Lately, doesn’t it seem like every brand-new security startup is saying the same thing?
They’ve “reimagined” detection and response with “agents.” They “use AI to make sense of security data.” They “connect the dots across your stack.”
The websites look great. The promises are bold. But when you finally get to the demo, the illusion breaks. Most of these tools are just wrappers: thin layers on top of your existing stack, designed to repackage findings and alerts in a new UI. At best, they run a handful of enrichment steps and hand you a longer list of things to investigate.
At worst, they don’t even filter out the noise. They just format it and “add context” (aka, make it even longer and harder to consume).
Director of Product Strategy & GTM at RAD Security.
We’re hearing this firsthand from teams who’ve been through the cycle with vendors who started out in the post-LLM world: Impressive website. Confident pitch. Underwhelming demo. And then the same question, every time: “Is that it? Is that all there is?”
This is a real problem…not just for buyers, but for the industry. At a time when security teams are genuinely overwhelmed, when budgets are tightening and talent is scarce, we can’t afford more tools that look smart but don’t do the work.
When there’s nothing under the hood
Wrappers’ promise of “AI for security” sounds transformative … until you see it in action. We’ve talked to teams who took demos of the latest “AI-native” platforms, only to find that the system simply rephrased whatever data it was fed. A CrowdStrike alert became a neatly summarized CrowdStrike alert, with other alerts added on. A vulnerability scan report became… a longer vulnerability scan report.
What these teams wanted was help knowing what mattered. What they got was a different wrapper on the same mountain of inputs they already struggled to interpret.
There’s a pattern here: Tools that collect every alert from your stack, run a few enrichment routines, and hand the pile back to you labeled “contextualized.” These systems often describe themselves as prioritization engines or copilots, but the internal logic is usually opaque and the output is rarely actionable.
Even features shown in demos tend to fall apart under real data, where nothing is quite as clean as the marketing examples. As one of our customers said recently: “Is the tool wrong? No. But it’s also not very useful.”
The teams building these tools are doing their best to solve real problems. But as anyone who’s worked in security long enough knows: there’s no shortcut to sense-making unless your tool actually understands what’s happening in your environment. And most of these tools don’t.
What it takes to go beyond a wrapper
If you’re evaluating security tools that claim to “put AI to work,” it’s worth stepping back and asking: what exactly is the work being done?
A wrapper tool can pull together outputs from other platforms, reformat them into natural language, and display them through a chat interface, but that’s not the same as delivering outcomes.
Here’s what to look for instead:
- Real system-of-record integration Tools should have some way to directly interface with the actual systems running your infrastructure, a “brain” of its own that doesn’t rely solely on signals from other vendors. Without that depth, any “insight” is just a repackaged notification.
- Defined, autonomous workflows Ask whether the tool operates on a schedule, independently delivers results, and drives action without constant prompting. If you have to ask it every time, it’s just a chatbot.
- Decision-making based on actual conditions Wrappers can parrot what other tools say. A smarter system understands how those signals relate to the state of your cloud, your risk profile, your compliance status. It can explain why something matters and what to do about it.
- Visible, repeatable results Can the tool show its work? Can it explain why it prioritized one risk over another, or how it arrived at its recommendations? Real intelligence should be inspectable.
- Answers and actions, not just summaries You’re not looking for a content generator—you’re looking for a teammate. That means structured outputs, not just nicer phrasing.
- Structured outputs that support decision-making The most useful tools provide results in formats that teams can act on, like prioritized triage queues, ready-to-share compliance reports, or remediation guidance aligned with your environment. These outputs help security teams focus effort where it counts and communicate clearly across stakeholders.
Everyone wants the gold. Few dig deep enough to find it
There’s a rush happening. New AI-native security tools are racing to market, chasing the promise of automated insight and hands-free remediation. But in that sprint, many are skipping the hardest and most essential step: collecting meaningful signals.
It’s easy to build a wrapper. It’s fast to plug into someone else’s data and rephrase alerts with fancier language. But systems that don’t gather their own telemetry can’t actually reason.
They can’t detect what’s real, or what matters. And they certainly can’t act with confidence. The result is a growing class of tools that promise action … but deliver only summaries.
Strong systems start with direct signal. Deep telemetry offers a window into the real shape of your environment: what’s running, what’s changing, and what matters most.
It’s the raw material that lets AI do more than pattern-matching. With the right signals, reasoning becomes possible. Action becomes credible. Intelligence moves from theoretical to practical.
We’re watching an AI gold rush play out in real time. There’s a race to be first, to raise fast, to ship something (anything!) that can wear the “AI-native” badge.
But in the scramble, a lot of teams are skipping the hard part: understanding the ground they’re building on. Getting signal takes time. Connecting it to real-world outcomes takes more. The companies that invest in that foundation now will be the ones still standing when the dust settles.
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