Security buyers have always relied on other people's judgement — analyst reports, peer recommendations, practitioner communities — because the product is difficult to evaluate directly and the cost of choosing badly is high. AI assistants now sit alongside those sources, and they answer the same question buyers have always asked: who is credible here?
Why this category is unusually exposed to AI-mediated discovery
Two things make security different. Buyers research extensively before ever contacting a vendor, and the questions they ask are the kind AI systems answer well: comparisons, category explanations, "who does X". A shortlist is often substantially formed before anyone fills in a contact form.
That means the relevant question is not whether a vendor's own site ranks. It is whether the vendor's name appears when someone asks a question about the category — and that depends on what independent sources have said, not on what the vendor says about itself.
Vendor claims are the weakest possible input
Large language models weigh corroboration. A claim that appears only on a company's own website, repeated across its own pages, carries little weight because there is nothing independent confirming it. The same claim appearing in security trade press, in analyst commentary, in practitioner discussion, is a different kind of signal entirely.
This is why AI visibility in security is largely a PR problem wearing a technical hat. The structural work matters, but it cannot manufacture corroboration that does not exist.
What actually generates citable material in this sector
Original threat research is the clearest example. It gets written about, referenced, and discussed, which produces exactly the independent, corroborating mentions that make a vendor a plausible answer. It also tends to be durable — good research keeps being cited long after the news cycle.
Named expertise compounds the same way. A researcher or CISO who is consistently quoted on a specific topic becomes associated with it, and that association is visible to systems reading the web. Category explanation helps too: the vendor that clearly explains a problem often becomes the reference for it.
The structural half
Alongside earned coverage, the parseable groundwork still matters: unambiguous claims about what the product does, consistent facts across every property a system might read, a real FAQ answering questions buyers actually ask, and clean entity information so the company is not confused with a similarly-named one. See what generative engine optimisation actually involves for how the two halves fit together.
What nobody can promise
No agency controls what an AI system says about a company. There is no submission process, no ranking to buy, and anyone describing guaranteed placement in AI answers is describing something that does not exist. What can be influenced is the evidence base these systems read — and in a category where corroboration is scarce and buyers are sceptical, that evidence base is a genuine competitive asset.
Where to start
- Audit what AI assistants currently say about the company and its category.
- Fix the structural basics: consistent claims, clear entities, a real FAQ.
- Commit to research or commentary that gives independent sources something to cite.
- Build the named-expert presence that makes a person, not just a brand, the reference.
See our AI visibility service for the full method, or our cybersecurity PR practice for how the earned-media half runs in this sector specifically.