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Nightfall: AI Data Security & Data Loss Prevention Platform — 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
nightfall.ai ↗	100 match 2 shared topics	Nightfall: AI Data Security & Data Loss Prevention Platform	en	information-and-network-security	robots llmsaihumans	emailphone	none
relyance.ai ↗	74 match 2 shared topics	AI data security platform	en	information-and-network-security	robotsllmsaihumans	emailphone	none
mailspree.xyz ↗	72 match 2 shared topics	Wald: AI Data Loss Prevention (AI DLP) and Secure LLM Access for Enterprises	en	artificial-intelligence	robotsllmsaihumans	emailphone	partial · 8
marketvaulthub.xyz ↗	72 match 2 shared topics	Wald: AI Data Loss Prevention (AI DLP) and Secure LLM Access for Enterprises	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
marketgrooves.xyz ↗	72 match 2 shared topics	Wald: AI Data Loss Prevention (AI DLP) and Secure LLM Access for Enterprises	en	artificial-intelligence	robotsllmsaihumans	emailphone	partial · 8
marketfuses.xyz ↗	72 match 2 shared topics	Wald: AI Data Loss Prevention (AI DLP) and Secure LLM Access for Enterprises	en	artificial-intelligence	robotsllmsaihumans	emailphone	partial · 8
marketfuse.online ↗	72 match 2 shared topics	Wald: AI Data Loss Prevention (AI DLP) and Secure LLM Access for Enterprises	en	artificial-intelligence	robotsllmsaihumans	emailphone	partial · 8
marketnavis.xyz ↗	72 match 2 shared topics	Wald: AI Data Loss Prevention (AI DLP) and Secure LLM Access for Enterprises	en	artificial-intelligence	robotsllmsaihumans	emailphone	partial · 8
markettrailpath.xyz ↗	72 match 2 shared topics	Wald: AI Data Loss Prevention (AI DLP) and Secure LLM Access for Enterprises	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
marketqubes.xyz ↗	72 match 2 shared topics	Wald: AI Data Loss Prevention (AI DLP) and Secure LLM Access for Enterprises	en	artificial-intelligence	robotsllmsaihumans	emailphone	partial · 8
cyera.io ↗	72 match 2 shared topics	AI Security Platform \| Protect Data & Secure AI \| Cyera	en	information-and-network-security	robotsllmsaihumans	emailphone	none
manta.red ↗	71 match 2 shared topics	Manta Security - AI Agent Red Team Platform	en	information-and-network-security	robotsllmsaihumans	emailphone	none
backslash.security ↗	70 match 2 shared topics	Backslash \| Agentic AI Endpoint Security Platform	en	information-and-network-security Webflow	robotsllmsaihumans	emailphone	none
beeliti.com ↗	70 match 2 shared topics	Beeliti — AI Security Platform	en	information-and-network-security	robotsllmsaihumans	emailphone	none
getsafety.com ↗	70 match 2 shared topics	Safety - The AI Development Security Platform	en	artificial-intelligence	robots llmsaihumans	emailphone	none
kenzosecurity.com ↗	70 match 2 shared topics	Kenzo - Agentic Security Platform	en	information-and-network-security	robotsllmsaihumans	emailphone	none
kenzosecurity.net ↗	70 match 2 shared topics	Kenzo - Agentic Security Platform	en	information-and-network-security	robotsllmsaihumans	emailphone	none
ai-overwatch.info ↗	70 match 2 shared topics	Cloud Data Security & Privacy Preserving AI \| Anjuna	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.