Our commitment
Every page published on ghostshield.ai is intended to be a useful, accurate, and verifiable resource. We separate product copy (which is, by definition, promotional) from research and explanatory content (which should be neutral). We disclose conflicts of interest, mark estimates as estimates, and link to primary sources for factual claims. When we use AI to draft research articles, we say so in a visible byline.
Content classes
Product & pricing
Pages under /pricing, /downloads, and /earn describe what we sell. They are commercial, not editorial. Feature claims correspond to current product behaviour; numerical claims (server count, encryption type, protocol) match what our infrastructure actually runs.
Research & statistics
Pages under /research compile cybersecurity statistics from named sources — industry reports, government publications, academic papers, and our own monitoring data where applicable. Every numerical claim has a sourced citation visible on the page. We update research pages annually or whenever a primary source publishes new data. Datasets are published under CC BY-SA 4.0.
Educational guides
Pages under /learn, the blog, and the country / streaming / unblock / use-case templates explain technology and use cases. These are written to be neutral toward our product — we recommend competitors where appropriate, and we explain when a VPN is not the right tool for a problem.
Tool pages
Pages under /tools are interactive utilities. The result we display is computed in-browser or via a stateless API; we do not log results, do not associate them with your account, and do not retain inputs.
Sourcing standards
The hierarchy we follow for factual claims, strongest first:
- Primary documents — protocol RFCs, court filings, statutes, primary research papers, official disclosures.
- Vendor-published documentation — Netflix, Microsoft, the Tor Project, etc.
- Established journalism — Reuters, AP, WIRED, Bloomberg, the FT, named bylines at TechCrunch / Ars Technica / The Register.
- Industry research from named labs — Citizen Lab, EFF, Cloudflare Radar, Akamai, Cisco Talos.
- Our own infrastructure data — only with the methodology explained on /methodology.
We do not cite anonymous social-media posts as evidence of fact. We do quote them as opinion when attribution is impossible — and we mark them clearly as opinion.
Accuracy and fact-checking
- Every published article passes a 25-point internal quality gate covering grammar, factual claims, source attribution, broken links, image alt text, mobile rendering, schema validity, and AI-assistance disclosure.
- Statistical claims must include a sourced inline citation. “Studies have shown” is not a citation.
- Dates on every research and blog page reflect either the original publish date plus a separate last-updated date — never silently moved.
- Where we make a forward-looking statement (“encryption will likely become quantum-resistant by…”), we mark it as a prediction, attribute the source, and explain what would prove the prediction wrong.
Independence and conflicts of interest
- Comparison pages (e.g., vs NordVPN) list points where competitors beat us alongside points where we beat them. We do not selectively pick categories that favour GhostShield.
- Affiliate links (when we link out to other VPN or security products) carry a visible disclosure on the page.
- Sponsorships are labelled. Editorial control sits with the author, not the sponsor.
- We do not accept paid placement in research or educational content.
AI assistance disclosure
We use large language models (Claude, GPT-class models) to draft research articles and parts of the security blog. AI assistance is disclosed in a visible byline on every page where it is used. Every AI-drafted article passes the same 25-point quality gate, including a human review of every factual claim and source citation. Where AI is used, the byline reads “Drafted by AI, reviewed by [editor name].”
Test methodology
For tested claims (streaming compatibility, speed, leak protection) we publish the test matrix — devices, protocols, regions tested, and dates — on /methodology. Tests are re-run on a published cadence; the “last tested” date appears on each affected page.
Corrections policy
When we publish something wrong, we correct it publicly. A correction note appears at the top of the affected page describing what changed and when. We do not silently rewrite history. Major corrections (changed numerical claims, retracted findings) are listed in our quarterly accuracy review.
If you find an error, email editorial@ghostshield.ai. We aim to respond within 5 business days and to publish a correction within 7 business days of confirming the error.
Reader feedback
We welcome critique. Suggestions for new topics, requests for clarification, and pointers to better primary sources all go to editorial@ghostshield.ai. Feedback that becomes a published correction is acknowledged on the corrected page.
Our editors
Content here is reviewed by named editors with stated expertise and credentials. Author profiles include training, certifications, and a published portfolio:
- Ahmed El Alaoui — AI & Marketing Analytics. Sets research and editorial direction and reviews claims against real data.
- Omar Oumessaoud — Cybersecurity & AI Developer. Reviews technical claims around encryption, protocols, threat detection, and infrastructure.
- Ayoub Boulmeghras — AI Engineer. Reviews AI, machine-learning, and threat-detection content.
- Asmae Misbah — Artificial Intelligence Engineer. Reviews AI/ML and natural-language claims for accuracy.
- Fatima Ezzahra El Azrague — Software Engineer. Reviews application-security and secure-software content.
- Chaymae Belfaik — Software Engineer. Reviews setup guides, troubleshooting, and product how-tos.
Last reviewed
This editorial policy was last reviewed 2026-05-23. We re-review it at least once per year.