Down the Security Rabbithole Podcast (DtSR)

TL;DR: Stop vibe-coding your detections! This week's pod features Matt Coons at GitLab as we talk about detection engineering, but with AI agents.

Guest

• Matt Coons

Description

Your detection backlog is endless, your alert queue never stops, and “just tune it out” quietly creates blind spots. From the floor of BSides Atlanta, we sit down with Matt Coons from GitLab to talk about a different path: agentic detection engineering that moves faster while staying provable, testable, and human-owned.

We get concrete about what changes when AI agents enter the loop. Matt explains how end-to-end validation can work in practice, from running real API calls to ensuring the right events land in the logging platform and trigger the intended rule. We also unpack the difference between detection engineering and detection tuning, why time-bound tuning beats “mute forever,” and where human judgment still matters when an agent suggests exclusions that might hide real malicious behavior.

A big theme is designing security operations for scale. We explore signals versus detections, grouping signals by entity to expose patterns, and using multi-agent triage to reduce SOC burnout without losing visibility. Matt shares how model tiering can control cost, why newer frontier models can deliver more production-ready detection content, and why the real bottleneck is shifting toward human approval and accountability. We also hit prompting discipline, skill files, and how to get summaries that are actually shorter than the original.

If you care about AI in cybersecurity, SOC automation, threat hunting, MITRE ATT&CK coverage, and reducing false positives while catching more real incidents, this one is for you. Subscribe, share it with a teammate, and leave a review, then tell us: what part of your detection workflow would you trust an agent to handle first?

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TL;DR: Xavier Ashe was there when that first DDoS attack by MafiaBoy went down. He's got stories, and we're going to tell them. Recorded live at BSides Atlanta in 2026.

Guest

• Xavier Ashe

Description: A DDoS attack used to be something you could “just block” once you saw the source. Then defenders learned the hard way that thousands of small senders can knock over a giant, and the playbook changed for good. From the stage at B-Sides Atlanta at Georgia Tech, we sit down with security veteran Xavier Asht for story time from the defender’s seat during one of the earliest recorded distributed denial-of-service incidents and what that night still teaches security operations teams today.

From there, we follow the thread into the modern SOC: the messy gap between “something is broken” and “we are under attack,” the endless tooling arms race, and why AI makes some problems easier while creating brand new trust issues. We talk candidly about non-deterministic systems, why “AI will triage everything” keeps falling apart in practice, and how AI may be most useful when it helps glue together fragmented enterprise data the pain point SIEM tools have tried to solve for decades.

We also shift to the human side of cybersecurity: neurodiversity in the workplace, why so many of us feel more at home in hacker spaces than in “normal” environments, and how strengths like hyperfocus can be a superpower only in the right context. We close with a grounded five-year outlook on AI economics, regulation, and what happens when today’s cheap access meets real-world business pressure.

Subscribe for more conversations from the Security Rabbit Hole, share this episode with a friend, and leave a review so more defenders can find it.

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TL;DR: This week's pod is a conversation about AI and "Open-Weight Models", and if you aren't intimately familiar with what these are this is an episode for you. Wes has a lot of insight, and this is a discussion rather than an info dump.

Guest:

• Wes Widner

Description

The most disruptive AI feature might not be a new chatbot trick; it might be the ability to download the model and run it yourself. Recorded live at B-Sides Atlanta 2026, we sit down with Wes Widner, senior principal engineer at Cyber Haven and a veteran of McAfee threat intel and CrowdStrike, to unpack what open-weight models really are and why they’re changing the rules for security teams.

We break down the practical side of local LLMs: model weights, quantization, laptop-friendly variants, and why the open model ecosystem on Hugging Face has become a real “model store.” Then we go straight into the uncomfortable part: if a model runs on your hardware, the guardrails are optional. Wes explains how “locks” and refusals get removed, why safety and alignment consume huge effort for frontier labs (not labs), and why trying to regulate distribution can backfire by driving unlocked models into underground channels.

From there, the conversation turns into cybersecurity reality. AI doesn’t reliably understand context like “medical-critical” versus “normal” IoT, and reverse engineering firmware at home can look identical to reversing a sensitive system. Our takeaway is blunt: stop assuming the attacker is limited, and start designing detection, response, and secure engineering for a world where both sides have powerful AI copilots.

YouTube: https://youtu.be/xGIjifjdjnM

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TL;DR: Phile has been penetration testing for a long time. The difference now is that he's having a much easier time with it thanks to AI. It's an interesting world, and he's here to explain the offensive side of it.

Guest: Phil Brass

Description

Zero-days are not a once-in-a-career event anymore. From B-Sides Atlanta, we sit down with veteran penetration tester Phil Brass to unpack what happens when large language models make vulnerability research dramatically faster and cheaper, not just for defenders, but for attackers too.

We get specific about where AI penetration testing actually moves the needle: LLM-assisted security research that can turn a normal two-week internal pen test into a hunt for brand-new bugs in the commercial software your environment depends on. Phil walks through the mindset shift from “scan the network and review configs” to “download the exact products the client runs and start looking for flaws,” including lessons learned from targeting VMware infrastructure and finding critical issues in vCenter, the control plane that can unlock everything downstream.

That power comes with consequences. We talk responsible disclosure, vendor response, and the uncomfortable reality that patches can be reverse-engineered quickly, leading to real-world exploitation soon after fixes land. On the defense side, the guidance is refreshingly direct: tighten patch cycles, take threat intelligence seriously, and implement real micro-segmentation so one foothold does not expose every dependency on your network. We also dig into third-party risk management and why asking suppliers about LLM-assisted testing may become a new baseline.

We close with the human side of AI in security: validating LLM output, dealing with hallucinations, avoiding intellectual laziness, and making judgment calls that protect production systems. If you found this useful, subscribe, share it with a teammate, and leave a review so more builders and defenders can find the show.

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TL;DR: Mehul joins Rafal live from BSides Atlanta to talk about how AI is changing how vulnerabilities are found and exploited at scale and cost. It's an interesting conversation about how much it costs to break into an organization's software stack.

Video: https://youtu.be/kGajA9ipsp4

Guest

• Mehul Revankar

Description

A disclosed vulnerability no longer needs weeks of hacker effort. When exploitation becomes an automated, agentic workflow measured in minutes and dollars of compute, the way we rank risk, plan patching, and measure security maturity has to change.

Recorded live at B-Sides Atlanta, we sit down with Mehul Revankar, co-founder and chief product officer at Quantro Security (Contro.security), to unpack what “agentic AI offense” looks like in the real world. Mehul shares results from research on building and validating exploits at startling speed, why compute budget is becoming the true limiter, and how model escalation and exploit mutation loops turn yesterday’s “low priority” bugs into today’s fast-moving threats. We also pressure-test the comforting idea that you can simply patch your way out, and we ask what happens to common metrics like EPSS or “not exploited in the wild” when an AI agent only needs to succeed once.

From there, we shift to defense. We talk about compensating controls that still matter, including web application firewalls, tighter firewall policies, and practical ways to reduce exploitation even when you cannot patch everything. We dig into attack surface reduction, the hard problem of legacy software, and why decommissioning old systems may become more realistic as AI lowers the cost of rebuilding. Finally, we explore what AI-native scanners and defensive agents could look like when they can reason on the fly rather than follow brittle, rules-based logic.

If you’re thinking about vulnerability management, cyber risk management, AI security, and the future of cyber defense, this conversation will recalibrate your threat model. Subscribe, share this with your security team, and leave a review with the one metric you think still holds up in an agentic era.

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TL;DR: This episode is all about an essential skill few of us have mastered. How do you communicate value? What is it? And how do you make sure you're evaluating value in a way that makes sense to the person you're talking to? This is something that's essential to your career, your business, and your life, and Michael is a master of this art. Don't miss this one.

Guest:

• Michael Santarcangelo

Description

Most cybersecurity pros know the feeling: you ship real work, you reduce real risk, and the reaction is a polite nod and a budget cut. We sit down with returning guest Michael Tandarcangelo (aka Mr. Santa), who’s turned decades of “problems that don’t have names” into a clear philosophy at ValueSmith: delivering value in a world built to overlook it.

We get concrete about what “value” actually means and why it so often gets misread inside security programs. If leaders see the cost on a spreadsheet but can’t see the benefit, the mental math turns negative fast and security becomes a cost center. We talk through better ways to frame work like PCI DSS and SOC 2, how to connect controls to outcomes people care about, and why value is a dialogue not a declaration.

Then we go upstream to the real failure mode: solving the wrong problem perfectly. Michael shares a simple way to slow down just enough to ask smarter questions without looking “weak,” so you can align with the person who must recognize the result as valuable and make that value travel up the chain. We also dig into how market demand shifts over time, why certifications can be noisy signals, and why paying for outcomes beats paying for hours when you want great work.

If you want clearer stakeholder communication, stronger budget conversations, and a better handle on your own career value, hit play and grab Michael’s free Value Delivery Check. Subscribe, share with a teammate, and leave us a review if this helps you change how you show impact.

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TL;DR: This week's podcast features Rafal & Anton discussing the hype and hysteria that surrounds today's AI headlines. Which side is right, which side is propaganda, and what's real? Listen and find out.

Guest

• Anton Chuvakin

Description

AI panic has officially gone mainstream, and we’re feeling it from every direction: “AI will kill us” on one end and “AI is useless hype” on the other. Rafal brings in Anton Chuvakin to filter the noise and ask a simpler question: what’s actually true when you strip away the social media fever dream?

Anton shares why he’s genuinely torn between two models of reality. Some days, AI looks like a normal enterprise technology wave, complete with failed pilots, dirty data, and brittle implementations. Other days, the pace of improvement feels like an exponential curve, especially when you hear what thoughtful engineers inside major AI labs are seeing firsthand. We talk about why that insider perspective matters, how it clashes with “AI doesn’t work” skepticism, and why both the doomer and booster narratives can miss the point.

Then we get practical. Even if AI capabilities grow fast, the physical world often doesn’t: batteries, robotics, infrastructure, clinical trials, and regulation impose time and physics constraints that no model can simply will away. We connect that to cybersecurity realities too: if you don’t have logs and telemetry, no AI agent can conjure evidence that was never collected. Finally, we tackle the quieter fear that hits close to home: overreliance. If GPS weakened navigation skills, what happens when AI becomes the default for writing, maths, and judgment?

If you want a grounded take on AI safety, AI hype, and real-world limits, listen now, then subscribe, share the episode, and leave a review with your take: are we overreacting, underreacting, or arguing the wrong point?

Video: https://youtube.com/live/v0pkpH1SgXI

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TL;DR: This week's guest is Richard Bird, who has a new book coming out. He says, "Cybersecurity measures activity while its adversaries measure opportunity," and thirty years of spending has been priced against the wrong scoreboard. Great start, and you know it's going to get a little punchy.

Guest:

• Richard Bird

Description

Cybersecurity spending has exploded for 30 years, and the scoreboard still looks brutal. We sit down with Richard to talk about his new book, “Built Wrong: Why Cybersecurity is Failing and How We Can Rebuild It,” and we don’t sugarcoat the core claim: defenders measure activity while adversaries measure opportunity, and our metrics push us toward more tools, more dashboards, and more noise instead of fewer breaches and smaller blast radii.

We dig into why common security metrics behave like “measuring the weather” and how that creates a self-perpetuating cycle of reports, blinky lights, and ROI stories that don’t hold up in financial terms. Richard shares a blunt economic lens through his Hacker in a Hoodie framing, then we connect the dots to cyber insurance and how premium pricing can accidentally reward planned security purchases rather than proven loss reduction. If losses go up while premiums go down, something is broken in the measurement and incentive chain.

From there we get practical: how do we justify investments like firewall upgrades without relying on vendor promises and buzzwords? We talk about using real evidence like the Verizon DBIR and OWASP Top 10, and Richard offers a simple way to organize security thinking and metrics into three buckets: exposure, interdiction, and consequence. We also push on the leadership problem: if “security is everyone’s job” but only the CISO gets penalized, accountability will always be uneven.

If you care about cybersecurity metrics, cyber risk management, CISO leadership, and building a security program that speaks the CFO’s language, this conversation will challenge your defaults. Subscribe, share this with a security leader who’s tired of vanity metrics, and leave us a review with one measurement you think the industry needs to stop using.

YouTube Video: https://youtu.be/sOdKjIibIKc

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TL;DR: Robert "RSnake" Hansen & Jeremiah Grossman join the pod to talk about the "Vuln-pocalypse", or rather, the lack thereof. We do some math, discuss whether it's worth patching vulnerabilities attackers don't exploit, and discuss a potential alternative to the madness of vuln management and patching we're all living with today.

Guests

• Robert "RSnake" Hansen
• Jeremiah Grossman

Description

The “vulnerability apocalypse” makes great headlines, but our day-to-day reality is stranger: more scanners, more CVEs, more urgency, and still the same breaches. We sit down with Jeremiah Grossman and Robert Hansen from Root Evidence to ask a blunt question most security teams avoid saying out loud: if the overwhelming majority of known vulnerabilities are never exploited, why are we treating every patch like a debt we must repay at any cost?

We dig into where vulnerability management went off the rails. CVSS scores and severity tiers often turn into a kind of black magic, especially at enterprise scale where “patch everything” can be operationally impossible and sometimes actively dangerous. We share real-world patching fallout, talk about why prioritizing millions of findings is like turning the Titanic with a teaspoon, and outline what “reasonable” can look like when you stop optimizing for perfect dashboards and start optimizing for outcomes.

The turning point is data. Cyber insurance carriers and DFIR teams perform root cause analysis on real claims, and that actuarial view shows which remotely exploitable vulnerabilities actually drive financial loss. We talk about focusing on known exploited vulnerabilities, why a relatively small list of CVEs can matter more than thousands of “critical” alerts, and how controls like MDR, canary tokens, and segmentation help prevent exploitation from becoming a business-ending event.

We also pressure-test the AI panic. According to multiple carriers, AI-attributed losses are effectively zero when you exclude phishing, raising an uncomfortable thought: are we funding fear instead of risk reduction? If you want a more evidence-based approach to patch management, cyber risk, and cybersecurity ROI, hit play, subscribe, and share the episode, then leave us a review with the one change you’d make to how your team prioritizes vulnerabilities.

YouTube Video: https://youtube.com/live/wC1v8Vg7qr4

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TL;DR: This pod is part 2 of a 2-part series in which Patrick shares his insights on the world of finance as it relates to cyber startups and financing. As promised, the tracksuit shows up, and it's an epic episode!

Guest: Patrick Dennis

Description

Private equity can feel like a plot twist: one day you are celebrating growth, the next day you are hearing new words like EBITDA, leverage, recap, and “value creation plan,” and everything from headcount to product scope is suddenly under a microscope. We sit down with Patrick to explain what is actually happening behind the scenes when a cybersecurity company moves from venture-backed momentum into private equity discipline.

We start with a clean breakdown of venture capital vs private equity. VC funds can afford many misses because a few massive outcomes pay for everything, whereas PE makes fewer investments and needs them to work more reliably. That difference shapes company behavior, especially when “grow at all costs” collides with a finite market, customer concentration, and the hard truth that bookings and ARR only matter if they turn into cash.

Then we demystify the metrics and mechanics that operators keep hearing but rarely get explained: why cash flow becomes the real governor, why EBITDA becomes the shared language among management, lenders, and investors, and how a value-creation plan drives changes in spend, pricing, and operating discipline. We also get candid about the darker fork in the road: down rounds, running out of cash, taking on debt through leveraged buyouts, and what happens when a company cannot service interest, and lenders end up in control.

Finally, we map the exit paths and what they mean for employees: IPO vs strategic sale vs sponsor-to-sponsor trades, plus how cash and stock deals can (and cannot) translate into payouts for option holders. If you want to evaluate your company’s trajectory with clearer eyes, subscribe, share this with a teammate, and leave a review with the finance term you want us to unpack next.

Youtube Video: https://www.youtube.com/live/3YfiJiTdLhk?si=oiGXZVhHofb3ijnk

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TL;DR: This week's pod features a Masterclass on finance for Cyber folks, led by Patrick Dennis, where we ask all kinds of questions and go from idea to the pre-growth stage of a company. What's next? Check out part 2 coming next.

Guest: Patrick Dennis

Description

A splashy funding announcement can feel like proof you’re winning, but it can also be the moment the clock gets louder. We sit down with returning guest Patrick Dennis, a multi-time [cybersecurity] CEO and operator who “swims in these waters,” to translate the funding ecosystem into plain English and remove the mystique from venture money without dumbing it down. If you’ve ever wondered why a Series D gets celebrated, what that really signals, and what it costs later, this conversation is for you.

We walk step-by-step through the early-to-late funding path: angel investors and seed rounds, venture capital at Series A and B, growth equity, and how private equity can show up as companies scale. Along the way, we tackle the words people throw around at conferences as if they’re obvious: valuation, dilution, burn rate, cap table, down rounds, and liquidity. Patrick breaks down why valuation is largely “on paper” until an exit, why investor timelines often point to a three-to-seven-year window, and why the higher you push the price, the smaller your list of realistic buyers becomes. We also talk about the “markup” incentive system that can quietly trap teams into needing a bigger round or a bigger exit than the market will support.

Then we bring it back to operating reality: how the zero-interest-rate [ZIRP] era trained cybersecurity companies to spend aggressively, and what changes when money is no longer cheap. We close with the human part founders underestimate most: taking money means giving up control and choosing a partner, and “hands-off” becomes “hands-on” fast when performance slips. Subscribe for part two, share this with a founder or operator in your circle, and leave a review with the funding question you want answered next.

YouTube video: https://youtube.com/live/zbDNnbHKEaU

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Guests

• Jack Korzeniowski
• Ken Foster

TL;DR: Budget season is upon us, and Rafal & James sit down with Ken Foster and Jack Korzeniowski to get some insights into how CISOs deal with budgets, separate truth from fiction, and get down into the dirty secrets.

Description

Budget season can feel like a slow-motion incident response: everyone has competing priorities, nobody wants surprise spend, and security still has to defend the enterprise. We sit down with experienced security leaders to talk candidly about how cybersecurity budgeting really works when you’re fighting for headcount, renewals, and new controls while leadership asks for ROI and says, “Nothing’s happened for three years, so why are we spending so much?” We share what actually lands in a budget deck that survives scrutiny and what gets cut even when it is clearly needed.

From there, we get practical about the messy realities that break a clean plan. Audit findings can free up money faster than any risk narrative, but relying on that is a terrible strategy, so we discuss how to model “what happens if we don’t renew” and how to translate risk tolerance into finance language. We also dig into surprise drivers like mergers and acquisitions, sudden asset growth, and cross-team dependencies, where security buys the tool, but IT and app teams handle deployment and ongoing operational workload. If you don’t align early, you don’t just miss timelines; you damage trust across the organization.

Then we tackle the newest budget collision: AI. Tokenomics, unpredictable utility bills, rising infrastructure costs, shadow AI, and rapidly forming “AI debt” can turn a reasonable forecast into a painful true-up. We outline ways to cap spend, phase rollouts, and keep governance tight without killing innovation. Finally, we give vendors and consultants a clear playbook for being better partners: multi-year price predictability, phased licensing, honest roadmap commitments, no surprise true-ups, and real total cost of ownership and ROI modeling that helps us sell the work internally.

If you found this useful, subscribe, share it with a security leader heading into budget season, and leave a review with the one budget surprise you never want to relive.

Youtube Video: https://youtube.com/live/al7gQvb7C_Q

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Guest: Dr. Chase "ZeroTrust" Cunningham

TL;DR: AI Marketing slop took over Black Hat Las Vegas 2026, and Rafal sits down for a one-on-one with Chase Cunningham. Featuring their patented level of snark, real analysis, and hype detection, it's a fun conversation.

Description

Black Hat is supposed to be where the security industry shows its best work. This year, it also showed its biggest temptation: slap “AI” on everything, crank the volume, and sort out the truth later.

We break down what we saw on the floor and what it says about cybersecurity right now, with Chase Cunningham (Dr. Zero Trust) bringing receipts from vendor go-to-market changes and the money flowing into “AI-first” security startups. We talk about why so many tools sound identical, how “AI infrastructure” became the new catch-all label, and why the word “leader” stops meaning anything when everyone uses it. Then we get practical: how do you ask a booth team to define their artificial intelligence, what counts as autonomy, and what answers should make you walk away?

The conversation gets real when “AI security” turns out to be a polished UI sitting on top of a large language model API call. We dig into vendor lock-in, pricing and subsidy risk, and the uncomfortable question of who eats the impact when model providers change costs or terms. From there we go straight at the hottest promise in security operations: autonomous SOC and agent swarms. Non-deterministic models can be useful, but mistakes at machine speed can turn into outages and bad calls, so we map where automation helps and where humans still matter.

We also call out what we didn’t see: serious talk about hiring, growing junior talent, or serving SMB security needs even though many breaches flow through third parties and smaller vendors. If you care about security outcomes more than security slogans, this one’s for you. Subscribe, share, and leave a review, then tell us: what’s the most inflated AI claim you’ve heard lately?

YouTube video: https://youtu.be/mww1YpP-J0k

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Guests: Michael Farnum, Sam Van Ryder

TL;DR: Most cybersecurity dashboards don’t fail because they’re ugly. They fail because they don’t change what we do next.

Description

In Episode 717, Rafal sits down with Michael Farnum (Cybersec Community) and Sam (Dragos, a long-time OT and industrial cybersecurity practitioner) to talk about the uncomfortable truth behind security UI/UX: much of what shows up in the GUI is pure noise. We dig into the split between “pretty but useless” interfaces and tools that are practical but painful, then map out what a real practitioner-focused dashboard should deliver: contextual metrics, clear workflows, and data that drives action inside a SOC, an MDR, or an enterprise security team.

From SIEM and EDR lessons to product management realities, we unpack why companies miss the mark when marketing drives the roadmap or when engineering builds for engineers only. We also get candid about the analyst ecosystem, the difference between grounded practitioner feedback and “ivory tower” trend-chasing, and how that can steer executives toward tools that are hard to operationalize.

Then we move into AI and CTEM, continuous threat exposure management, and why the win isn’t a flashier interface. The win is faster telemetry correlation, so we can answer the questions that matter: is this vulnerability reachable in our environment, is it exploitable, and what should we prioritize right now? We also touch on modern “interfaces” like APIs and MCP, where the UX becomes how efficiently you can get results, even when the user is another machine.

If you care about security tools that actually work under pressure, hit play, subscribe, share the episode with a teammate, and leave a review with your biggest UI pet peeve.

YouTube Video: https://youtube.com/live/ts2rU1-j4EI

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Guests: Jason Vest & Josh Neil

TL;DR: Alert fatigue is still a problem; detection isn't generationally better - but we have all this AI. So what gives?

Description

Alert fatigue is not just a workload problem; it is a product design problem. We dig into a provocative claim sparked by a LinkedIn post: today’s “AI SOC triage” can be a band-aid if it only cleans up alerts after the fact instead of improving threat detection where it starts, in raw telemetry and early signal extraction.

We’re joined by Jason Vest (CTO at Binary Defense) and Josh (a statistician with experience from Los Alamos, the Department of Energy, and leading the Microsoft Defender for Endpoint data science team). Together we unpack why rules and detection engineering still matter: they encode what we know is bad, but rigid rule matches and atomic alerts can also trap teams in an endless false positive vs. false negative trade-off. Josh goes as far as to argue that alerts should “die in a fire,” pushing us to think in terms of attack stories and enterprise-wide context, not isolated hits.

From there we explore what actually scales: when anomaly detection works, why “model everything” breaks down, and how trigger-based just-in-time modeling can build lightweight models on demand, score the nearby context, then disappear. We also talk about moving from alerts to “situations,” using agentic AI to gather more context across identity, endpoint, and network, plus what transparency should look like for model validation and community standards.

Subscribe, share this with your SOC team, and leave a review. Where do you think detection should evolve next: better rules, better models, or a world without alerts?

YouTube Video: https://youtube.com/live/GYjePXCiKM0

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TL;DR: David Kennedy & Larry Whiteside, Jr. join the podcast to talk about how 'alerts' are a terrible measure of work in the SOC, and what we're doing about it. We discuss the analyst experience, why it's so difficult and leads to burning our best staff out, and also David introduces his latest: NightBeacon CMD. You need to hear this.

I'm starting to edit my own videos, so hopefully you like the new format.

YouTube Video: https://youtu.be/aMTFaXwRl_A

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TL;DR: This week's pod is the conclusion to the 2-part series on AI SOC with Anton Chuvakin, Daniel Miessler, Rock Lambros, Erik Bloch, and Raja Mukerji. We talk about the rational application of AI to cybersecurity and what the future holds for the various options. You won't want to miss this one.

Youtube Video: https://youtu.be/5YIpZuQLTMg

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TL;DR: Join Raja Mukerji, Anton Chuvakin, Daniel Miessler, Rock Lambros, and Erik Bloch for a banger of an episode (part 1 of 2) where we debate the potential of AI and the current state of delivery for SOC and security applications.

This episode was made possible by a group of people with much better things to do than record a podcast who donated their time. Please join me in thanking them for their generous contributions.

YouTube video: ...coming soon, blame Anton

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TL;DR: Seth Summersettof Embed Security joins the podcast to talk about the snake oil that's coming from the AI SOC sellers. There's such a significant gap between what you're being sold and what's real. Seth and the gang talk about what's real, what you should expect, and where the line of bullsh** is.

YouTube video: https://youtube.com/live/Z8R19cjTjwA?

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TL;DR: This week's pod features Tim Chase, a field CISO and, by background, a practitioner. We talk about "prioritization", a topic that's boring but incredibly necessary, as I am confident nobody out here has this solved yet!

YouTube Video: https://youtube.com/live/099rUu0pV0g

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