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Aiidentityprotocol alternatives & similar sites

AI Identity Protocol — 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 Artificial Intelligence →

Domain	Match	Title	Country/Lang	Category	AI files	Contact	AI-protection
4tress.org ↗	69 match 2 shared topics	4tress.org — Decentralized Identity for AI Agents	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
incode.com ↗	68 match 2 shared topics	AI-powered Identity Verification \| Incode	en	information-and-network-security	robots llmsaihumans	emailphone	none
inaisec.com ↗	67 match 2 shared topics	InaiSec - Identity-First Incident Response	en	information-and-network-security	robots llmsaihumans	emailphone	none
inaisec.ai ↗	67 match 2 shared topics	InaiSec - Identity-First Incident Response	en	information-and-network-security	robots llmsaihumans	emailphone	none
0byte.tech ↗	67 match 2 shared topics	0byte - The Origin Protocol for AI Content	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
onmero.com ↗	67 match 2 shared topics	Onmero International \| AI-Powered Identity & Security	en	information-and-network-security	robotsllmsaihumans	email phone	none
aegisdefi.fun ↗	67 match 2 shared topics	Aegis Protocol — AI Agent Security for DeFi	en	information-and-network-security	robotsllmsaihumans	emailphone	none
1id.com ↗	66 match 2 shared topics	1id.com — Hardware-Backed Identity for AI Agents & Relying Parties	en	information-and-network-security	robots llmsaihumans	emailphone	none
imsecure.ai ↗	66 match 2 shared topics	IM Secure™ – Identify. Secure. Protect.	en	information-and-network-security WordPress WooCommerce	robotsllmsaihumans	emailphone	none
accesssecurden.info ↗	66 match 2 shared topics	Securden – The World’s First Unified Identity Security Platform \| Unifying PAM, EPM, IGA, CIEM & AI	en	information-and-network-security	robots llmsaihumans	emailphone	partial · 8
topsecurden.com ↗	66 match 2 shared topics	Securden – The World’s First Unified Identity Security Platform \| Unifying PAM, EPM, IGA, CIEM & AI	en	information-and-network-security	robots llmsaihumans	emailphone	none
astrix.security ↗	65 match 2 shared topics	Identity Security for AI Agents & NHIs \| Astrix Security	en	information-and-network-security WordPress	robots llmsaihumans	emailphone	none
cordialabs.io ↗	65 match 2 shared topics	LetsPing \| Secure your AI agents. Firewall, HITL and agent identity.	en	information-and-network-security	robotsllmsaihumans	emailphone	none
incountry.com ↗	64 match 2 shared topics	Data residency and AI data protection - InCountry AgentCloak	en	artificial-intelligence WordPress	robotsllmsaihumans	emailphone	none
aevum.build ↗	64 match 2 shared topics	Aevum	en	information-and-network-security	robots llmsaihumans	emailphone	none
incognia.com ↗	64 match 2 shared topics	Incognia \| Identify Users vs. Fraudsters. Across devices.	United States~ en	information-and-network-security HubSpot	robotsllmsaihumans	emailphone	none
incognia.ai ↗	64 match 2 shared topics	Incognia \| Identify Users vs. Fraudsters. Across devices.	United States~ en	information-and-network-security HubSpot	robotsllmsaihumans	emailphone	none
42crunch.com ↗	63 match 2 shared topics	Automate API Security across Agentic Workflows	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.