Industries/AI Visibility for Cybersecurity: The 2026 Earned Media Playbook

Machine Relations for Cybersecurity Companies: How Security Startups Build AI Engine Authority

How cybersecurity startups build authority in ChatGPT, Perplexity, and AI-driven security research tools through earned media and Machine Relations.

When a CISO types "best endpoint detection platforms for Series B fintech" into Perplexity, the companies that appear are not the ones who spent the most on ads. They are the ones whose names appear in editorial coverage that AI engines have already cited, indexed, and learned to trust.

This is the core problem — and the core opportunity — for cybersecurity companies right now.

Machine Relations is the practice of building authority with AI engines — ChatGPT, Perplexity, Google AI Overviews, and the AI agents embedded in security procurement workflows — through earned media and editorial presence. Where traditional PR convinced human journalists to cover you, Machine Relations builds the citation graph that AI systems pull from when answering security buyer queries.

For cybersecurity companies, this distinction matters more than in almost any other category. Security buyers are sophisticated, skeptical, and increasingly research-driven. Enterprise technology purchases now involve an average of 6 to 10 decision-makers, according to Gartner's B2B buying research. The AI layer has added a pre-evaluation step that runs before any of those stakeholders joins a meeting: the CISO or their team queries AI search to narrow the field before anyone picks up a phone.

If you're not in the AI response, you don't make the shortlist.

Why Cybersecurity Buyers Now Start With AI Search

Perplexity has become a primary research tool for security professionals precisely because it surfaces real-time, cited sources. Unlike ChatGPT with its training cutoff, Perplexity pulls live content and shows its work — which aligns perfectly with how security teams evaluate information. They want to see the receipts.

A 2026 outlook published by Red Sift found that security buyers increasingly use AI tools like Perplexity to benchmark vendor claims against peer data and third-party coverage before engaging sales teams. This means your editorial footprint — not your website copy — determines whether you appear credible when a buyer runs that first query.

The publications that matter in this environment are the same ones security teams have trusted for years: Wired for broad technology narratives, TechCrunch for company credibility signals, Ars Technica for technical depth, and trade publications like Dark Reading and SC Magazine for CISO-specific validation. When these outlets cover your company, AI engines learn the association between your brand and the security category. That association compounds every time a security buyer queries an AI system.

The Cybersecurity Editorial Challenge

Cybersecurity PR has a specific tension that most other verticals don't face: the most newsworthy security stories are often the ones you can't tell.

You can't name the vulnerability you found in a client's infrastructure. You can't describe the breach you helped remediate. You can't publish specific attack chain details tied to a named third party without risking both legal exposure and the operational security of the companies you protect.

This is why security companies that rely on traditional PR often find themselves with thin editorial footprints. They're waiting for the perfect moment — a major threat report, a funding announcement, an industry award — instead of building the consistent cadence of editorial presence that AI engines learn from.

CrowdStrike, Palo Alto Networks, and SentinelOne demonstrate this pattern consistently — their editorial approach prioritizes educational research and data-driven thought leadership over product announcements, which is precisely the type of content AI engines cite. The companies that are winning at Machine Relations in security have figured out what they can say:

Aggregate threat intelligence: Quarterly reports on attack patterns across anonymized datasets. CrowdStrike's annual threat report is one of the most cited security documents across AI engines not because it's a PR play, but because it is genuinely useful editorial content.
Category-defining research: SentinelOne built AI engine authority by consistently publishing threat analyses that helped journalists write informed stories. Every piece of coverage that resulted seeded citations into AI knowledge graphs.
Technical thought leadership: Op-eds and contributed articles by security researchers and founders in Wired, Ars Technica, and MIT Technology Review. These carry high domain authority and AI engine weight.
Educational frameworks: Named methodologies, maturity models, and vendor-agnostic frameworks that security teams actually use. If your framework gets cited in Dark Reading, it gets cited in AI responses to "how should I approach zero-trust architecture."

None of these require disclosing client data or naming vulnerabilities. All of them build the editorial citation graph that Machine Relations relies on.

What a 90-Day Machine Relations Program Looks Like for a Security Company

This is not a general PR program. It's an AI engine authority-building system designed around how security buyers actually discover vendors.

Days 1–30: Research authority foundation

Your first set of placements should establish you as a named source on your core category — not your product, your category. If you build threat intelligence platforms, you should appear as a cited expert in stories about threat intelligence, attack surface management, and SOC automation.

Target: 2–3 placements in DA 70+ outlets where you are quoted or contributed to a story that a security buyer would actually read. Forbes, Business Insider, and TechCrunch for brand credibility; Dark Reading and Security Week for category depth. These placements need to include specific attributed quotes with your name and company, not generic "a security expert said."

Days 31–60: Original research distribution

Publish one piece of original research — anonymized threat data, a benchmark survey, an attack pattern analysis — with enough editorial quality that journalists and AI engines will reference it. Pitch it to three to five journalists who cover your category. Secure two coverage pickups minimum.

This is the highest-leverage activity in a cybersecurity Machine Relations program. Original research with clear methodology and named findings is cited in AI responses at a far higher rate than product announcements or thought leadership essays.

Days 61–90: Technical publication depth

Target trade publications: SC Magazine, Cybersecurity Dive, CSO Online, and relevant SHRM and industry vertical outlets that your buyer's CISO peers actually read. The goal at this stage is depth — enough coverage across specific categories that AI engines begin associating your brand with expert-level knowledge in your category, not just general cybersecurity awareness.

Track AI citation appearances by running your core buyer queries in Perplexity and ChatGPT weekly. The goal is appearing in responses to category-level queries ("what are the best XDR platforms for mid-market companies") before the end of month three.

See how earned media dominates AI search results — the underlying mechanism is the same regardless of vertical, and security companies follow the same pattern with an additional emphasis on research credibility over brand narrative.

The Publications That Drive AI Visibility in Cybersecurity

Not all coverage is equal for Machine Relations. AI engines weight sources based on domain authority, citation patterns, and topical relevance. For cybersecurity companies, the optimal publication mix combines general business credibility (DA 90+) with security-specific depth (high topical authority).

General business DA 90+: Forbes, Business Insider, and Wired carry the highest domain authority signals that AI engines learn from. A single Forbes placement that names you as a security expert will seed your brand into AI responses far more persistently than ten placements in lower-authority outlets.

Technical credibility: TechCrunch and Ars Technica signal to AI engines that you have substance behind the brand story. Security buyers use Ars Technica as a credibility filter — if Ars covered your research, it carries a different signal than a press release republished across 40 newswires.

Category authority: Dark Reading, SC Magazine, and Cybersecurity Dive are the publications that CISOs and security architects actually subscribe to. Coverage here tells AI engines you belong in responses to expert-level security queries, not just general technology searches.

Also worth noting: contributed articles and bylines carry different weight than mentions. When your CTO authors a technical piece in Wired or Dark Reading, it creates a named author-publication association that AI systems index. This is one of the fastest ways to establish your leadership team as named sources in AI-generated security content.

Why Machine Relations Is the Only Moat in Security

Every security company claims better detection, faster response, or lower false positive rates. From an AI engine's perspective, these undifferentiated claims collapse into noise. What AI systems learn from is the citation graph — who covered you, how authoritative those sources are, how consistently your name appears when security buyers search for your category.

This is the moat that Machine Relations builds. It is not fast — three months of consistent placements is table stakes, twelve months is where real AI engine authority compounds — but it is durable. Editorial coverage does not deprecate like ad spend. A Wired piece published in Q1 continues shaping how AI engines respond to security queries in Q3 and beyond.

Traditional PR convinced human gatekeepers. Machine Relations convinces the AI systems that sit between your buyers and the decision to put you on a shortlist. For a security company selling into enterprise buying cycles, that difference is worth more than any campaign.

FAQ

How does Machine Relations differ from traditional cybersecurity PR?

Traditional PR optimizes for human journalists and media placements as an end goal. Machine Relations treats those placements as inputs — the raw material that AI engines like ChatGPT, Perplexity, and Google AI Overviews learn from when answering buyer queries. The goal is not the Forbes article itself, it's what that Forbes article teaches AI systems about your company's category authority.

Which AI tools do cybersecurity buyers actually use to research vendors?

Perplexity has become the primary AI research tool for security professionals because it surfaces real-time, cited sources — which aligns with how security teams evaluate credibility. ChatGPT is widely used for general queries. Google AI Overviews intercept an increasing share of security-related Google searches before buyers ever reach individual websites. All three are influenced primarily by the editorial coverage in high-authority publications.

How long does it take to appear in AI-generated security research responses?

Most companies see initial citation appearances in Perplexity and ChatGPT responses within 60 to 90 days of a consistent earned media program. Owning specific queries — appearing as the default recommendation for your specific category and buyer persona — typically takes 6 to 12 months of sustained placement activity across both general business and security-specific publications.

What types of content drive the most AI citations for security companies?

Original research with named methodology, aggregate threat intelligence data, contributed technical articles in DA 80+ security publications, and founder or CTO bylines in general technology publications (Wired, TechCrunch, Ars Technica). Product announcements and press releases generate minimal AI citation authority regardless of how many outlets republish them.

Can smaller security startups compete with CrowdStrike and Palo Alto Networks in AI search?

Yes — and their advantage is category specificity. AI engines answer specific queries, not brand popularity contests. A Series A identity security company that dominates AI responses to "zero-trust implementation for healthcare" is worth more in that category than being fifth in responses to "best cybersecurity company." Machine Relations strategy for smaller companies should focus on owning specific query clusters rather than competing for general category authority.