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MELURNA | Third-Party Privacy Risk Management — 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
detectblackkite.com ↗	74 match 2 shared topics	Black Kite \| Standards-Based Third-Party Cyber Risk Management	United States~ en	information-and-network-security	robots llmsaihumans	emailphone	none
goblackkite.com ↗	74 match 2 shared topics	Black Kite \| Standards-Based Third-Party Cyber Risk Management	United States~ en	information-and-network-security	robots llmsaihumans	emailphone	none
mine-privacy.com ↗	72 match 2 shared topics	Mine \| Autonomous AI Governance, Privacy & Risk Management	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
visotrust.ai ↗	71 match 2 shared topics	VISO TRUST: AI-Powered Third Party Cyber Risk Management	en	information-and-network-security WordPress WooCommerce	robotsllmsaihumans	emailphone	none
aiprivacy.pro ↗	69 match 2 shared topics	AI Privacy Pro	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
simuphish.ai ↗	68 match 2 shared topics	SimuPhish \| The AI Driven Human Risk Management+ Platform	United Kingdom~ en	information-and-network-security	robotsllmsaihumans	emailphone	none
ctrltower.org ↗	68 match 2 shared topics	Ctrl Tower \| The operating system for AI-era risk management	en	information-and-network-security	robotsllmsaihumans	emailphone	none
gocoverbase.com ↗	68 match 2 shared topics	Coverbase - Agentic third-party risk and security	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
above.security ↗	68 match 2 shared topics	Above AI Agents for Proactive Insider Risk Management	en	information-and-network-security	robots llmsaihumans	emailphone	none
dcailaw.com ↗	68 match 2 shared topics	Cybersecurity, AI, and Privacy	United States~ en	information-and-network-security Squarespace Magento	robotsllmsaihumans	emailphone	none
dcailawyer.com ↗	68 match 2 shared topics	Cybersecurity, AI, and Privacy	United States~ en	information-and-network-security Squarespace Magento	robotsllmsaihumans	emailphone	none
dccyberlawyer.com ↗	68 match 2 shared topics	Cybersecurity, AI, and Privacy	United States~ en	information-and-network-security Squarespace Magento	robotsllmsaihumans	emailphone	none
deploycytex.com ↗	67 match 2 shared topics	Leading Cybersecurity Solutions & Risk Management \| Cytex.io	en	information-and-network-security WordPress	robotsllmsaihumans	email phone	none
gocytex.com ↗	67 match 2 shared topics	Leading Cybersecurity Solutions & Risk Management \| Cytex.io	en	information-and-network-security WordPress	robotsllmsaihumans	email phone	none
anotic.xyz ↗	67 match 2 shared topics	Anotic SaaS \| PrivacyShield	en	information-and-network-security	robotsllmsaihumans	emailphone	none
annchang.blog ↗	66 match 2 shared topics	CYBERSECURITY AI & RISK MANAGEMENT – Field Insights and Real-world Experiences	en	information-and-network-security WordPress	robotsllmsaihumans	emailphone	none
vulnerability.management ↗	66 match 2 shared topics	AI vulnerability management	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
vypersec.io ↗	66 match 2 shared topics	AI-Native Cyber Service Management \| Treat	en	information-and-network-security WordPress	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.