.pluralsight.com/authors/amy-coughlin [https://www.pluralsight.com/authors/amy-coughlin] Connect with Amy Coughlin on LinkedIn: https://www.linkedin.com/in/amy-coughlin-07300b44/ [https://www.linkedin.com/in/amy-coughlin-07300b44/] Questions or comments? podcast@pluralsight.com www.pluralsight.com [http://www.pluralsight.com/]

Foundations for AI Success | Faye Ellis

Most organizations are excited about AI. Far fewer are actually ready for it. In this episode of The Pluralsight Podcast, host Josh Burkhead sits down with Faye Ellis — AWS Hero and Pluralsight Author Fellow, cloud architect turned educator, and AI upskilling strategist — to talk about what separates organizations that are stuck in AI curiosity mode from those that are building real, measurable capability. Faye brings a practitioner's perspective to some of the most pressing questions in L&D and technology leadership today: How do you close skills gaps when the technology keeps moving? How do you bring non-technical teams along without losing them? And why are 80% of AI pilots still failing to reach production — even as investment in AI continues to climb? Whether you're leading an L&D function, managing a technology team, or trying to figure out where to even start with AI upskilling, this conversation is packed with frameworks and honest perspectives you can take back to your team. In this episode: * Why fear is not an AI strategy — and what to do instead * How to move from AI curiosity to a skills-first, outcome-driven program * The case for AI literacy at every level of the organization, not just technical teams * What a successful upskilling program actually looks like in practice * Why the organizations getting it right treat learning as a continuous journey, not a project Chapters: 00:01:08 — From Data Centers to AI: Faye's Career Journey 00:04:07 — What Got Her Hooked on Teaching 00:05:41 — The AI Curiosity Trap: Why Organizations Stay Stuck 00:09:21 — What It Looks Like When Strategy Clicks 00:12:53 — Running a Skills Gap Analysis in a Moving Target Environment 00:15:13 — Including Non-Technical Teams in the Talent Pipeline 00:18:31 — Building a Program That Actually Works 00:21:07 — Connecting Learning to Business Outcomes 00:26:13 — Designing for Confidence, Not Just Competence 00:31:22 — Scaling a Learning Culture Without Letting It Fizzle 00:37:00 — Trust as the Hidden Driver of Upskilling Success 00:41:02 — What Leaders Are Still Getting Wrong About AI Literacy 00:44:20 — Rapid Fire: Myths, Hard Truths, and One Thing in Common Want more insights on AI, security, and cloud? Subscribe to our newsletters: https://plrsg.ht/3MZ78ya [https://plrsg.ht/3MZ78ya] Follow Pluralsight on LinkedIn: https://www.linkedin.com/company/pluralsight/ [https://www.linkedin.com/company/pluralsight/] Connect with Faye Ellis on LinkedIn: https://plrsg.ht/420lS3W [https://plrsg.ht/420lS3W] Questions or comments? podcast@pluralsight.com www.pluralsight.com [http://www.pluralsight.com/]