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PhasmaSec — Penetration Testing, AI Red Teaming & Security Consulting — 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
mind-gard.com ↗	76 match 2 shared topics	Mindgard - Automated AI Red Teaming & Security Testing	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
mindgard.ai ↗	76 match 2 shared topics	Mindgard - Automated AI Red Teaming & Security Testing	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
cybersage.solutions ↗	74 match 2 shared topics	CyberSage \| AI-Powered Penetration Testing in Ireland	en	information-and-network-security	robotsllmsaihumans	emailphone	partial · 6
shellvoide.com ↗	73 match 2 shared topics	Shellvoide - AI-Powered Cybersecurity & Penetration Testing \| Certified Security	en	information-and-network-security	robotsllmsaihumans	email phone	none
cedrusstrategic.com ↗	72 match 2 shared topics	Cedrus Strategic - Cybersecurity Consulting & AI Security	en	information-and-network-security	robotsllmsaihumans	emailphone	none
360sec.com ↗	71 match 2 shared topics	AI-Powered Cybersecurity Services — MDR, Red Teaming & AI Security Testing \| 360 Security Group	United States~ en	information-and-network-security	robotsllmsaihumans	emailphone	none
sergiodn.com ↗	71 match 2 shared topics	Security and AI consulting — sergiodn	en	artificial-intelligence	robotsllmsaihumans	emailphone	partial · 8
nullscan.io ↗	71 match 2 shared topics	Nullscan - AI-Powered Penetration Testing	en	information-and-network-security	robots llmsaihumans	emailphone	none
2thrivellc.com ↗	71 match 2 shared topics	Cyber Security and AI Consulting	en	information-and-network-security	robotsllmsaihumans	emailphone	none
mirayaai.com ↗	71 match 2 shared topics	MirayaAI \| Offensive Cybersecurity Consulting	en	information-and-network-security	robotsllmsaihumans	emailphone	none
cybermassive.tech ↗	70 match 2 shared topics	CyberMassive \| Cybersecurity Consulting for Startups & AI Companies	en	information-and-network-security	robotsllmsaihumans	emailphone	none
0xclaw.dev ↗	70 match 2 shared topics	Local AI pentesting for security teams	en	information-and-network-security	robots llmsaihumans	emailphone	partial · 7
phantomred.com ↗	70 match 2 shared topics	PhantomRed — Autonomous Penetration Testing Platform	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
trytontech.com ↗	69 match 2 shared topics	Tryton Technologies \| AI, Cybersecurity & Automation Consulting	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
askmeshi.com ↗	69 match 2 shared topics	Security Breacher – AI Red Team on Demand	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
oasec.org ↗	69 match 2 shared topics	OASec 2026 — AI Safety & Security Conference \| Singapore, 28 September 2026	Singapore~ en	information-and-network-security	robotsllmsaihumans	emailphone	none
sforensics.com ↗	69 match 2 shared topics	AI-Powered Penetration Testing \| Sherlock Forensics	Canada~ en	information-and-network-security	robots llmsaihumans	email phone	none
gsconsultingllc.com ↗	69 match 2 shared topics	GS Consulting \| Secure AI, Cybersecurity, Software & Data Solutions	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.