# AI Guardrails and Agentic Software Reshape Enterprise Security

**Podcast:** a16z Podcast
**Published:** 2026-08-18

## Transcript

One of the interesting things in the OpenAI Hugging Face breach has been the difficulty that Hugging Face actually had responding to the incident.
Model providers have great reason to establish guardrails, safeguards, because these are super capable systems.
The unfortunate side effect of that is, as a defender, I may not be able to respond effectively.
The challenge with the existing...
We seem to be speed running every technology.
cycle that's ever happened before this one.
What is the path forward for a lot of this inference?
Cybersecurity was built to defend against two things, people and malware.
AI agents are neither.
In this episode, A16Z's Joel De La Garza sits down with Nick Warner of NEO and Max Pollard of Kotool to unpack what that means for security teams as increasingly capable models move from the cloud onto endpoints.
and into enterprise software.
They discuss why AI guardrails can actually make life harder for defenders, why traditional signatures and behavioral detection are starting to break down, and what happens when software no longer behaves predictably enough for security teams to define what normal looks like.
And from Black Hat, they look at the other side of the equation.
The same AI that's creating an entirely new attack surface is also giving defenders tools they could never have built before.
Awesome.
Well, thank you so much, guys, for joining us.
I think maybe let's set the stage for the discussion.
It's been a very active couple weeks.
We've obviously had the crazy pace of AI development.
Every week there seems to be a new model release.
There seems to be new capability.
There's some new fields metal getting won or some new vulnerability getting discovered.
And in the news recently has been the report that models from Frontier Labs have found a way to escape containment and hack things on the Internet.
which has been a pretty interesting revelation.
That's a very sophisticated capability.
I guess there's probably two things happening, right?
There's a discussion about, well, how secure is the internet actually?
Also combined with, man, these models are surely progressing and doing some great stuff.
So we've got the two of you here to discuss this, and I think maybe Max will start with you.
One of the interesting things in the open AI...
hugging face story breach event has been the difficulty that hugging face actually had responding to the incident.
And so maybe you could tell us a little bit about sort of like what was happening there.
Why won't these models help the good guys?
What's going on and what's the deal?
Yeah, so I mean, super topical, like the model providers have great reason to establish guardrails, safeguards, because these are super capable systems and we don't want attackers using them for nefarious purposes.
And so they have a responsibility to make sure those guardrails are in place.
The unfortunate side effect of that is, as a defender, let's say you're triaging an incoming bug bounty report or something of that nature, oftentimes you're going to be asking very similar questions to a probing attacker, which is, hey, what piece of this software is vulnerable?
How would I exploit it?
Can you validate this?
And so part of the side effect of these model providers doing their job is, as a defender, I may not be able to respond effectively.
Now for Hugging Face specifically, they had the luxury of having open weight and open source kind of in their DNA.
And so they were able to, when they saw these cyber refusals or these guardrails being triggered, fall back to GLM-5-2 in their case, but could have been KMEK-3 or a QN model, something that isn't going to have those guardrails in place.
And so for defensive teams, flexibility is kind of becoming paramount, right?
You need the ability to kind of fall back in the case of refusals.
Yeah, absolutely.
And I guess the question would be, what is it specifically refusing?
Like, I guess the thing maybe for folks that are listening is, like, I get that it stops you from saying, hey, go hack this website.
Sure, I mean, go to Citibank.com and change my account numbers, right?
And like, the refusal makes sense there.
But what are blue teams doing that is specifically generating the refusal?
Why does it look like a hacker to the models?
Yeah, I mean, I think it's almost more helpful sometimes to flip the script, right?
Like if I were an attacker trying to bypass a guardrail and I wanted to, let's say, hack into pets.com, right?
Like I may pull some bundle from the front end or I may just decompose and find the entry point that I want to target.
And instead of saying, go hack this system, I may say, hey, I own this code.
I am a developer at pets.com and...
I want to do an internal vulnerability assessment.
So that slight rephrasing of the question might trigger the model into wanting to be helpful and providing you input as to, here's some things you might want to shore up and here's how an attacker might get in.
Now, as an attacker, that's the answer I wanted and I might go ask it to go do those things, right, to validate that.
And so I almost find it more helpful to look at it in reverse.
Now, as is the case with any kind of...
guardrail-based system, you will run into false positives, right?
We noticed a very fun one maybe a quarter ago where there's a security tool out there called Vectra.
And it just so happens to be also a drug used by veterinarians to treat, I think, dogs or something like that.
So we noticed folks using the Vectra tool were hitting a biofilter, right?
Oh, this could be used to generate bioweapons and so we're going to refuse this request.
And so it's not only an exact science, but there's just a myriad of reasons why you can run into this stuff.
And so, Nick, Neo is building a way to defend endpoints from...
these inference-style attacks, or to deal with, I'd say, I guess they call it AI governance, sort of more broadly, right?
But from a brass tacks perspective, all things ultimately come to the end point, right?
The dust ultimately settles on the floor, and that's your infrastructure.
How are you guys thinking about defending and playing in this kind of world where the automated attacks need an automated response?
Yeah, I think part of the challenge with the existing security tools that are out there is they really were built to tackle two things.
The first being people, and the second is malware, AI and AI agents and agentic processes.
or neither one of those things.
And so what we wanted to do is get ourselves right at the layer between the human and AI interaction.
So a big part of what we're building is a way to properly set guardrails and controls around the software before it runs.
And we thought the best way to do that would be at the endpoint.
Yeah, that makes a lot of sense.
And it's interesting, right?
Because the tax now, if you look at sort of the way the models are behaving, it's a very different style of attack than what I would say the traditional hacker does, right?
Like it is, to your point, not malware.
And there is the element of social engineering.
But there's also sort of the I'm going to use a payload that gets the model to do something malicious, right?
And that's sort of like the category of attacks that are completely new.
Yeah, you know, and I think what a lot of these things that we've read about recently embody is the end justifies the means in the mind of the model.
And I think what people have learned is that regardless of guardrails that the AI labs are putting around these things, you can't rely on models to stop themselves or to understand context.
And I think the most recent couple of attacks we've seen in the last few weeks really have shown that.
Yeah, absolutely.
It's interesting on the blue team perspective because I think, and you guys have noticed this, right?
Like these models are all somewhat trained on varied data sets and there's a lot of post-training that happens that's very different.
And when you're responding to some of these things as a blue team member, you essentially get different outputs from different models, right?
And so is the value proposition you guys are working on, I get the refusals part of it and routing around kind of the refusals, but it's also making sure you pick the right tool for the job.
Yeah.
The reasons can really vary across teams.
Almost the simplistic tastemaker style, like, hey, I really like the way Opus presents a full-page report in the place of an incident.
But then there are also more deterministic evaluations you can run on how well does a certain model execute a step-by-step remediation plan?
How closely does it adhere to the steps that need to be taken?
So I think the most important part for blue teams is...
because of the pace of progress, to have as much flexibility as possible to be able to say, hey, when Astra gets released by OpenAI, I have an upgrade path and I know exactly what's going to improve and what's going to maybe require some TLC.
And so today those teams have probably two or three options, right?
You can roll with codecs or cloud code and lock yourself into a specific model provider.
And vendor locking is always good.
Yeah, we love it.
You can buy something that is kind of incumbent vendor plus AI, which leads you beholden to some level of opaqueness and what models do they support, etc.
Or you decide, hey, we're going to host open weights, we're going to support all major model providers, and I think a single H100 costs.
$250,000 a year.
And so that's just not a viable option for most teams.
And you kind of want to look towards providers that are always going to be releasing at the cadence that the labs do and give you that flexibility.
It's interesting, right, because we're going through this phase and you're starting to see this in the GPU markets, which is where prices are going up again.
Things are becoming more scarce.
There's a lot of talks about shortages.
And coming from a firm that spends a lot of time in those markets with their porcos buying time, it is more than just hype.
And it just seems like we're entering this phase of the build-out where inference will be everywhere.
And so right now it's clear that there's inference in the data center, there's inference in the neoclouds, the frontier labs, and eventually it's going to end on the endpoint.
And so I guess the question would be, that seems like a very different thing to defend against than, to your point, the traditional...
you know, Chinese malware that wants to come and steal my keystrokes.
Yeah, and, you know, the thing we're really starting to see, which is wild to think about, is companies now for the last year, from a cybersecurity defender perspective, have really been obsessing over how do I lock down, you know, software from the AI labs companies, which do great things, but also introduce new risk.
But if you think about stats that we're seeing now, that 50% of enterprise apps will be agentic by the end of this year.
And you can be sure that the half that are not will be rushing to become so in the next year.
And there's even less vetting and understanding around what agentic process is, what back-end AI models they're going to be using, what guardrails they...
they install and put in.
And so I think that's going to put even more, really more pressure on defenders to better understand how their employees are deploying using these type of tools.
And the average enterprise is something like six or 7,000 unique pieces of software within their environment.
And so you think about thousands of software instances becoming agentic in the next couple of years, the problem's going to get more complex and more challenging.
And so that's why we think giving visibility and control into the software universe that's unfolding is going to be paramount.
Yeah, it's funny, right?
It's because we're like, we seem to be speed running every technology cycle that's ever happened before this one.
And so like in both the original days, there was always the client and the server, right?
And there's always been this ebb and flow of information.
Everything starts with a centralized.
hub of everything being important lives here, right?
It's the server.
And then over time, you have things like web, you have things like thick clients, you have different technology things that come that federate the data, that push it out, that kind of push the envelope.
And we're very much going through that right now, except sped up 10 hundred times or 100 times.
And we're now seeing all these inference and these tests go into the endpoint.
It's just fascinating.
And I guess that means, I guess maybe because Max, you guys look across so many model providers.
How do you think for blue teams, for the actual people that are defending this stuff, what is the path forward for a lot of this inference?
What do you think the, I mean, I wouldn't even say end state.
What do you think next week looks like?
I know it's hard to think beyond next month, but where do we get to?
Because it just seems like all this, this feels like a watershed event for security teams where all of a sudden.
They were fine using OpenAI or they were fine using Anthropic.
And now they're like, maybe we need our own inference.
Maybe we should host our own models.
Like, what are you kind of seeing out there?
Yeah, to be honest, we see people trying everything.
We see people saying, you know, we've got Devin and Cloud Code and Cursor and, you know, we have the OpenWay stuff running locally or through fireworks.
So it's really all over the map.
I think, like, you know, what it means for...
a week from now, two weeks from now.
One thing I'm pretty confident in is there are certain approaches that worked in the past that clearly aren't going to work.
Things like...
you know, using static detection rules for everything, right?
Like that's, you know, just...
Signatures are probably dead.
Right, like it's great for the minefield.
Like, let me know if an admin hits this service in GCP that is a prod service.
But for other stuff, it's just, you know, it's going to fall apart.
And no amount of tuning and adjustment is really going to make that stick.
I think the other thing that's tricky is like...
Great, we should try and patch everything.
We should try and get rid of vulnerabilities, but to think that you're going to get to zero there is also kind of a failed approach.
And then even some of the more modern techniques like deception, right, that work really well, we're starting to see problems with, right?
Like, hey, there's a honeypot on a developer's device that contains AWS keys.
Well, guess what?
Our sales rep just asked to deploy something and the agent went and found the AWS key because it...
thinks that it should deploy it on AWS.
And, you know, now we got a ton of false positives in the deception provider, right?
So even newer approaches that were kind of like, hey, super precise, always going to work, are kind of being invalidated.
There's just a lot of rethinking that needs to happen for these teams, and they need to be able to move quickly and be flexible to be able to defend their companies.
I hadn't even thought about the honeypot example.
That's hilarious.
Yeah.
It's really crazy.
Wow, okay, maybe I shouldn't have sprinkled a bunch of credentials around my environment to test for that.
We saw overnight just like 100% true positive rate to just like a couple of our customers being like, this thing is awfully noisy now.
And we're like...
Probably a bunch of CISOs that woke up in the middle of the night coming like, oh, the honeypot's been tripped.
Yeah, yeah.
I guess to that point, right?
Like, I think we are, it is funny, right?
We are moving into a world in which signatures are dead.
And the attack surface is now the total sum of human expression.
Like, how do you think, like, that seems like a very different problem from sort of the legacy problems that security people have defended against.
For sure.
And if you look at the evolution of cyber defenses, it went from signature-based approaches to dynamic behavior-based approaches.
But at the end of the day, those behavior-based approaches made one sort of upfront bet, which is you could determine how software should behave, and you would look for anomalous behavior against that.
And now with agentic software, That just doesn't apply.
And so, you know, the example that you gave and then also the example of, you know, full system control by different agentic software processes, you can't really tune up or tune down your existing defenses to detect or not detect.
It's like bringing a knife to a gunfight.
And so really what we think it requires is a major rethink in understanding who's installing what, what can it do, how's it set up.
to execute on what it's installed for and what is it doing.
And believe it or not, like from a security perspective, most of those questions aren't currently answered because there was this assumption that you could know what software would do by its publisher or by its intended design.
And those days are gone forever.
Yeah.
Yeah, you can see whole, it's interesting because you do see the whole tech landscape reshaping in a way that I don't think, there's always been, it's funny, there was always a saying that sort of like, old tech companies stop becoming companies and become annuities, right?
Like they just have this long tail of enterprise software and, you know, CA was, Computer Associates was, you know, famous for this, which is where like, hey, we have this product installed on mainframes that have been running since 1968 and over the next 50 years they're going to continue to throw off this revenue.
But like, it does feel like this time this is very disruptive in a way that it hasn't been.
Like, and it's just an exciting time to be in tech.
So one of the more interesting things is that this is all happening and playing out against the background of us being incredibly warm in the desert at Black Hat, a security conference.
The enterprise version of a hacker conference.
Would love to maybe just do a couple seconds on sort of what are you seeing at Black Hat?
Like, it doesn't...
I'm going to...
do this a lot and spend a lot of time in this world.
So as I'm sure you guys do as well, but you know, it feels a lot like all the other black hats.
I'm curious.
Do you feel the disruption here?
I feel the excitement for sure.
You know, I'll go with the cynical take and then the optimistic take.
Always be optimistic.
The cynical take is this.
If I see machine speed on another billboard, I'm going to lose it, which is really ironic coming from us because when we launched six, seven months ago, we had machine speed in our launch.
So, you know, that's probably a hit on us, if anything.
On the optimistic side, talking to practitioners, talking to builders.
There's just an overwhelming sense that there's never been a more exciting time to build.
We saw this with the shift to cloud earlier where people are dealing with problems left and right but at the same time are invigorated and excited to take on the challenge.
That's been fun.
It's been fun to have those sorts of conversations.
And I'm sure you've been to about as many of these as I have.
So I would love to get your take on what you're saying.
Yeah, you know, I think there's an incredible amount of innovation out there against a backdrop of a really heightened sense of insecurity against this new threat landscape.
But I think, you know, even for us at NEO, what's really interesting is that the tools we're being provided with from the AI labs and frontier models give us incredible armament in building the right type of tools to defend.
And it's sort of ironic.
We're defending AI and we're also defending from AI.
And so it's really this dual prong mission that we're on.
But, you know, for us as a company, if we were trying to build what we built five, seven years ago, we'd have to hire hundreds of threat researchers, spend years building out this taxonomy of software.
And we're able to do that with thousands of agents in the automated process.
We were able to do that in weeks and months.
And so I think that's just sort of how...
reality plays out is that the balance of power always sort of shifts back, I think, in the favor of the Defender.
But we're going through that sea change right now.
Yeah, absolutely.
I mean, to me, it feels like, you know, way back when the first vulnerability scanning tools were coming out.
And, you know, it...
Prior to there being things you could download and do click to hack, right, you actually had to know how to program and had to figure out, like, how a buffer overflow works and how to write them and what, you know, all the different complexities of actual computer operations.
And then, obviously, these tools that let you do point and click hacking came out, and they gave birth to the script kitties.
And the script kitties, for those of you that weren't around then, was basically...
you know, largely males, 18 to 25-year-old that had downloaded a bunch of tools from the internet and decided to go hack a bunch of stuff, behaving very similarly to the frontier labs right now.
And that actually gave birth to start at the beginning of the security industry at large, right?
We had some of the first public companies, some of the first large-scale companies, and every cycle has gotten bigger.
And so it just very much feels like that same energy where everything we've done to get to this point, We still have to do it because those threats never go away, but there's this whole new category of stuff that nothing has prepared us for, which is ultimately just exhilarating if you're a security practitioner.
Great.
Well, thank you so much, guys, for coming by.
You've sweated it out with the best of them, and I'm sure this will not be your last black hat, so we'll try to do it again next year.
Enjoyed it.
Thank you.
Thanks for having us.
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