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Semgrep App Security Platform | AI-assisted SAST, SCA and Secrets Detection — 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
semgrep.dev ↗	85 match shared topic	Semgrep App Security Platform \| AI-assisted SAST, SCA and Secrets Detection	en	information-and-network-security	robots llmsaihumans	emailphone	none
quest.com ↗	65 match shared topic	AI Data, Cybersecurity & Platform Modernization \| Quest Software	en	artificial-intelligence Drupal	robotsllmsaihumans	emailphone	none
checkmarx.com ↗	65 match shared topic	Agentic Application Security Testing Software Platform \| Checkmarx - Checkmarx	en	artificial-intelligence WordPress	robots llmsaihumans	emailphone	none
nudgesecurity.com ↗	65 match shared topic	SaaS & AI Security Platform \| SSPM Solution \| Nudge Security	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
bastion.tech ↗	65 match shared topic	Bastion \| AI-native Compliance & AI-ready Security Platform	en	information-and-network-security	robots llmsaihumans	emailphone	partial · 5
spin.ai ↗	65 match shared topic	SaaS Security Solution - AI Data Protection Platform \| Spin.AI	en	information-and-network-security WordPress	robots llmsaihumans	email phone	none
archipelo.com ↗	65 match shared topic	Archipelo - Developer Security Posture Management Platform	en	artificial-intelligence	robotsllmsaihumans	emailphone	none
nowsecure.com ↗	60 match shared topic	The Mobile App Security Experts \| NowSecure	en	information-and-network-security WordPress	robotsllmsaihumans	emailphone	none
doppler.com ↗	60 match shared topic	Doppler \| Secrets management for humans and AI agents	en	information-and-network-security Sanity	robotsllmsaihumans	emailphone	none
opentext.com ↗	55 match shared topic	OpenText \| Secure Information Management for AI	Canada~ en	artificial-intelligence	robotsllmsaihumans	emailphone	none
runtimeverification.com ↗	55 match shared topic	Runtime Verification - Software Assurance for the AI Age	en	information-and-network-security	robotsllmsaihumans	emailphone	partial · 8
whistic.com ↗	55 match shared topic	Third-Party Risk Management & Customer Trust Software \| Whistic	en	information-and-network-security	robotsllmsaihumans	emailphone	none
esentire.com ↗	52 match shared topic	Managed Detection and Response Services & Security Operations Platform \| eSentire	en	information-and-network-security	robots llmsaihumans	email phone	partial · 9
sentinelone.com ↗	47 match shared topic	SentinelOne \| AI-Powered Enterprise Cybersecurity Platform	en	information-and-network-security	robots llmsaihumans	emailphone	none
securitypal.com ↗	47 match shared topic	Cybersecurity Assurance Management Platform: AI + Certified Experts \| SecurityPal AI	United States~ en	information-and-network-security HubSpot CMS	robotsllmsaihumans	emailphone	none
aikido.dev ↗	47 match shared topic	Aikido Security \| Unified Security Platform from Code to Runtime	en	information-and-network-security	robotsllmsaihumans	emailphone	none
forcepoint.com ↗	47 match shared topic	Forcepoint: The #1 AI Data Security Platform	en	information-and-network-security Drupal	robotsllmsaihumans	emailphone	none
darktrace.com ↗	47 match shared topic	Darktrace \| The Essential AI Cybersecurity Platform	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.