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blog – Mobile Security Mentorship & Inspiring Kids to work in tech — 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
0xmurphy.me ↗	67 match 1 shared topics	0xmurphy - Mobile Security Specialists	en	information-and-network-security	robotsllmsaihumans	emailphone	none
mysk.blog ↗	67 match 1 shared topics	Mysk Blog – In-Depth Cybersecurity & Mobile App Privacy Research	en	information-and-network-security	robotsllmsaihumans	emailphone	none
sitefortis.com ↗	66 match 1 shared topics	SiteFortis \| Managed MDM & Industrial Mobile Security	en	information-and-network-security	robotsllmsaihumans	emailphone	none
sitestreamsecurity.com ↗	66 match 1 shared topics	SiteStream \| Advanced Mobile Site Security & Remote Monitoring	en	information-and-network-security	robotsllmsaihumans	emailphone	none
gcored.com ↗	65 match 1 shared topics	Graylog – Centralized Log Management & Security Analytics	United States~ en	information-and-network-security	robotsllmsaihumans	emailphone	none
myoversecured.org ↗	65 match 1 shared topics	Oversecured: Automated Mobile App Security	en	information-and-network-security	robotsllmsaihumans	emailphone	none
crystaltech.co.ke ↗	65 match 1 shared topics	Home - Your Partner in Technology & Security	en	information-and-network-security WordPress WooCommerce	robotsllmsaihumans	emailphone	none
protection-suggestion.com ↗	65 match 1 shared topics	mobile.security	en	information-and-network-security	robotsllmsaihumans	emailphone	none
1appteam.com ↗	65 match 1 shared topics	1APP Team – Bridging Network Apps & Security	en	information-and-network-security WordPress WooCommerce	robotsllmsaihumans	emailphone	none
1accord.net ↗	65 match 1 shared topics	1 Accord Consultting – Information Technology and Cyber Security Consulting	en	information-and-network-security WordPress	robotsllmsaihumans	emailphone	none
1spam.net ↗	65 match 1 shared topics	eSignature, Authentication, Mobile App Security \| OneSpan	en	information-and-network-security Drupal	robotsllmsaihumans	emailphone	none
1spam.org ↗	65 match 1 shared topics	eSignature, Authentication, Mobile App Security \| OneSpan	en	information-and-network-security Drupal	robotsllmsaihumans	emailphone	none
1span.net ↗	65 match 1 shared topics	eSignature, Authentication, Mobile App Security \| OneSpan	en	information-and-network-security Drupal	robotsllmsaihumans	emailphone	none
1span.org ↗	65 match 1 shared topics	eSignature, Authentication, Mobile App Security \| OneSpan	en	information-and-network-security Drupal	robotsllmsaihumans	emailphone	none
attackforge.app ↗	65 match 1 shared topics	AttackForge – Offensive Security Management and Reporting	en	information-and-network-security	robotsllmsaihumans	emailphone	none
attackforge.io ↗	65 match 1 shared topics	AttackForge – Offensive Security Management and Reporting	en	information-and-network-security	robotsllmsaihumans	emailphone	none
1spam.com ↗	65 match 1 shared topics	eSignature, Authentication, Mobile App Security \| OneSpan	en	information-and-network-security Drupal	robotsllmsaihumans	emailphone	none
into-security.com ↗	65 match 1 shared topics	Cybersecurity matters – Into Security	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.