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Fraud Detection & Bot Prevention for SaaS | Trueguard — 18 websites ranked by shared content topics, category and on-page relevance.

Each result shows its full tech stack, contacts and AI-policy — not just a name · Browse all sites in Information And Network Security →

Domain	Match	Title	Country/Lang	Category	AI files	Contact	AI-protection
computedfuture.com ↗	68 match 1 shared topics	Computed Future – AI based network intrusion detection and prevention.	en	information-and-network-security WordPress	robotsllmsaihumans	emailphone	none
mdr247.com ↗	67 match 1 shared topics	Managed Detection & Response 24/7	en	information-and-network-security	robotsllmsaihumans	emailphone	none
123verifi.com ↗	67 match 1 shared topics	123Verifi.com — Fraud Prevention	en	information-and-network-security	robotsllmsaihumans	emailphone	none
1ondizayn.com ↗	67 match 1 shared topics	Bot Detection	en	information-and-network-security	robotsllmsaihumans	emailphone	none
0secure.com ↗	67 match 1 shared topics	ZeroHack \| cybercrime prevention & response	en	information-and-network-security Wix	robotsllmsaihumans	emailphone	none
overridelabsprevention.org ↗	67 match 1 shared topics	Override Labs — Prevention by Design	en	information-and-network-security	robotsllmsaihumans	emailphone	none
ownstrac.com ↗	66 match 1 shared topics	Strac \| Data Loss Prevention, Data Discovery & DSPM for SaaS, Cloud, GenAI	United States~ en	information-and-network-security Webflow	robotsllmsaihumans	emailphone	none
mbuster.io ↗	66 match 1 shared topics	MBUSTER｜Bot detector - prevent scraping	en	information-and-network-security	robotsllmsaihumans	emailphone	none
alexburst.dev ↗	66 match 1 shared topics	Alex Burst // Detection Engineer	en	information-and-network-security	robotsllmsaihumans	emailphone	none
dguard.ai ↗	66 match 1 shared topics	DGuard – AI-Powered Digital Protection & Security	en	information-and-network-security	robotsllmsaihumans	emailphone	none
digital-safeguard.org ↗	66 match 1 shared topics	Technology-Facilitated Abuse Prevention \| The Digital Safeguard Centre	Australia~ en	information-and-network-security Adobe Experience Manager Spree Commerce	robotsllmsaihumans	emailphone	none
df-f.com ↗	66 match 1 shared topics	Dataflow Forensics \| Mobile Threat Detection for Government	en	information-and-network-security	robotsllmsaihumans	emailphone	none
overwatch-cs.com ↗	66 match 1 shared topics	Overwatch Surveillance Detection	en	information-and-network-security Wix	robots llmsaihumans	emailphone	none
landginternational.net ↗	66 match 1 shared topics	Advanced RF Detection & SCIF Security \| L&G International	en	information-and-network-security WordPress WooCommerce	robotsllmsaihumans	email phone	none
24metrics.com ↗	66 match 1 shared topics	Realtime Click and Affiliate Fraud Prevention	en	information-and-network-security WordPress	robots llmsaihumans	emailphone	none
cscif.org ↗	66 match 1 shared topics	CSCIF - Cyber Security Consultation & Information Foundation	en	information-and-network-security	robotsllmsaihumans	emailphone	none
altcha.org ↗	66 match 1 shared topics	Privacy-First Captcha and Bot Protection \| ALTCHA	en	information-and-network-security	robotsllmsaihumans	emailphone	none
crystalintelligence.app ↗	66 match 1 shared topics	Crystal Intelligence - Blockchain Intelligence for Crime Detection	en	information-and-network-security	robotsllmsaihumans	emailphone	none

How the match score works

Each match is a 0–100 similarity score — the higher it is, the more two sites resemble one another. It’s computed automatically from our own crawl data (never from what a site says about itself) by combining several independent signals, so a high score means several of them point the same way:

shared topics how many of the same fine‑grained content topics (IAB sub‑categories) both sites cover — the main signal.
same category whether they sit in the same business / industry category.
content & title how closely their titles and page descriptions line up in wording and meaning.

No single signal decides the result — they’re blended together. Treat the score as a way to rank candidates rather than an absolute percentage; the chips on each result show which signals contributed.