Karthik Kannan is Founder and CEO of Anvilogic.
“AI-native” used to mean something precise: built around AI from the start, not strapped onto legacy systems. That distinction predicted how deeply the AI could reason about your business.
Companies that built AI-native from day one made a decision that paid off. Their databases, formats and workflows were designed for a model to read: clean structure, rich labeling, context on tap. That foundation gave their AI real context to reason from, and its recommendations could be trusted.
This kind of head start is nearly impossible to retrofit, and most vendors were already too far along to start over. Instead, they bolted AI onto legacy systems and fed it whatever those systems exposed like outdated reports and dormant databases. One group had real context. The other had leftovers.
The Gap They Can’t Close
Vendors appropriate the “AI-native” label because building the real thing takes years they don’t have. When they can’t close the technical gap, they close the perception gap.
That’s an easier bluff to run. Using an LLM is table stakes; every serious platform has one wired in somewhere. The dividing line was never whether a vendor uses AI. It’s whether that AI understands your environment or can only reason in the abstract.
Many grafted AI onto old platforms instead of rebuilding them, then repackaged the seams as features such as a summarization tool or an “ask AI” button on last year’s dashboard.
It worked because no buyer can verify “AI-native” from outside; nobody inspects the data model or confirms the schema was built for a machine. All they see is a sales deck and an RFP checkbox marked “AI-native: yes.”
Once procurement started requiring that checkbox, every vendor scrambled to mark it, whether or not their architecture earned it, and nobody checks what happens after the sale.
Repackaging The Seams
That’s a real liability, not a technicality. A generic model can sound fluent about your alerts and still be blind to the basics, like which asset triggered it or which workflow should fire next.
It has no idea that pump 12 feeds the line that can’t go down. Your severity taxonomy lives in your systems, but nobody wired it into what the model can see. So a false positive and a shutdown-now event look the same.
Ask it about an anomaly, and it will answer confidently, pattern-matching against the internet’s view of your industry, not yours. It won’t say “I don’t know.” It will state something false, with aplomb, and nobody catches it until someone’s already acted on it.
How The Bluff Gets Past Procurement
The label can’t be trusted, the checkbox is worthless, but buyers still have to decide. The usual ways of verifying a claim don’t work; certifications lag the market, case studies are fluff and nobody inspects the data model before signing.
What’s left is the interrogation you do yourself, not a technical audit. Four questions, asked before the pilot, separate architecture from repackaging. Here’s where to start.
Context
Does it know your environment, or just the internet? Every vendor claims it learns over time. Ask how long, and what happens meanwhile, since “learning” usually means guessing from generic patterns on your live systems while you pay full price.
Reach
Can it reach your data where it lives, or does it need a migration first? Migration-first is a red flag: another silo, another retention policy, another login, months before the AI does anything and a second location for sensitive data that now needs its own controls.
Action
Does it act with governed autonomy, or just describe what already happened? Plenty of AI tools summarize well without doing anything about it. Real automation acts under rules you set, with stakes matched to the action. Isolating a production server and disabling a compromised account are not the same decision, and a vendor who can’t tell you which one their AI is cleared for hasn’t reckoned with it.
Economics
Does cost track value delivered, or raw volume processed? Vendors call volume-based pricing “usage-based,” as if that’s a feature. Your worst week is also your most expensive, since the bill scales with volume, not risk caught, so you end up hoping it catches less.
You don’t need an engineering degree to ask hard questions, and any vendor should be ready to answer them before you sign.
Moving Forward
“AI-native” had a good run as a real architectural claim. Then it became an RFP checkbox, and then a bet that most buyers never bothered to verify.
That bet is getting harder to win. None of these four questions require a technical background, just the willingness to ask before signing, not after something breaks.
The next generation of security tools won’t be judged by whether they use AI. That question is already answered (they all will). They’ll be judged by whether they can answer these four questions honestly, and whether the promises hold up six months later, once the demo is a distant memory.
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