similar sites

Browse

AI Report Tools

Sites similar to lumos.com

Lumos alternatives & similar sites

Identity Management for the Agentic Era | Lumos — 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
demipass.com ↗	76 match 2 shared topics	DemiPass — Secrets management for the agentic era	en	information-and-network-security	robotsllmsaihumans	emailphone	none
0run.net ↗	72 match 2 shared topics	runZero: Exposure Management for the AI-attack era	en	information-and-network-security	robotsllmsaihumans	emailphone	none
0run.org ↗	72 match 2 shared topics	runZero: Exposure Management for the AI-attack era	en	information-and-network-security	robotsllmsaihumans	emailphone	none
discoveraembit.org ↗	72 match 2 shared topics	Aembit \| Agentic AI and Workload Identity & Access Management	en	information-and-network-security WordPress Lightspeed	robotsllmsaihumans	emailphone	none
useaembit.com ↗	72 match 2 shared topics	Aembit \| Agentic AI and Workload Identity & Access Management	en	information-and-network-security WordPress Lightspeed	robotsllmsaihumans	emailphone	none
demoaembit.com ↗	72 match 2 shared topics	Aembit \| Agentic AI and Workload Identity & Access Management	en	information-and-network-security WordPress	robotsllmsaihumans	emailphone	none
joinaembit.com ↗	72 match 2 shared topics	Aembit \| Agentic AI and Workload Identity & Access Management	en	information-and-network-security WordPress Lightspeed	robotsllmsaihumans	emailphone	none
newcore.com ↗	71 match 2 shared topics	NewCore \| Identity Security for Humans and AI Agents	en	information-and-network-security	robotsllmsaihumans	emailphone	partial · 7
cavexia.ai ↗	71 match 2 shared topics	Cavexia — Security for the agentic stack	US~ en	information-and-network-security	robots llms ai humans	emailphone	none
cavexia.com ↗	71 match 2 shared topics	Cavexia — Security for the agentic stack	US~ en	information-and-network-security	robots llmsaihumans	emailphone	none
usesense.ai ↗	71 match 2 shared topics	Identity Verification & KYC for the AI Era \| Sense	en	information-and-network-security	robotsllmsaihumans	emailphone	partial · 8
descope.com ↗	70 match 2 shared topics	Descope \| Customer and Agentic Identity Platform	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
join-pushsecurity.com ↗	70 match 2 shared topics	Browser Security for the AI Era \| Push Security	en	information-and-network-security	robotsllmsaihumans	emailphone	none
joinpushsecurityteam.com ↗	70 match 2 shared topics	Browser Security for the AI Era \| Push Security	en	information-and-network-security	robotsllmsaihumans	emailphone	none
joinpushsecurity-team.com ↗	70 match 2 shared topics	Browser Security for the AI Era \| Push Security	en	information-and-network-security	robotsllmsaihumans	emailphone	none
usevigil.dev ↗	69 match 2 shared topics	Agent Auth by Vigil — Identity for AI Agents	en	information-and-network-security	robots llmsaihumans	emailphone	partial · 8
basedagents.ai ↗	69 match 2 shared topics	BasedAgents — Identity for AI Agents	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
usekeygraph.com ↗	69 match 2 shared topics	Keygraph \| Continuous Agentic Pentesting for the Enterprise	en	information-and-network-security	robots llmsaihumans	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.