You Can't Outpitch a Category Your Buyer Can't Define. Selling AI Security Before the Vocabulary Exists.
Walk the show floor at Black Hat this year and the AI security booths look like a healthy, competitive market. A dozen logos, each promising to secure the AI you are about to deploy, each with a demo and a deck and a line out front. It reads like a category that has settled into a fair fight. It has not. It is a category that got capital before it got language, and the buyer standing in front of you can feel the difference even if they cannot name it.
The money is real and it moved fast. AI companies made up roughly half of every cybersecurity venture deal in 2025, about 50.5 percent by PitchBook's count. But look closer and the category cannot even agree on what it is. Counted broadly, AI security startups pulled in around $6.34 billion in 2025, nearly triple the year before. Counted narrowly, meaning the companies actually built to secure AI systems, models, and agents, it is about a dozen pure-play startups and roughly $414 million. The same two-word label stretched across a fifteen-fold range depending on who is doing the counting. That gap is the first sign of what the buyer is up against, and capital at this speed always outruns the vocabulary needed to evaluate what it funded.
Here is what that does to your deal. The buyer walking your booth is not comparing features, because they do not yet have a stable set of categories to compare across. They are one layer back, trying to figure out which kind of AI security they even need. Is the risk in the model they are fine-tuning, in the data feeding it, in the prompts users send it, or in the agent they just wired into their CRM? They cannot tell, and the market has handed them a dozen overlapping pitches instead of a map.
Most reps read that confusion as a pitching problem. They sharpen the deck, tighten the demo, and lead with why their AI security is better than the booth next door. That is the wrong move, and this piece is about why. It covers how the category got funded ahead of its own language, why the diagnostic skill beats the pitch, the three exposures your buyer is actually carrying and where each one lives in their stack, how to map exposure to the accountability the buyer cannot offload, and why none of this is the job you trained for selling endpoint or network.
The Category Got Funded Faster Than Anyone Built the Language to Evaluate It
Categories normally form slowly. A problem appears, a few vendors name it, analysts draw a quadrant, buyers learn the words, and over a few years everyone converges on a rough shared definition of what the thing is and how to judge it. That shared definition is what lets a buyer compare two products without a PhD. It is the unglamorous infrastructure underneath every clean bake-off.
AI security skipped that step. The capital arrived before the definitions did, and the funding data shows it. Depending on where you draw the line, AI security took in either about $6.34 billion in 2025, if you count every startup that touches AI and security, or roughly $414 million across about thirteen companies, if you count only the ones built to secure AI systems, models, and agents themselves. Same two-word label, a fifteen-fold difference. Dozens of vendors entered at once, each naming the problem slightly differently because there was no incumbent vocabulary to inherit. One sells an AI firewall. One sells AI security posture management. One sells model scanning and red teaming. One sells guardrails. Gartner files much of it under AI Trust, Risk and Security Management, a market it sizes near $2.34 billion in 2024 and growing past $7 billion by 2030, and most platforms now bundle several of those labels at once.
From the buyer's chair, that is not a competitive market. It is noise. The terms overlap, the boundaries move from vendor to vendor, and the same word means different things at two adjacent booths. When a buyer cannot tell whether two products solve the same problem or two different ones, they do not run a careful comparison. They freeze, they default to the biggest logo, or they ask the rep in front of them to explain the whole landscape. That last one is the opening, and most reps walk right past it.
Most Reps Read the Confusion as a Pitching Problem
The instinct, when a buyer looks confused, is to assume you have not pitched well enough. So the rep does more pitching. They sharpen the deck, they add a slide on differentiation, they tighten the demo to land the wow moment faster, and they lead with why their AI security is better than the company across the aisle. It feels like progress because it is motion, and motion is comforting when a deal is murky.
It does not work, because it answers a question the buyer has not reached yet. You are arguing that you are the best answer while the buyer is still trying to figure out what the question is. A better pitch lands as more noise on top of noise. The buyer nods, takes the one-pager, says it looks great, and leaves no closer to knowing whether they need what you sell. You did not lose on the merits. You lost because you sold to a buyer who could not place you on a map they do not have.
Leading with comparison also quietly concedes the buyer's framing, which is that all these booths are roughly the same thing and the only question is which is best. That framing is your enemy. If the buyer believes every AI security vendor is interchangeable, the safe choice is the biggest platform, not the best fit. Out-pitching reinforces the exact belief that hands the deal to the largest logo on the floor.
The Skill That Wins Is Diagnosis, Not Pitch
The rep who wins this is not the one with the best AI security pitch. It is the one who can speak the buyer's actual exposure, model risk, data poisoning, prompt injection, and map it to what that specific buyer is on the hook for. That is a diagnostic posture, not a sales one. You are not trying to be chosen. You are trying to be the person who finally made the buyer's own problem legible to them.
A doctor who walks in and pitches a surgery before examining you is a salesman. A doctor who examines you, names what is wrong in language you understand, and then tells you what it will take to fix it has already earned the close. The AI security buyer is surrounded by salesmen. They have met almost no one who will diagnose first. The rep who does becomes the trusted reference point against which every other booth gets measured, which is a far stronger position than winning a feature debate.
Diagnosis also changes who controls the comparison. When you hand the buyer a clean way to think about their own exposure, you are the one who drew the map, and your product naturally sits where the map says the real risk is. You are no longer one of a dozen interchangeable options. You are the vendor who understood the problem, and the others are now being judged against the framework you gave the buyer for free.
The Three Exposures Your Buyer Is Actually Carrying
To diagnose, you need a stable map, and you do not have to invent one. The OWASP Top 10 for LLM Applications, refreshed for 2025, is the closest thing the field has to an agreed taxonomy, and it is free, neutral, and credible with technical buyers. You do not need to memorize all ten. You need to fluently separate the three exposures that account for most of what a buyer is actually worried about, and to know where each one lives in their stack and who owns the consequences.
Prompt Injection
Prompt injection is the top entry on the OWASP LLM list, holding the number one spot for the second edition running, and for a reason. A large language model reads instructions and data through the same channel, with no hard wall between them, so an attacker can hide an instruction inside what looks like ordinary content, a support ticket, a web page, a document, and the model follows it because it cannot tell the command from the text. This is the exposure that lives at the moment of use, every time a user or a system feeds the model input. The buyer most on the hook here is anyone who has put an LLM in front of customers or wired it into tools that can take action. The consequence is the model doing something it was never supposed to do, on data it was never supposed to touch, triggered by input the buyer does not control.
Data and Model Poisoning
Data and model poisoning sits earlier in the lifecycle and is its own OWASP category. Here the attack is on what the model learned, not on what it is told at runtime. Corrupted training data, a tampered fine-tuning set, or a poisoned embedding can bend the model's behavior in ways that are quiet, durable, and hard to trace after the fact. The 2025 OWASP revision widened this beyond training data alone to include fine-tuning and embedding stages, which is where a lot of enterprises are now doing their own customization. The buyer most exposed is anyone training or fine-tuning on data they did not fully vet, or pulling models and datasets from sources they do not control. The consequence is a model that looks fine in a demo and is compromised at the root, which makes it the exposure that is hardest to detect and most expensive to unwind.
Model Risk
Model risk is the broad question of whether the model itself can be trusted, and it is where several OWASP entries cluster: the supply chain the model came through, the outputs it produces and whether anything downstream blindly trusts them, and the unauthorized extraction or misuse of the model as an asset. This is less a single attack than a posture question. Where did this model come from, what could it leak, what happens when its output is wrong or manipulated, and who can copy or abuse it. The buyer most on the hook is whoever signed off on putting the model into production and will answer for it if it misbehaves. The consequence shows up as the boardroom question after an incident, the one that starts with how did this get approved.
Map Exposure to What the Buyer Is on the Hook For
Naming the exposure is half the diagnosis. The other half is connecting it to the thing the buyer cannot offload, which is accountability. A security buyer does not lie awake over a taxonomy. They lie awake over the breach they will have to disclose, the auditor who will ask which controls were in place, the regulator who will want to see the AI governance they claimed to have, and the board that will ask why the model was approved. Every exposure becomes real to the buyer only when it is attached to one of those.
So the diagnostic move is to take each exposure and finish the sentence in the buyer's terms. Prompt injection is not a model behavior problem, it is the path to an unauthorized action your customer-facing AI takes that you will have to explain. Data poisoning is not a training quirk, it is the compromise you cannot prove the absence of when the auditor asks how you validated your model. Model risk is not a supply chain abstraction, it is the question of who signed off and what they checked, asked after the fact. When you map exposure to accountability, you stop selling a feature and start describing the buyer's own liability back to them more clearly than they could. That is the moment they decide you are the one who gets it.
This is also where you separate what the buyer needs from what they were about to buy. A buyer worried about a customer-facing chatbot has a prompt injection and output-handling problem, and a model scanning pitch will sail right past it. A buyer fine-tuning on proprietary data has a poisoning and supply chain problem, and a runtime firewall only covers part of it. When you can say out loud which exposure they actually carry and which they do not, you have done the buyer a service no other booth did, and you have quietly disqualified the competitors whose product solves the wrong layer.
Why This Is a Different Skill Than Selling Endpoint or Network Ever Was
It is worth being honest that this is a genuinely new demand on the rep. Endpoint and network security had decades to settle their vocabulary. By the time a rep walked into a deal, the categories were stable, the buyer knew the words, and the comparison ran on familiar rails. The rep needed to know the product and the competitive set, not redraw the map of the category every call. Diagnosis was rarely the job, because the buyer already had the diagnosis.
AI security gives you none of that. The vocabulary did not exist on a sales floor eighteen months ago. Many reps are pitching a category they cannot yet diagnose, using terms they learned from their own marketing rather than from the buyer's reality. That gap is partly structural, because the people who can genuinely speak this language, the practitioners, are scarce and expensive, and most sales teams are staffed from outside the domain and taught the security part secondhand. I wrote about that talent squeeze in the cybersecurity sales talent closed loop, and it is the reason diagnostic fluency is rare enough to be a real edge right now.
It also compounds the consolidation problem. When six of the most recognizable AI security companies get absorbed into larger platforms in barely a year, as I covered in what the AI security land grab did to the AE's pitch, the buyer's confusion gets worse, not better, because the logos keep moving. The rep who can diagnose exposure independent of which platform now owns which feature is the one who stays useful while the org chart churns underneath the category.
The Question Your Buyer Asks That You Couldn't Answer a Year Ago
Ask any rep selling into AI security right now what their buyers ask that they had no answer for twelve months ago, and you get some version of the same thing. Which of these do I actually need. How is what you do different from the four other AI security pitches I heard today. If I already have an AI firewall, do I still have a poisoning problem. A year ago those questions were rare. Now they are the meeting, and the rep who can answer them in the buyer's own terms of exposure and accountability is the rep who controls the room.
That is not a memorization problem you can cram for the night before. The category is still moving, the vocabulary is still forming, and the right diagnosis depends on the specific buyer in front of you and the specific competitors they are weighing. This is exactly the kind of work that benefits from real-time competitive intelligence and sales coaching, surfaced at the moment of the call rather than reconstructed afterward. It is the problem we built KillChain Overwatch to solve, as a force multiplier for strong reps and built for cybersecurity AEs who are good at selling and now have to diagnose a category that did not have words a year and a half ago.
The takeaway is simple and it is not a slogan. Stop trying to out-pitch a category your buyer cannot define. Become the person who defines it for them, accurately, in the language of their own exposure and what they are on the hook for. In a market this loud and this new, the diagnosis is the differentiation.
FAQ
What are the main AI security categories a buyer needs to distinguish?
At a buyer level, three exposures cover most of what matters and map cleanly to the OWASP Top 10 for LLM Applications. Prompt injection is risk at the moment of use, when input manipulates the model into doing something it should not. Data and model poisoning is risk in what the model learned, through corrupted training data, fine-tuning, or embeddings. Model risk is the broader question of whether the model can be trusted at all, covering its supply chain, its outputs, and its extraction or misuse. Vendor terms like AI firewall, AI security posture management, model scanning, and guardrails are products that address one or more of these, which is part of why the category feels confusing.
Is prompt injection really the top AI security risk?
It is the number one entry on the OWASP Top 10 for LLM Applications, and it held that spot across the 2025 revision as well as the prior edition. The reason is structural: an LLM processes instructions and data through the same channel, so it cannot reliably tell a command hidden in content from the content itself. That makes prompt injection both common and hard to fully eliminate, which is why it ranks first. It is not the only risk that matters, but for any buyer who has put a model in front of users or connected it to tools, it is usually the first one to raise.
How big is the AI security market right now?
It depends entirely on how you define it, which is part of the problem. By a broad Crunchbase-based count, AI security startups raised about $6.34 billion in 2025, nearly triple the $2.16 billion of the prior year, with average deal sizes climbing from about $34 million to $54 million. But that bucket mixes AI-powered security tools with the much smaller set of companies actually securing AI itself, which is roughly thirteen pure-play startups and about $414 million. PitchBook found AI companies made up about half of all cybersecurity venture deals in 2025. And Gartner's adjacent AI Trust, Risk and Security Management market is estimated near $2.34 billion in 2024, projected to reach about $7.44 billion by 2030 at a 21.6 percent CAGR. The honest headline is not the size, it is the speed and the disagreement: the capital and the labels arrived faster than a shared definition of what is being bought.
How do I diagnose a buyer's exposure on a call without sounding like an engineer?
Lead with where the AI lives, not with the threat names. Ask whether the model is customer-facing, whether they are fine-tuning on their own data, and what the model is allowed to do or trigger downstream. Those three answers point straight at the relevant exposure: customer-facing points to prompt injection and output handling, fine-tuning points to poisoning and supply chain, and action-taking agents raise the stakes on all of it. Then translate the exposure into the accountability the buyer carries, the disclosure, the audit, the sign-off, rather than the mechanism. You sound like someone who understands their business, not someone reciting a threat catalog.
Why doesn't a better pitch win these deals?
Because a better pitch answers which vendor is best, and the buyer has not yet figured out what they need. When the category vocabulary is still forming, a sharper differentiation slide lands as more noise on top of a confusing landscape. It also quietly accepts the buyer's assumption that all AI security vendors are roughly interchangeable, which steers the safe choice toward the biggest platform rather than the best fit. Diagnosis wins because it changes the buyer's frame before the comparison starts, and the vendor who supplied the frame is rarely the one who loses on it.
References
- OWASP. OWASP Top 10 for LLM Applications 2025. Prompt injection is ranked LLM01, and data and model poisoning is a distinct category whose scope was widened in the 2025 revision to include fine-tuning and embeddings. OWASP GenAI Security Project
- Software Strategies Blog, analyzing Crunchbase data. AI Security market 2025 funding data. Counted broadly, AI security startups raised about $6.34 billion in 2025, nearly tripling the $2.16 billion raised in 2024 across 175 companies at Series A, B, or C, with average deal size rising from about $34 million to $54 million; the narrower set of startups built to secure AI systems, models, and agents was roughly 13 companies and about $414 million. Software Strategies Blog
- PitchBook. Q4 2025 Analyst Note: AI Propels Next Phase of Cybersecurity Investment. AI companies accounted for about 50.5 percent of cybersecurity venture deals in 2025, outperforming non-AI peers on deal size and cadence. PitchBook
- Crunchbase News. Global venture funding in 2025. Context on the broader 2025 surge in AI and cybersecurity venture investment. Crunchbase News
- Grand View Research. AI Trust, Risk and Security Management Market Report. Market estimated near $2.34 billion in 2024 and projected to reach about $7.44 billion by 2030 at a 21.6 percent CAGR. Grand View Research
*Written by Jonathan, co-founder of KillChain Sales. Former offensive security operator, now leading go-to-market for an AI competitive intelligence platform built for cybersecurity AEs. If you sell in or around AI security and have watched a deal stall because the buyer could not tell your category apart from the booth next door, join the waitlist or connect on LinkedIn.*