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Now, onto the show. Welcome to another Practical AI Podcast episode. This time it's just Chris and I, my co-host in these episodes where it's just the two of us. We try to take something that's in the AI news, or a topic for a deep dive, something that will help all of us level up our AI and machine learning game. I am Daniel Weitnack.
I'm CEO at Prediction Guard, and I'm joined as always by my co-host Chris Benson, who is a principal AI and autonomy research engineer. How you doing, Chris? Hey, doing great. Lots of cool stuff out there. Looking forward to today's conversation.
Yes, yeah, for sure. There's no shortage of things to talk about, but even in our, I don't know if you remember this passing comment, Chris, but I think it was in our episode where we were talking about MCP on top of Kubernetes, the guest mentioned that, hey, when anthropic kind of drops one of these white papers or research topics or blog posts, often that's a window into something that's significant and something to pay attention to and review in detail. And it just so happens that they on May, I think 27th of this year, 2026 released, I guess it's an ebook, white paper blog post, however you want to frame it, framework around with trust.
Yeah, zero trust for AI agents. Say zero trust for AI agents. We share a security framework for deploying autonomous AI agents in enterprise, covering the new threat landscape, a tiered zero trust architecture, and defensive operations built for AI accelerated attacks. So that's a lot of words. Now, I think first off, Chris, it's probably worth recognizing that anthropic obviously has a horse in this race, especially with things like cloud code or cloud co-worker, all the cloud things.
These are autonomous agents that can operate in your enterprise environment. So obviously, I think probably there are things that are happening and things where their customers or people using these tools are obviously thinking about the security implications of that. They also recently released cloud security, which is more on the AI for security side, not so much the security for AI side, which is mostly what we'll talk about today in relation to this article or ebook. But yeah, I think that's worth acknowledging obviously if people have a secure way of deploying autonomous agents. I'm sure they are hoping that many of those are built on anthropic technologies.
I'm sure they do. And just to keep in the back of our mind, this is the same organization that has Methos out there and is working with, I believe the latest number of 150 organizations is the latest thing I saw published on their website, trying to go through and do security audits and such as that. And with the timing of this, I would guess, don't know, but just making a guess that some of the leveling up that Methos has enabled is probably driving some of their zero trust and other security concerns going forward. So looking forward to this. Yeah, I guess that's a good place to start with the kind of premise of this.
I think there's a few things to frame here. Maybe one is there is probably a segment of the market and of our audience that is already using autonomous agents for something, even if that's just like cloud code or something like that for development purposes, where by autonomous, I mean, it's making actions on your behalf to do some things. And I think generally in terms of where we're seeing the market going, on the positive side, organizations are going to need to more and more adopt to these autonomous agents within their organization for value creation or new revenue or saving on operational efficiency.
So that's like thing, premise one is that that's the way the market's going. I think the other kind of background to this though, is like you were saying, there's a bit of a forcing function here because AI or how should I, so attackers, so malicious parties, hackers, et cetera, have equal, they have equal access to these agent coding and development capabilities themselves, right? Meaning that the pace at which people are about to be or are already being attacked and exposed to threats in their infrastructure is just like expanding exponentially, which means you cannot keep up with that level of attack using human only approaches.
Meaning that the forcing function that I'm talking about is you're necessarily going to have to adopt autonomous agents at least to help you manage the threats associated with the offensive use of this AI technology. So I think there's the positive side of this obviously, which is we, there's a future where autonomous agents are doing very positive things and you have this kind of digital workforce of agents within your organization, but maybe part of the forcing function behind this discussion is that people actually need to adopt autonomous agents because of this offensive threat to their infrastructure. Yeah, I agree. And I think that'll put quite a strain on a lot of the humans involved in this because there's a certain amount of leveling up from a human standpoint to understand what different harnesses are and what the different capabilities that are now becoming available, understanding different vendors versus open source and such as that.
So to actually get to the point where you can start implementing these is a bit of a lift. And I think that that's going to be something that we observed is that I think there'll be a spread across organizations where you'll have some, you know, on one extreme end you have the anthropics that are leading the way and producing these capabilities and stuff like that. But then there's a lot of kind of a mom and pop organizations or maybe not that small, but you know, mid-size and stuff like that, they're going to struggle to level up just a little bit. And so I think we have some interesting, I think the security landscape will be very interesting, a little bit wild west in the days ahead as people, even if tools are available, they have to get to where they can uptake those and get productive with them.
So yeah. Yeah, so I agree. And I think the, or maybe a way to get into this discussion is that if we frame the background with an assumption, and I'm sure there are arguments against this assumption, but let's assume that your organization is and will adopt autonomous agents for, you know, positive things like I talked about operational efficiencies, new revenue, whatever that is, and or cyber security purposes. If we assume that, then you say, well, okay, well, now we're going to have these autonomous agents operating in our environment. They could cause all sorts of harm themselves.
So it's like, I could shoot myself in the foot, trying to protect against the offensive malicious people by releasing a bunch of agents into my infrastructure. And they themselves cause a lot of harm. Like how do I manage those things? And anthropic has, so they have not come up with this idea of zero trust, to be clear. This is a general concept, which we can talk about the definition of, but they're essentially releasing with this framework a way to think about a zero trust approach or a zero trust framework for managing AI agents or autonomous agents within your organization.
So maybe it'd be good to just define that term. First, in the past, if we think about cyber security, there's been what's generally referred to as perimeter based cyber security. This is a more traditional model that would focus on that boundary of your organization and outside or internal and external and the kind of core principle being that I'm gonna trust everything that's inside and distrust everything that's on the outside. So there is a perimeter in which, within that perimeter I trust things. A zero trust approach to cyber security on the other hand, would actually assume that everything inside the network, that threats are already inside your network, already inside your perimeter.
So it treats every user, device, request as a potential threat. So that's why it's called zero threat. And like I say, this has been something that's been around from for a long time, NIST has published about it in zero trust architecture back in 2020 and other government organizations and others have talked about it as well. So that's that kind of difference. I don't know if that zero trust idea has crossed into your perimeter of knowledge, Chris, I'm sure.
Yes, without going into any detail at all, working in defense and intelligence, that it is pretty core. And yeah, I mean, the simple way of thinking about it is every single API request that you have has to have security credential. And that can be from a variety of different mechanisms, but you don't trust anything and everything is down to a granular level unless it is authenticated and authorized to do whatever it is trying to do. So in the world that I'm living, that's pretty standard. Though, I think there's room for all of us, even those of us who have been doing it to level up and get better at this.
So I don't think that there's anybody who has just nailed it. So it's one of those ongoing learning curves. Yeah, and we're about to dig into a lot of that as related to AI agents. However, to your point, there's a lot of organizations that are still trying to think about this concept, even generally in their general cybersecurity world. And one of my hot takes here is we'll talk about these foundational things that anthropic is suggesting.
And probably 90% of plus of organizations, enterprises that have AI deployments currently, are not operating according to this model. According to this framework, they would be completely exposed. And I think just acknowledging much of this is probably aspirational for enterprises. And they need to work towards it in a maybe a more rapid way, just because of how things are advancing. And there's better tooling out there, day by day, better products, et cetera.
But yeah, just so if you're out there and you're thinking, I have agents running and I have none of what we're about to talk about, that's probably the situation that most are in the enterprise world would be more focused. Hopefully today we can help people start on a path here to be able to do it. Exactly. Next week, you have no excuse. But coming into this conversation, you have an excuse.
Yeah, exactly. So I think the I would encourage people to if you just search for zero trust for AI agents, anthropic blog posts, we'll link it in the show notes as well so you can click through to that ebook and the framework itself. There's a lot that we won't be able to cover in detail. But I think the overall structure that they present are some kind of initial background and considerations kind of definitions related to autonomous systems that people need to consider. And then they talk about the current threats to those agent or autonomous systems and then how to apply the zero trust to those threatened agent systems.
That's kind of the flow of what they talk about. So the first thing, and I think this is something we've talked about more on the show and have already covered, but just to set the foundation. Some of these considerations, kind of background information that we may want to give is that, why are we talking about a new framework? Well, agents are different in how they operate. We've talked about this on the show before.
They use a distributed set of tools. They interpret instructions, try to accomplish goals. They execute operations without human initiation. I think importantly, they might preserve context across sessions if they're trying to accomplish some goal. And then you kind of add multiple agents and they might communicate with one another.
So you've got this multi-agent communication. Now, there's a couple of terms here, Chris, that I think we've even mentioned, but they just defined specifically related to agent security as new terms that people might be unfamiliar with. One is blast radius, which kind of, I think people could assume what that means. It measures the potential damage if something goes wrong, if an agent does go off the rails of that blast radius. And least agency, which I guess is a term coined by OOSP, and that extends this kind of idea of least privilege to agent application.
So you shouldn't be getting more agency to your agents than they need to do their agent things. And that's standard zero-trust ideas. You give it just what it needs and absolutely no more. Yep, and so that's kind of the, I guess, the background in which we're operating. Then the anthropic paper goes into these current threats, which is summer ones, we've talked about summer ones, we've not talked about as much, Chris.
It's interesting that they kind of frame everything within the agent world as agent systems, which I very much like in our product. That's why I insist on using the idea of AI system as a thing, because you have these distributed set of things that are powering agents these days. And so they kind of break down then this current threats to agent systems. The first of those, which is probably not a surprise, because it's the first on OOSP list often as well, is prompt injection and instruction manipulation. Again, we've talked about this.
There's everything from the obvious direct, human input into a chat interface, ignore your instructions and do this other thing, which you shouldn't be doing. But the one that they mentioned as the more difficult or scary one would be the indirect prompt injection, where that's coming in through maybe it's a file. That's you have an agent connected to your email and attachment comes through with hidden instructions in it. Anectotally, I helped another company do some interviews and I wrote a technical exercise and put it in a PDF. And I knew everyone would use Cloud Code like they should, but just because I wanted to be fun, I had all the instructions in BlackText and then I had an extra like three-fourths of a page.
So I just filled up that page with instructions that would make Cloud Code do the opposite of what I was saying in the instructions, just to see if they would touch it. So that sort of thing. Very devious. Very devious. It was that, did you make it white text in the PDF?
So it was an obvious white text. Which would get interpreted if you just uploaded it into Cloud Code or whatever. That's very sneaky, but actually quite common in terms of vector having, because everyone just wrote everything they can, the way things have been operating. And so, yeah, that's what we're doing today. Yes, true.
And I guess the other, so that's threat number one, prompt injection and instruction manipulation. Threat number two that they talk about, which is related to agents using tools, particularly through MCP, which was a topic on a recent episode of this show, which you can look back at for much more information on that. On MCP. Yeah. So when they talk about agents that can manipulate tools maliciously or kind of do things that they shouldn't be doing because of privileges, I think about Chris, like it's kind of like you set up a server, maybe I set up a fast API API that my agent could use.
And I only tell it about instructions, you know, about a couple get routes on the API in the instructions, but I don't shut down the other routes, right? And if the agent was smart in any sort of way, right? It could just look at the swagger documentation at the slash docs endpoint and know about all the other routes that maybe it shouldn't use. And then like all of a sudden I have problems, right? That's right.
And just to clarify, swagger is a protocol that defines what those routes are. And you mentioned, you know, kind of going off the rails, but you know, the notion of malicious MCP server has now been documented, and there could be lots of various types of tooling that is coming into being, now just to take advantage of these vulnerabilities. So I think we'll see a whole class of malicious software arising to do these kinds of tool and resource misuse. Yeah, exactly. And a lot of times these tool descriptors or schemas or metadata is injected into the context for an LLM to actually generate the output.
So if I'm a malicious party, or maybe just an agent that doesn't know what it's doing and like I say, as drifted from its goals or something, it's nothing preventing that from doing this poisoning thing where I like find out about the descriptor schema and metadata and I even modify that in the instructions to maybe get the MCP server to do different things, right? So this tool and resource misuse is definitely is a reason why it's kind of number two there. The next one, identity and privilege abuse. So yes, yes, exactly. So they talk about this agents often operate with elevated privileges or service accounts and traditional identity systems designed for humans struggle to accommodate them.
There's sometimes unscoped privilege inheritance. Almost like, I kind of think about this, like what was that cyber security book from the Cuckoos? Yes, the Cuckoos Nest or something. Yeah, yeah, yeah. Someone can tell us in the comments, but it's like you kind of land one place in a network and then you escalate privileges, right?
And you can move laterally and go in all of these directions, right? Really old book, it was one of the original cyber security books that came out before it was really a field. I read it many years ago and yeah, definitely inspiring. Yeah, and so. The Cuckoos, egg, that's what it was.
Yeah, and as you are looking at lots of different agents that have different levels of privilege and different capabilities and as agents are formulating things during runtime essentially that didn't exist as a preset static thing that you wanna do and they're developing that. It's very easy for one agent to spin off another agent and it has more privilege than it needs and then that can be taken advantage of. So there are lots of different variations of how those kinds. Yeah, yeah, for sure. So that's the privilege and I should say, I do really encourage people to take a read through the ebook.
Obviously we're highlighting some of these things but there's much more detail there. Also a great resource around this if you're trying to learn some of this as if you go to the OWAS Gen.AI project. We've had reps on our show before and my team's involved in the AI bomb project and other things with OWAS, there's a lot of great people involved but they have so many great resources online related to this sort of thing and guides for MCP, guides for agent security, et cetera. So take a look at those as well. You might be listening to this episode and thinking that, hey, I am part of one of those organizations that's in the 90% of enterprises that are not ready security wise for autonomous agents operating in my environment.
How am I gonna manage supply chain risks and have an AI build materials and define agent boundaries, cure tool access and implement input validation and output controls? Well, this is one of the reasons why I think it's so important to have great platforms that don't require you to build your own AI agent governance platform. That's why outside of the practical AI podcast, I personally am leading an organization full of really smart people that are thinking about these problems and have brought prediction guard into existence. Prediction guard is an AI control plane that's self-hosted. It lives in your own infrastructure where you're gonna deploy those autonomous agents and it allows you to manage the supply chain risk and put in governance policies that are enforced and maintain observability over those agents.
And I'm just really excited about the capabilities that are already in the product and are being released later this year. So I would encourage you, please check us out at predictionguard.com slash practical AI. You can book a call with me and the team to discuss how you're going to manage security for your agents operating in your enterprise. That's predictionguard.com slash practical AI. Predictionguard.com slash practical AI.
The next one that anthropic highlights is supply chain and dependency risks. So you were just mentioning how sometimes agents compose things at runtime, Chris. This includes potentially loading external tools or installing packages or changing infrastructure. And so that that supply chain can actually update in real time or at runtime as agents are trying to accomplish a task, but also model and tool supply chain. So models have their own supply chains related to the weights and how they were trained or fine-tuned, how easy it is to jailbreak them or prompt inject them.
But then MCT servers are also software components, right? They have their own integrations, their own software dependencies, et cetera, which have their own potential vulnerabilities. So all of this, it's very much a multi-layer thing that could evolve dynamically, which is kind of scary. And one thing to call out or talk about supply chain dependency risks is that all of the traditional zero risk vulnerabilities, all the things that we were talking about in the cybersecurity world before we started having, you know, AI-agentic system conversations about this, those all still apply as well. And when we're talking, I was prompted, no pun intended to say that by you when you mentioned the multi-layer.
So you can still have, you know, BIOS and CMOS vulnerabilities that can take, that lend themselves to some of these vulnerability, you know, layers and packages that build up. So there's many different points in a stack where these attacks can occur. All the way down to, you know, networking. All the firewall, right? If you're, if you're having agent operating in that environment, it could, you know, find and detect things that it shouldn't.
And so yeah, it's so, yeah, I guess multi-layered, which many security things are. And I know OOS always recommends this kind of layered approach. But yeah, the last two are kind of related memory and contacts poisoning and rag poisoning. Both obviously are this type of way that you can either in the memory or contacts to an LLM call or into rag data, retrieval augmented generation data, which often lives in a database, a vector database. If you have no control over what and how things are committed to that memory or to that vector database, there's nothing preventing agents or external parties from inserting things into that memory.
So, you know, the, I think the one, the example I used last year at the Midwest AI Summit, Chris, which as a reminder to our folks, Midwest AI Summit coming up October 15th, gonna be another great, great experience. You can can search the details Midwest AI Summit. But I think I use the example where it was a healthcare situation and someone, you know, an agent or a prompt is like, in a first interchange, it says, Hey, do this for patient A and then you, in the follow-up say, like, well, in all the following, you know, consider patient A to be patient B, and then you keep filtering in that information about patient A being patient B, and then all of a sudden when, you know, later on, you're wanting some information about patient A or patient B, all of a sudden you're getting data that you shouldn't be getting.
So it can happen and has been shown to happen. So, okay, Chris, that's all the scary things. I guess there's a lot of them. Now we gotta figure out how to fix this, right? Now we gotta figure out how to fix this.
And I do like the general structure that Anthropic provides here, recognizing again that many people are behind in this and that new tools and products will need to address many of these things gradually over time. They present three capability, I think what they call capability tiers or three tiers of application, basically saying, Hey, in these different areas, you need to do something. There's like the minimal thing that you should do, which they call foundation, the minimum viable thing. And then there's an enterprise tier, which means, Hey, if you're an actual enterprise and needing to be robust and resilient, you need to do these things.
And then there's advanced, which would apply to kind of particularly high risk or stringent regulatory environments or maybe aspiration only for everyone else to try to get to that level. So foundation, enterprise, and advanced in each of these categories. And then for, they develop something in each of these categories for each of a number of the threats that we talked about or the areas in which you need to secure. The first one, it kind of breaks them down by different by dimensions and then tiers them against those three tiers that you just described. Yeah, it's kind of like, I need to consider these however many things I forget how many there were.
I need to at least be in the foundation level for all of these. And then I can circle back and maybe upgrade particular ones to enterprise or like, gradually work on it over time. So the first of those is agent, identity, and authentication, which they kind of frame as the foundation for every other security capability. Because without this identity, you can't really enforce other things throughout the framework. Now, as we go through here, they talk about certain ways of doing identity and verification.
And there are a couple of terms in here that people may be unfamiliar with as well. One of those being they talk about hardware bound credentials. Have you, I'm sure this is also a part of your lifetime, Chris. Hardware bound credentials, or where you have to present a fit, you know, you may be a USB or something, you know, there's a lot of different ways it can, but you have to insert a piece of hardware or make access and make accessible a piece of hardware, which provides that authentication which in adversary would be unlikely to have in their possession. And that doesn't necessarily do it by itself.
There's usually multiple tiers, but that is one way of contributing significantly as if you don't have a physical piece of hardware in your hand, you're not gonna be able to gain access, even if you can break through other tiers. Yeah, and this idea of it being bound to hardware, I think is the key point that you're referencing, Chris, where otherwise they view kind of, hey, if you have API keys, for example, and those are just floating around, you should probably consider those already compromised if we're going with this idea of zero trust versus, if an agent has an identity and has an authentication to access this environment, it has authentication tied specifically to the hardware that it's operating on, you know, something like that.
Hardware bound credential is something that they talk about. And just to give some examples here in the agent's identity and authentication piece, the foundational, and we won't be able to go through all the tiers of all the categories we just don't have time. But just to give an example of these, there is the identity verification piece, the foundation level that they suggest there is to have unique cryptographic identifiers for each agent instance. So to assign persistent agent IDs back by cryptographic material, not just labels, the track agent lifecycle from creation to retirement, ID is appear in old logs and access requests.
The enterprise level is certificate based authentication with full lifecycle management. And the advanced is hardware backed identity with attestation. So that advanced, you know, you store agent credentials in hardware security modules or trusted platform modules with remote attestation, which there's a whole rabbit hole you could go down there with those terms, but that would fit into their advanced category. That's right. Yeah.
So that's an example of one of these categories, agent identity and authentication. The next category that they talk about is access control and privilege management. So assuming you have an identity for your agent, then you need to control access and privileges for that agent. And that authorization layer should enforce this idea that we defined earlier of least agency, which is ensuring agents receive only the access required for their specific function. And this can get very subtle like that API example that I gave.
You could only tell an agent about these endpoints, but if you haven't physically, like if you haven't literally shut off the network for other endpoints or something, then there's nothing preventing that agent from going off of the rails in that case. So yeah, just to give another kind of set of examples here, access control, foundation level is role based access control or RBAC with deny by default. That's the foundation in that category. That's right. And by the way, just as we're working through this, we wanted to make one quick comment.
These are all standard zero trust concepts. So those of you who may be watching, you may recognize a lot of these categories and stuff. And I think the key is kind of thinking about it within this agent context. And as we were all onboarding agents and stuff, that throws it out. But keep going.
I just wanted to call that out for those that might recognize that. Yeah, yeah, for sure. I think we can't abandon our good security intuition. And especially when you start treating these agents as having an identity and operating in this zero trust environment, some of these things kind of flow through if you work out those details. But yeah, the next category, behavioral monitoring and observability and auditing.
That was a so there's actually these two are tied together. We could probably talk about them together. There's observability, which essentially captures what agents do. So it observes what agents are doing. And you need visibility into that.
So you need logging and audit trails, often in our implementations with customers in my day-to-day work. I often like to say, hey, we need to know that this human user using this API key triggered this agent, which has this identity to do this goal, which issued these prompts, which triggered this tool call, which had this input, which was blocked by this governance policy, et cetera. Like that's where we're and down the line. We need that kind of traceability and logging. Otherwise, you can't have visibility or build rules or monitor things.
So that's the observability piece. But observability captures only what agents do. The behavioral monitoring that they're talking about determines whether the actions that agents are doing should be allowed or are suspicious. Are they appropriate for what you would expect? Are they appropriate?
Yes, that's right. Exactly. And this is behavioral monitoring and response. So in certain cases, like I say, when we enforce governance policies, we say, well, if we see this, then do this, right? So sometimes that's blocking certain things.
Sometimes it's just logging. Sometimes it's alerting someone using a particular platform. OK, the second to the last one is input validation and output controls. I think actually this one, so what are we on? One, two, three, four.
This is the fifth one. This is probably the one that most often comes to people's mind. And I think is often maybe overemphasized, which is this idea that you would have point checks over harmful things that the agent could produce in its output or harmful things that could go into the agents' context or something. This is very important, I would say. But it's kind of like table stakes.
The example I usually give is, is it bad for me to take my temperature if I want to be a healthy human? Well, that's not a bad thing. You can take your temperature. It doesn't mean that you are plugged into a healthy lifestyle or being governed by health records and as part of a health care system and have a primary physician and have a care plan and a diet. And it's just a very limited way to view that kind of overall health.
And if we extend that here, this would be these sort of point checks of validating inputs and outputs, which are, yeah, again, I would say those are table stakes. And the last one is integrity and recovery. So all of this prevention and detection assumes agents operate correctly when they don't. What do you do? Yeah.
And I think that's actually a pretty big question in the agent systems world in that if you think about going back a couple of points to behavioral monitoring and trying to identify what's appropriate for agents to be doing within all the other security parameters that we've talked about along the way. But when you have gotten outside the bounds of what is appropriate, trying to figure out how to roll agents back, especially if they're in critical functions, can be quite challenging because those critical functions still have to be addressed. And so if a critical function is compromised by an agent that is intentionally or unintentionally off the rails, then figuring out how do you take a critical system back and get it back to a safe place to proceed in whatever is appropriate for that function can be quite challenging.
And so I have spent some time in that space myself. And I think that there's a lot of imagination that has to go into it that maybe wasn't quite as necessary in pre-agentic zero trust models. So I just wanted to call that out. Yeah. Yeah.
They talk about it to give some examples, Chris, for configuration integrity. They talk about on the foundational level version controlled agent configurations and advanced level immutable infrastructure with attestation. On the recovery capabilities, they talk about it at the foundation level documented rollback procedures, which to your point, having an idea of what you might do is one thing being able to actually do it is sometimes a challenging thing. At the advanced level, they talk about self-healing systems with automatic remediation. So yeah, definitely agree with your points there.
I know that we're getting to the close to the end here, Chris, and just to kind of wrap things or get close to the end here, anthropic does a good job at kind of saying, hey, here's all of this stuff and all of these tiers and levels and categories, et cetera. But then they do provide a kind of phased way that you can think about implementing agents, which I think is helpful. One, identifying requirements to managing supply chain risks, including they talk about AI bomb or AI build materials, defining agent boundaries, defending against prompt injection, securing tool access, protecting agent credentials, and then safeguarding agent memory. And they give some kind of specifications under each of those phases for people to think about.
Yeah, I think as we're winding up, as they address it, I know just to share kind of how I perceive the establishing the workflow is in the zero trust world that we've been in for a number of years, it's fairly static. There's a lot of things, and you kind of have to tick them all off. And a lot of, it's almost a regulatory approach to system development. And I think the thing that agentic implementations require is trying to anticipate an incredibly dynamic capability that can arise, kind of an emergent quality that people are doing. And I think what Infropic has done for us is given us a way of taking what we already know in a zero trust context and pointed out that within a genetic system, these capabilities, it definitely requires a level up to take the same ideas, but get them out of that static mindset and move into an anticipating dynamic capabilities from agents.
And I know as we're in our own jobs and stuff, that certainly required us to kind of level up and reconsider. It makes it for a very interesting problem set to address. Yeah. And there's major thought process changes or philosophical shifts as you're mentioning that as practitioners, we may have to make. They talk in the e-book, Antropic does, about this idea of AI rendering that, hey, there's these fragile open source projects out here that you might rely on.
The thing to do might just be to have your agentic coding system just completely vendor or literally not copy, but generate a new version of that project that's proprietary to you and under your control and just include it in your project rather than then bringing in a third party dependency. So there's philosophical shifts like that. I do think there's some hard things that we'll still have to wrestle with around. I think there's still some of this conclusion that humans are going to have to make containment decisions around how to contain these things and whether it be threats in your environment or agents operating in your environment. And if things are moving so fast, I just think it's going to be hard for humans to, you know, if something is happening in your infrastructure and exploit timelines go from months to hours to minutes to seconds, you can't just rely on waking up the CISO in the middle of the night to approve shutting this thing down.
I mean, this is a revolution in cybersecurity, just to put a dot as we're finishing up here. Every intelligence agency in the world is learning how to both defend against and exploit these potential vulnerabilities that we're talking about, as well as criminal organizations of all sizes, shapes, on a global scale. So this, I think we're at the very beginning of this journey. I think this is a fantastic start to get us thinking. I think we're going to see a lot more tooling and a lot more capabilities coming out in the days ahead.
And it seems to be coming out very quickly because the threats have risen very quickly. And so I hope folks find this as useful as we did in terms of kind of reframing this modern take on cyber in our in this agentic world that we've been talking about, nonstop throughout this last year. And we'll like I say include the links in the show notes. So take a look at those and excited to keep the conversation going. Thanks for this today, Chris.
Yeah, thanks for taking us through. It was a good, good exercise day. All right, that's our show for this week. If you haven't checked out our website, head to practicalai.fm and be sure to connect with us on LinkedIn, X or Blue Sky. You'll see us posting insights related to the latest AI developments.
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