Cybernews has published the AI Trustworthiness Ranking, assessing 500 AI companies across four weighted dimensions: security, data privacy, organisational transparency, and public perception. Each company receives a score out of 100. Companies above 75 are designated AI Trustworthiness Leaders.
The findings are sobering for an industry that increasingly handles sensitive business and personal data. Security scored lowest of the four pillars, averaging 32 out of 100 across all assessed companies. Organisational transparency performed best at 90 — largely because basic corporate information is publicly available. Between those two extremes sit data privacy and public perception, with significant variation by sector.
By category, Office and Productivity companies averaged 78 overall, the highest of the 21 sectors covered. Music and Audio ranked last at 54. The top ten most trustworthy companies by the ranking's methodology are Google (Gemini), Krisp, Fireflies.ai, Adobe, Magnific, Writesonic, Veryfi, Salesforce, Grammarly, and Lovable.
The disclosure gap is the detail most relevant to enterprise buyers. 63% of AI companies do not clearly state whether they use customer data to train their models. 65% do not disclose how long they retain user data. For organisations deploying AI tools that touch employee communications, financial data, or customer records, neither omission is acceptable as a baseline.
"AI is shifting from answering questions to taking actions, reading inboxes, moving money, and making decisions on our behalf," said Dr. Akshika Wijesundara, Senior AI Advisor to the UN and member of the Ranking's Advisory Board. "A chatbot that mishandles data is a privacy problem. An agent with broad permissions and weak governance is a security problem, with mistakes propagating through real systems at machine speed."
The ranking is intended to be updated annually and covers sectors from coding assistants and AI research tools to creative and productivity platforms. Its methodology relies on publicly available information — a constraint that limits depth but also reflects what enterprise procurement teams can realistically verify without vendor cooperation.
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