Here’s the latest short on the Global Nerdy YouTube channel: Debugging with Claude Code. Enjoy!
Category: Artificial Intelligence
At this moment in time, I can’t think of a better way to promote an energy drink and a beer than by what Liquid Death and Garage Beer did: harnessing the pop culture power of worries about AI data centers’ water usage (with an inspired solution), ’80s ballads, and Jason Kelce.
I love how the ad brings all sorts of people together; even Juggalos (who along with the furries, will show us the way to a better future)!
Here are some of the AI articles and videos I’ve been looking at this past week:
- Allen Bargi: Working With AI Feels More Like Leadership Than Coding
- Google: Introducing Gemini 3.7 Flash
- Mun logadan: Why does Opus 5 feel worse to work with?
- Netlify: Choosing an AI model: one prompt, 11 models, very different results
- jdagostino: No, We Have AI At Home — Chapter 1: A Box Of Scraps
- opensauce.it: Cloudflare’s AI psychosis
- Jason Brown (loktar00): Qwen3.8-27B – Release Day Demos
- Wonkhe: We can’t afford to turn away from computing degrees
- Machine Learning Street Talk: [VIDEO] The Real Reason Huge AI Models Actually Work (Prof. Andrew Wilson)
- Joshua Barretto: Why I remain a skeptic
- Tim O’Reilly: The Apache Lesson for AI
- Cal Newport: On AI Coding and Its Discontents
- Borislav Nikolov (jackdoe): My anti AI computer
- Google: Why Go is an Ideal Language for AI-Assisted Software Engineering
and finally, the folks at Honest Government Ad do an ad for AI Data Centers:
I spent the morning and early afternoon of 813 Tech Day at Hotel Haya in Ybor. I’m still thinking about the Fortifying the Digital Frontier: Cybersecurity at the Forefront of Fintech Innovation session, largely because of the twist that host Michael Hall introduced, which made it different from every other “Cyber is important, yo!” panel I’ve sat through. About two-thirds of the way in, he stopped running the panel and turned it into a consulting engagement. On stage, for free, for a random attendee. And it worked!
Read on, and you’ll see.
A show of hands
Michael started by asking everyone who runs a company or product that touches money, customer data, or both to raise their hands.
Some hands went up, which wasn’t surprising.
Then he asked: “Keep them up if you have a single person whose actual job is nothing but security.”
All hands down.
And that moment was the panel in summary. A room full of people founding or working at companies (or hoping to found and work at them) handling money and PII, and essentially zero dedicated security headcount among them. To be fair, a number of them were solopreneurs. Still, Michael’s follow-up question was an important one: “So what are you going to do about cyber warfare?”
Introducing the panel
The panel had unusually good coverage of the problem space: economic development, defense-grade compliance, offensive security, and someone who actually runs a bank.
They were, from left to right onstage:
- Alexei “Alex” Chirokikh, CTO of Waterfall Bank, with a couple of decades of building core banking platforms across a dozen-plus countries
- Aaron Butler, founder of BlackHack Society, who builds security and compliance programs for SaaS companies and had just flown back from DEF CON the week before
- Candace Williams, Associate Director of Cybersecurity Leadership at Raytheon, Tampa-based, and author of No Experience Required: Your Cybersecurity Career Kickstart Guide
- Paul Sohl, CEO of the Florida High Tech Corridor, which spans 23 counties, three research universities (USF, UCF, UF); he’s also a retired Navy Rear Admiral
The gap between passing the audit and actually defended
Michael’s next question was a good one: “What’s the widest gap between how secure fintechs think they are and how secure they actually are?”
Alexei’s answer was the cleanest formulation of the compliance trap: compliance does not equal security. You can check every box and still be wide open. His diagnosis of why startups get this wrong:
“We identify the target, then we fire, and then we aim.”
Speed first, aim later. But in banking, “later” can be expensive in ways founders don’t model. He mentioned that for an average-sized bank, a single day of downtime can put the bank’s license at risk.
Candace, coming from the defense side, made it concrete with the ATO, the Authorization to Operate. You bring in an assessor, they verify you’ve got your asset labeling and your SSO and your password policy, and you get the shiny gold star.
And then what?
“Are you updating your AV definitions after you have the ATO? Are you patching on a specific cadence? Are you continuously monitoring the controls you got a check box for?”
Compliance is a still-frame snapshot. Security is the whole movie. Everyone optimizes for the snapshot because that’s what gets audited, but forgets about the movie.
“Too small to matter” is not a security posture
Michael asked Aaron to scare the room, and to Aaron’s credit, he skipped the horror stories and reached for stats.
An attacker can get into essentially any internet-facing machine at almost any company inside an hour. Depending on whose telemetry you’re reading, that number is more like a few minutes. Someone in the audience called out CrowdStrike’s breakout-time figure, which is measured in seconds now.
Aaron’s framing:
“A breach is inevitable. Not if, but when. Your worst day is my every day. If it’s going to rain, you bring a raincoat, not an umbrella.”
That’s why “we’ll deal with it when it happens” isn’t a plan.
What AI did and didn’t change
Aaron summarized it well:
“AI lowered the skill floor for attackers and accelerated the execution timeline. It did not invent new attack classes.”
Phishing, smishing, and credential reuse: these are the same failure modes we’ve had for a couple of decades now. Attacks are just cheaper, faster, and automated now. All this means that your unpatched, password-shared, over-permissioned environment didn’t get more vulnerable; it just got found sooner.
He also had a nice riff on password policy whiplash. We spent years pushing everyone to 15–16 characters, guidance loosened again, and meanwhile the real-world state of the art is that password123 became password12345.
Alexei’s defensive take was the one that fintech founders in the room needed: his bank is doing “baby steps” on AI. Instead of a tool, the first step an AI policy and an AI strategy with actual guardrails. Because the failure mode isn’t anything as melodramatic as a rogue superintelligence, but something more mundane, such as an employee pasting client data into a public chatbot:
“Yes, you can get the answer. But now you’ve already lost that client data. It’s somewhere, and you don’t know who can get it.”
He also noted, matter-of-factly, that some of the adversaries in this space are state-funded. A small bank in Tampa versus a government-backed team is not a fair fight, which is precisely why the guardrails have process over motivation; policy and architecture over vigilance.
The compliance question founders actually care about: Which one pays?
When Michael asked which single compliance framework a founder should chase this year to unlock the most enterprise revenue, Aaron flagged it as a contentious opinion and we got the most useful ninety seconds of the panel:
- SOC 2 Type II is the one. Depending on your market, it can move your ability to capture revenue by somewhere between 5% and 40%. Nearly everyone selling to enterprise ends up needing it anyway.
- HIPAA is self-assessed. Draw your own conclusions about how rigorously that’s happening across the industry.
- PCI DSS: If you’re doing payments and processing, you can largely offload it. Stripe already has it. Use their pipes; as a startup you can’t afford to build that infrastructure yourself.
- Then there’s the practitioner’s trick: security people maintain crosswalks that map controls across frameworks. Do SOC 2 first and you’re roughly 70% of the way to ISO 27001. Do them in order and stop paying for the same control four times.
Candace added the necessary caveat: the right framework depends on your industry, and in defense you don’t get to choose; there are non-negotiable requirements.
Alexei pointed out that PCI DSS matters for finance the way HIPAA matters for healthcare, so “which framework” is downstream of “which industry.”
Candace’s advice for taking this to a board is deceptively simple: explain it in their language…
- Bad: “We need to implement AC-2.”
- Better: “We sell Cracker Jacks, here’s the system that keeps the Cracker Jack business running, here’s why this control protects it.”
The panel turned into a live advisory board
This is the part I’ve never seen at a conference.
An audience member who’s a consultant mentioned he’s got a client (transfer agents, handling bank relationships and a mountain of shareholder PII) who wants to point an agentic AI system at their overflowing email inbox.
The debate: should they be cloud-based, or reverse twenty years of industry momentum and go back to on-prem so they can hot-swap open-weight models without token costs and keep everything whitelisted?
Michael stopped the panel, brought the consultant to the front, declared the panelists a pop-up advisory board, and made them answer.
The responses split about how you’d expect from their backgrounds:
- Alexei: Going on-prem relocates risk rather than eliminating it. It also means you’re now defending on two fronts: not just outsiders, but also insiders! You’re paying for infrastructure, security, and people. The “cheaper” assumption usually doesn’t survive contact with the invoice. When Microsoft ships patches every week, that’s a vendor doing work you’d otherwise be doing yourself, maybe badly.
- Aaron: Go hybrid, on the grounds that nobody has a crystal ball about second- and third-order downstream constraints, and hybrid preserves optionality for next year.
- Candace: Her world is mostly on-prem and air-gapped, so that’s where her instinct goes, insider threat and hiring burden included.
- Paul: He’s a retired Rear Admiral, so he followed the Navy adage “A ship’s a fool to fight a fort” and declared the question outside his expertise and deferred to the other panelists. In my opinion, that earned him even more credibility.
Michael then asked for the consultant’s contact info so they can follow up in 60 days and report back to the room on what he actually decided. That’s the accountability loop conferences never close. I’ll keep tabs on this and let you know how it turned out.
After that, they did it again, this time with an attendee trying to break from defense-sector BDR work into commercial cybersecurity account management. They brought her to the front and gave her a live career consult.ation.
Candace’s advice was to stop being invisible on LinkedIn and start advertising the specific role she wants.
Aaron also had good cybersecurity-specific advice:
“Cybersecurity is one of the most arrogant professions on the planet. If you’re the one person they want to have a beer with afterward, you’ve already won.”
(I work in cyber. He’s right. In this field, being able to communicate humility is a cheat code.)
And finally, in a fit of audience participation, and after quickly consulting NetFoundry’s careers page (I work there and love it!), I stood up and asked her “How about starting with a Sales Development Rep role that works with Account Management? We have an opening at NetFoundry.”
She said “yes,” and Michael yelled “Joey’s got to get her the job!”
(She and I chatted afterward. Our conversation will be ongoing, and I guess I’ll have to follow up with Michael in 60 days…)
Tampa Bay resources you should know about
Paul’s whole reason for being there was to make people in the room aware of resources they might not have tapped:
- The Florida High Tech Corridor spans 23 counties from Tampa Bay to the Space Coast, and deliberately plays Switzerland across all of them; there are no favorites among universities.
- USF’s Bellini College of AI, Cybersecurity and Computing is teaching ethics at the beginning of the degree rather than bolting it on senior year, on the theory that security is fundamentally a judgment-call discipline, not a checklist one. Elizabeth Nelson is the Corridor’s point of contact at USF.
- SBIR/STTR grants are available from eleven different federal agencies, it’s non-dilutive funding, and the Corridor can help you go after it and match on top of it.
- Don’t forget places like Embarc Collective, Tampa Bay Wave, and spARK Labs!
- Florida’s structural advantage is dual-use! The military and commercial sides are unusually well connected here, and the biggest buyer in the world is a short drive away.
Closing round: What’s the one thing you can’t get wrong?
Michael went down the line and asked each panelist for the single thing a founder walking out with one weekend and a small budget can’t afford to get wrong:
- Paul: Take the first step. Just do something. Anything.
- Candace: “Culture eats cyber strategy for breakfast.” If your people haven’t internalized cyber hygiene, none of the rest matters. Her running metaphor all afternoon was brushing your teeth: you don’t deliberate about it, you just do it. Security should feel like that.
- Aaron: Get business insurance, make sure it has carve-outs for cybersecurity, and make sure those carve-outs cover AI-driven attacks. That’s the one I hadn’t heard before and the one I’d act on tomorrow.
- Alexei: Hire the right people. (Michael made him clarify for the audio: right people.)
My four take-aways from this session
- Compliance is a snapshot, security is a movie. If your controls aren’t monitored continuously, your ATO or SOC 2 report describes a company that existed on one Tuesday.
- The framework question has an actual answer: SOC 2 Type 2 first, offload PCI to your payment processor, use a crosswalk so you’re not re-implementing the same control in four vocabularies.
- AI didn’t create new attacks; it created new attackers. The skill floor dropped. The people who couldn’t do this eighteen months ago can do it now, at scale, cheaply. Your threat model didn’t change; your threat volume did.
Take-away number four is so good that I wanted to separate it from the rest:
4. Turning a panel into a pop-up advisory board bit was brilliant! Michael took real attendee problems, put them in front of a panel of experts, made them answer in public, and asked them to follow up in 60 days. This moved what the panel said from the rhetorical to the practical.
Let’s see more of this, please, and nicely done, Michael!
Here’s a video that isn’t from the place where I work (NetFoundry), but from someone works in a different part of AI security. Yet somehow they ended up at the same question that we at NetFoundry ask: How confident are you that your current tools can defend against the new threats brought about by AI?
The video features an interview that took place at the recently-concluded Black Hat 2026 in Las Vegas, and the interviewee is Daniel Bardenstein, CEO of Manifest Cyber.
While NetFoundry focus on identity-based security, Manifest’s focus is on the AI supply chain:
- Manifest’s approach to security is knowing what’s actually inside the models and software you build and buy. They’re paying particular attention to provenance: training data you didn’t source, open-weight models pulled from a public hub, what’s “under the hood” of your agents.
- NetFoundry approaches AI security from the other end: what’s outside the models and software you build and buy, and what’s allowed to reach it. We’re about machine identity, service-to-service access, and attack surface.
Watch the video; it’s got some notable stuff, including:
- Open-weight adoption is about to accelerate, and the reason is control. Bardenstein argues that with a frontier model you have zero control over the system prompt, the training data, or what some provider (or government) decides to change under you. With an open-weight model, you control the whole chain: prompt, data, deployment, guardrails. If you’re already self-hosting models behind OpenZiti, this is the security-side justification for it, from someone who isn’t us.
- The Hugging Face incident he describes is darkly funny. A lot of people say that the risk with AI models is that they’re non-deterministic, but the HF situation happened because the sandboxing was weak and guardrails (which really means “ordinary software security”) were missing. Then, when HF pointed its own AI at the forensics, its guardrails read the request as “too cyber” and refused to help,. HF had to fall back to an open-weight model.I’m going to start saying this regularly: AI fails like software because it is software!
- “AI has its own supply chain and you inherit it.” Every model off a public hub and every third-party dataset is a dependency you didn’t build and can’t fully vet. Bardenstein notes public datasets have shipped with everything from PII to, in documented cases, outright illegal content nobody caught until after training. As the people on the Antiques Roadshow TV show will tell you, provenance is important.
- The Log4Shell framing is the one I’d steal (in fact, I’m doing that right now). Bardenstein built Manifest after the Pentagon Log4Shell vulnerability (a.k.a. CVE-2021-44228) scramble. The issue boiled down to a simple, embarrassing question: “Where is this one piece of code running across everything I’ve built and bought?” Even having seen what happened five years ago, if you told a CISO a model or dataset was poisoned, they’d likely not be able to tell you where it’s deployed, who owns it, or which vendors are affected without a lot of phone calls, emails, spreadsheets, and gnashing of teeth.
Bardenstein’s observations are pretty much the mirror image of our recently-published 2026 State of Secure AI Access report, where we observe that:
- 99% of CTOs/CISOs at orgs with 1,000+ employees admit they lack full visibility into their AI deployments.
- 54% named new AI-specific services like MCP servers and LLM gateways as fresh attack surface.
- 8% think their identity systems are sufficient for non-human workloads.
Pair our observations with Bardenstein’s supply-chain point and the full failure mode is bleak. An agent you can’t identify, running a model you didn’t vet, reaching services over credentials nobody rotates. That’s three unknowns stacked on top of each other.
My read (not Bardenstein’s or NetFoundry’s report’s) is that giving an agent its own OpenZiti identity solves the “reach” half of the problem. You know what the system or service can talk to and every log line means something.
What it doesn’t do is handle the composition half, or to put it another way, it doesn’t account for what’s inside the thing you just handed an identity to.
Two separate disciplines that happen to share a root cause: You can’t govern what you can’t see, whether “it” is a network path or a model’s provenance.
- Watch the video: https://www.youtube.com/watch?v=FZewRqZwfOM
- Download NetFoundry’s 2026 State of AI Access report: https://info.netfoundry.io/lp-survey-august-2026
Here’s a fun little contradiction to start your week with: NetFoundry asked 200 CISOs and CTOs whether they feel pressure to secure the AI they’re deploying. Unsurprisingly, and to my considerable relief, every single one said “yes”.
We then asked how confident they are that their current tools can actually handle the new risks, and 15% said “very.” 15% is very slightly less that the odds for rolling six on a 1d6. More specifically, among the CISOs, whose entire job is to be the professional pessimist in the room, that figure dropped to 10%.
That’s the current situation: Universal pressure, near-universal doubt.
If you just want to get to the report, it’s here. But if you’d like to know more, read on…
Disclaimer and where the survey comes from

I work at NetFoundry as a developer advocate, and NetFoundry commissioned this report. So yes, this is a vendor survey, and you’re correct to raise an eyebrow. (I’d be worried if you didn’t).
In our defense, we did the thing you’re supposed to do: the survey itself was run by an independent research firm (Global Surveyz), the respondents were 200 US-based security and technology leaders at companies with 1,000+ employees, and it was fielded this past May and June.
I’m going to try and separate what the survey found from what I think it means. The first category is data. The second category is me, a guy on the internet, having opinions, which won’t always be the same as NetFoundry’s Marketign department (it happens). I’ll flag which is which.
The number that reframed the whole thing for me
Of everything in here, this is the one I keep coming back to:
Security leaders are nearly 10x more likely to worry about securing machine-to-machine workloads than human access to applications.
Specifically: 69% said machine workloads (service-to-service, API-to-API, agent-to-whatever) are where they’re least confident today. Just 7% said human user access. The remaining 24% said “both equally,” which I read as “please don’t make me pick.”
That tracks perfectly, and it’s a compliment to the last decade of security work. Think about what we spent the 2020s doing. COVID sent everyone home, remote access became the whole ballgame, and the industry poured an enormous amount of money and brainpower into VPNs, Zero Trust access, and all sorts of security measures for a world that was suddenly more online that ever. It worked, and hman access to applications is, comparatively, a solved-ish problem. We got good at authenticating people. (I should know; it was during that time that I worked at Auth0!)
The issue of identity
The catch is that all of that machinery is built on one quiet assumption: that the thing connecting to your app is a human being with a unique identity. You authenticate the person, then you grant the access.

Agents and models don’t work like that. They don’t have identities the way humans do. So the tooling we built for the last problem doesn’t cleanly transfer to this one, and the volume is going the wrong direction, fast. Machine traffic is now growing several times faster than human traffic year over year. We got really good at guarding a door that fewer and fewer of the visitors are actually using.
A few more stats worth your attention:
- 100% agree their attack surface is growing. Not a plurality. Not a strong majority. Everyone. The average projected increase was 14% over the next 12 months, and that figure only counts AI deployments already underway or planned. 14% is probably the minimum.
- 93% are concerned about the new risks AI introduces, but only 15% are highly confident their current tools can handle them. That’s the gap I opened with. When the level of concern and the level of confidence are that far apart, something structural is going on.
- 99% admit they don’t have full visibility into their own AI deployments. That remaining 1%, which would have to be one respondent? I would like to buy that person a coffee (or beer! or bourbon!) and ask them a lot of questions.
- 90% are worried about shadow AI, the unsanctioned tools employees adopt on their own because the approved options don’t cut it. This is not a technology problem, it’s a human-nature problem. I will neither confirm nor deny my own contributions to the shadow AI at previous organizations, but in my defense, I was getting things done! When a tool is genuinely useful, people use it, memo or no memo.
- Only 8% call their current identity systems “very sufficient” for non-human workloads. 85% are now actively evaluating or exploring new approaches. That second number is the tell. When five out of six organizations are shopping for a new approach at the same time, that’s teh surest indicator that the industry is collectively coming to the realization that the existing tools weren’t built for this.
- Oh, and it’s slow. 55% cited risk and compliance review as a top contributor to delays in the network changes AI deployments need, and those changes add an average of 8 days from request to implementation. And that’s now, while AI-specific scrutiny is still warming up.
My read (this part is me, not the data)
In this section, I’m switching from reporting to speculating.
I think almost every number above traces back to one root cause: machines don’t have real identities. They have internal names so that developers and devops people can talk about them, but when it comes to having reasonably canonical identities like we humans do (full name, usernames, an email address, a government-issued unique ID number), we haven’t really created these for machines.
In the absence of machine identities, we have workarounds. On the less secure end, we have IP addresses; on the (relatively) more secure end, there are shared secrets, API keys, long-lived service-account credentials. As with most workarounds, they quietly rot. The credentials we give machines tend to carry more permission than they need. They rarely get rotated. After a while nobody’s entirely sure which agent a given key even belongs to, or why it exists.
Once you’re in that world, everything downstream gets harder. Visibility is hard because you can’t tell one agent’s actions from another’s. Access control is hard because a secret isn’t an identity, it’s some piece of data that happens to belong to a robot (and all too easily duplicated). Auditing is hard for both of those reasons at once. The identity gap is the root problem of most of the other security problems in the AI age.
NetFoundry — who are made of some very smart people, a few of whom are literal greybeards! — obviously has opinions about how to close that gap, and the report gets into them. That’s the vendor part, and you can take it or leave it.
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In case you saw the em-dashes in the paragraph above and thought “Aha! AI!’, I assure you that I typed them in myself, because this is my relationship with AI: Entering em-dashes is dirt simple on macOS: option-shift-minus. On Windows it’s a little more work: alt+0151. On Linux: control-shift-U, then release and type 2014, then return/enter. Let me have just a couple of em-dashes in my article. Please. |
But strip the logo off and the underlying observation stands on its own: we spent a decade giving humans strong identities and largely ignored the machines, and now the machines are the fastest-growing thing on the network. That bill was always going to come due. It’s just arriving faster than most people planned for.
The stat I want ask you about
That 14% attack-surface increase feels low to me. If you’re actually running agents in production right now, watching them spawn sub-agents and reach across cloud boundaries and pick up new tool integrations every sprint, does 14% over a year match what you’re seeing, or is it wildly optimistic?
(That’s a genuine question, not a rhetorical one. I’d rather hear it from people living it than trust my own gut.)
Read the full 2026 State of Secure AI Access report!
Download it here. (You have to provide a little info to get it.)
Pictured above is my standard AI usage disclosure slide, which I include in all my slide presentations these days. It’s gives the audience a quick overview of how I prefer to use AI when putting a talk together.
Here’s the text:
This strategy presentation was developed using AI assistance (Claude, ChatGPT, and Gemini) for:
- Research: Market trend analysis and competitive landscape review
- Editing: Grammar, clarity, and flow optimization
- Ideation assistance: Testing ideas and generating new ones, because no matter how creative you are, it’s impossible to come up with a list of things you’d never think of.
The main contents — including strategic insights, tactical recommendations, specific positioning, and any em-dashes (option-shift-minus on Mac, alt + 0151 on Windows, Google “em dash” and copy and paste it on Linux) — were developed based on analysis of the interview materials and 15+ years of experience in the industry.
I suppose I should also include something about being too much of an egomaniac to let a word prediction machine outshine me. My relationship with LLMs is sort of like the relationship between Dr. Niles “Chief” Caulder (who formed the Doom Patrol) and Batman, as pictured in Batman/Superman: World’s Finest (2022), issue 2:


