Code Story: Insights from Startup Tech Leaders
The Gene Simmons of Data Protection - AI Inference-time Guardrails
11 Feb 2026
Chapter 1: What is the main topic discussed in this episode?
Hello, listeners. Today, we are releasing the final episode in our series entitled The Gene Simmons of Data Protection, The KISS Method, brought to you by none other than Protegrity. Protegrity is AI-powered data security for data consumption, offering fine-grained data protection solutions so you can enable your data security, compliance, sharing, and analytics.
In our final, final episode, we are talking with Ave Gatton, Director of Generative AI. We talk about how AI safety doesn't end with training. It begins with inference. We explore the overlooked frontier of AI security from prompt injection, data leakage, and model manipulation. Ave helps us understand how you can build guardrails that operate in real time and adapt to evolving threats.
Dave, thank you for being on the show today. Thanks for being on CodeStory.
Thanks Noah. Yeah, it's great to be here. Excited to talk about everything we have on the agenda.
Absolutely. We got a jam-packed agenda today and inference time, AI guardrails and safety and all the things. You're director of generative AI at Protegraity and I know you've got lots of experience and things to speak on. Before we dive into that, tell me in the audience a little bit about you.
I've been in the Bay Area since way back in 2013. Maybe that's a long time for some, not a long time for others. But I originally came out here to finish my PhD in atomic, molecular, and optical physics at Lawrence Berkeley National Lab. And then I did a postdoc down here at Stanford Slack and then never left the peninsula. I moved to Tech.
around the time of the pandemic in 2020, and have been working on a variety of projects for Fortune 100 and greater companies at a variety of small startups for the past five years, six years, and now find myself at Protegri as the director of GenAI. It's been a long road, but I've been working with generative AI even before I moved to the tech industry.
I was doing a lot of projects with it at Slack. One more thing. The Slack I'm talking about is Stanford Slack, which is the linear accelerator you drive over when you drive down 280 and not the company Slack, which I get a lot of that. I say I worked at Slack and no, it's not the messaging code.
That's great clarification because I think most of us and me included on this podcast would immediately go to Slack, the communication device. I appreciate you clarifying that. We've done some cool stuff. You have worked for some interesting folks and now you're at Protegority. I'm excited to dive into our
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Chapter 2: What are inference-time threats and why are they critical in AI security?
But the true utility of an agent typically comes from automating actions that may interface with other agents in the company or other people in the company. For instance, sending an email, right? And if my agent can send an email, then now you have this lethal combination, which is it's got access to the data I can see, And it can send it to whoever.
It can talk to the outside world or whoever wants to talk to it. So if I send it off to do some task and somehow a document it reads in doing that instructs it to send information to this unknown email, then yeah, it can do that if it is an email summarizing or email sending task. So once again, you run into the problem of utility.
So what role does compliance play in shaping inference time guardrails? Because we talk about the security aspect of it and not the before, but the after the action limiting what some an agent can do. But where does compliance come into play?
This is the big sticking point. This is what we see over and over again when we talk to large companies is that they want to be compliant. They want to be HIPAA compliant. They want to respect law 25, GDPR, et cetera, et cetera, et cetera. And it is really hard to ensure that an agent system, an agentic system,
will be compliant when you can't say with certainty that it's not going to send out, say, PHI or PII to an external endpoint. And so you have to rely on things like guardrails or things like secure design in order to ensure that. And we can get into guardrails and how they're not that great and they don't actually...
work for a lot of things they work in principle but they're really easy to break and so one thing i think about a lot in terms of compliance is if you're using guardrails how many nines do you have to have in terms of your certainty that it will find all of or guard against all of the
PHI leaks or PII leaks, how many nines of accuracy do you have to have or of false positives or false negatives do you have to have in order to satisfy a regulator to say, yes, this system is secure? And I don't think we have that from the regulatory side, and I'm sure we don't have that on the industry side. So it's an open question as people roll these systems up.
Yeah, for sure. Tell me about guardrails then. So dive into that. Why you think they're only as good as some of those nines? Tell me a little bit more about that.
There's a recent paper out by Google that just gives an exact formula for breaking a guardrail. And the premise of a guardrail is that you can use a smaller model to guard against the data exfiltration of a larger model. The problem is the smaller model by its very nature has less cognitive capabilities.
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Chapter 3: How do inference-time risks differ from training-time risks?
We're early enough in the agentic revolution that I will... outline a development pattern here. Develop your POC that does not go into production however you want. Aim for maximum utility. Then take that and shoehorn it into a secure framework.
So you might have just an agent that just natively accesses an MCP server and gets information from your database and that does some transformation with it and then kicks you out the result. Fine and good for a prototype to see what level of utility you can get out of the agent infrastructure. Do that quickly, do that fast, rapid prototype, develop quickly.
Then the hard problem becomes when you put that into production, you need to find a way to implement these secure patterns as much as possible. So instead of just going and talking to the database, now you're asking like yes, no questions, and you have a secure model that, or you have a untrusted model that goes and accesses the database and can only return yes, no answers, etc.,
So you're separating concerns there, and then you're wrapping things in guardrails to make sure, oh, this is topically relevant. Like, I'm not being asked to, I don't know, generate limericks or do some weird stuff that has nothing to do with, say, the finance task that I know I'm supposed to be working on. That'll mitigate some of these sort of more naive attacks.
All of that is what organizations should be doing, what businesses should be doing as they move into production. And then you iterate. You go from there. You try and improve the performance to gain again what you had in your POC. But that's what I would recommend. I think that having an eye, if you can,
move the security into the design process as quickly as possible after the POC so that you have a better sense of how you can create a secure agent and what the performance of that secure agent will be. I think often you'll find that adding all these layers of security doesn't actually impact performance too much.
Typically, what people are looking for with agents now is raw ability as opposed to latency. I rarely come across a scenario where people are like, oh, this is taking too long. Almost everyone building with agents wants the results to be good more than they want. They want a slow, good result versus a fast, bad result. A fast, bad result, you might as well not have the agent at all.
So a slow, good result is what everyone is aiming for. And I believe there is a broad understanding amongst the public and internally to companies that an agent can go off and take minutes to do a task as long as it does the task correctly and gets back to me.
And I think a lot of that comes from the fact that you have the deep thinking models that people are interacting with in the public at large coming from open AI and anthropic and these where they now have the expectation, oh, the agent is working. I don't need to worry about, I can come back to this in five minutes and it will have an answer for me.
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