Cloud LLMs, the most powerful tool ever created… and a Trojan horse that exposes companies that do not understand its mechanisms.
LLMs (Large Language Models) in cloud mode, like those accessible via API at OpenAI, Anthropic or their competitors, today represent the most widespread form of generative AI in business. It is essential to clearly distinguish these models hosted in Anthropic’s OpenAI cloud from other approaches: AI engines deployed and hosted internally (on-premise or private cloud) and which use data which remains strictly within the scope of the company, or even specialized AI models (vision, predictive analysis, etc.) which are not based on the same paradigm (see: AI: alternatives to LLM for process performance). Only general cloud LLMs are at the heart of this article, because they concentrate the risks of data capture and economic asymmetry.
An asymmetrical and unverifiable business model
These cloud LLMs are offered via pay-per-use (tokens). Businesses see it as a quick way to increase productivity: content generation, data analysis, code assistance, document summarization, automation of complex tasks, etc.
However, this model presents a major asymmetry. Enterprise offers contractually promise not to train their models on your data. But who controls the application of this promise? No client has audit rights on the training corpora. There is no technical means to verify what is – or is not – included in the next version of the model. Protection relies entirely on the good faith of the supplier and on configuration parameters that few companies really master. Add extra uses: free personal accounts, where training on your prompts is this time activated by default. The asymmetry is total: you pay, you expose yourself, and you cannot verify anything.
If these LLMs generated massive, constant and sustainable value for the user company, we would expect billing models aligned with results (gain sharing, equity, etc.). The choice of token invoicing reveals a preference for predictable income and the continuous capture of intellectual value.
Added value: often overestimated, sometimes negative
Beyond the exposure of data, the intensive use of cloud LLMs frequently produces workslop: mediocre content, undetected hallucinations, superficial analyses. When employees rely excessively on the tool without critical thinking, they gradually lose essential skills: in-depth reasoning, rigorous verification, detailed business expertise.
The result is an apparent productivity that masks a real erosion of organizational capabilities (see: We see AI everywhere, except in business productivity). Companies that treat cloud LLM like a magic button rather than a powerful but demanding tool are digging their own grave.
The three main dangers
1. The absence of clear AI governance
Few companies have defined precise rules: which documents or information can be sent to a cloud LLM, which must remain internal, what are the protocols for mandatory human verification, prompt auditing, or classification of sensitive data (see: 3 things you should never trust to AI). Without structured governance, the risk of leaks and operational chaos become inevitable.
2. Misuse by employees
Overconfidence, intellectual laziness, unintentional distribution of confidential data in insecure prompts. AI amplifies human errors and organizational biases on a massive scale. According to the National Cybersecurity Alliance (September 2024), nearly 40% of employees admit to having shared sensitive information with AI tools without their employer’s knowledge. And the phenomenon is accelerating: according to Cyberhaven’s AI Adoption and Risk Report, the share of sensitive data in what employees entrust to AI tools increased from 10.7% in 2023 to 27.4% in 2024, reaching 34.8% today.
3. Leaks via the partner ecosystem
Regulated professions and civil servants are bound by professional secrecy. But these new technologies, their risks, which add an unprecedented degree of complexity to their profession, are they understood in their subtlety by these great professionals. What guarantee do you have of their good practices?
When we paste your shareholders’ agreement into a cloud LLM to draw a summary and cross-reference the information with other documents, within what contractual scope is this done, under what configuration, with what account? Nobody knows – and above all, nobody asks. Your confidentiality agreements predate generative AI. Do your economic partners – including regulated professions and public authorities – have an obligation to report to you on their AI practices? Do they use cloud LLMs?
This is the weak link that no one addresses: you can have the best internal governance in the world, your strategic information already circulates in areas that you do not control.
Towards real AI sovereignty
L’artificial intelligenceand particularly LLMs, is probably the most powerful tool ever invented by humanity. It can radically transform productivity, innovation and the quality of decision-making.
But like any powerful technology, it severely penalizes those who use it without mastery. Companies that do not understand the specific pitfalls of cloud LLMs – unverifiable data exposure, blind overconfidence, lack of control over the ecosystem – risk being left behind, or even permanently weakened.
Those who succeed will be those who build AI sovereignty: rigorous governance, informed choice between cloud and internal solutions (on-premise or controlled open-weight models), continuous training of teams, AI clauses in partner contracts, and implementation of verification and knowledge capitalization processes. See also: AI in business: the real risk is letting go.
Time is running out. AI does not forgive strategic naivety.
