The generative AI promises a strong gain in productivity, but it also creates dependence on technologies controlled by few actors, often foreign. What are the real issues?
Generative artificial intelligence deeply upset our digital uses. Software development, task automation, content generation, customer support, threat detection … The promise of a large -scale productivity gain is now tangible. But this technological acceleration is accompanied by another reality, quieter, but just as structuring: the Genai is based on technical, material and cultural foundations concentrated in the hands of a few actors, most often foreign. Now, behind technological dazzling, we have to ask a simple question: what do we want to be dependent?
A technological revolution … and an extension of the attack surface
The integration of generative AI into everyday tools already transforms our way of working. From the developer who generates code to the analyst who formulates hypotheses from textual data, use cases are multiplying at high speed.
But these tools are not neutral. They often interfere with critical systems, manipulate sensitive data, and are based on complex mechanisms that are difficult to audited. This situation creates a new attack surface for cybercriminals. This poses a problem: we integrate a technology that we do not control in our biases, nor in its design choices, or in its intimate functioning. This is not a problem of ethics or morality. It is a problem of mastery, sovereignty, security.
Campaigns like ‘Operation Triangulation’ have proven that even ecosystems deemed hermetic could be infiltrated via sophisticated operating chains. Tomorrow, as the AI is embarked in the bones, apps and critical services, they will become a new brick to be secured – and therefore a potential vector. Which safeguards exist to avoid the leak of confidential information via the outputs generated? Generative AI, by nature, memorizes, learns, extrapola. So many characteristics that make it a great lever … but also a potentially manipulable tool.
A chain of dependence that is difficult to control
Behind each generative model hides a colossal infrastructure, often invisible to the end user. Material (GPU), ML libraries and executives (Tensorflow, Pytorch), Apis, Cloud services … All these components are produced and hosted outside Europe, mainly in the United States, even in China.
This technological concentration leads to a well identified risk in cybersecurity: that of the unique failure point. A breakdown, a unilateral decision, a tariff or contractual development can challenge the same availability of technology for European companies.
The problem is not limited to technical accessibility. It also relates to interoperability, resilience, and the sovereignty of our digital systems. A generative AI used in a critical context (health, energy, defense) should not be fully based on an architecture that we do not control either on the technical or legal level.
A soft algorithmic power
The generative AI is much more than a technical tool. It structures, guides, prioritizes information. In this, it also becomes a vector of influence.
Ask a generative model to explain a geopolitical conflict, to write a legal response or to suggest managerial behavior: in many cases, the responses reflect specific cultural benchmarks, often Anglo-Saxon, or even only American. It may seem trivial, but it is actually a systemic bias.
What generative AI “learn” – from training corpus, moderate content, designers’ decisions – becomes a filter applied to daily uses of millions of users. It is not a plot or maliciousness, but a progressive homogenization effect, where models standardize the responses and representations of the world.
For European organizations, this poses a strategic question: do we want to entrust our production of content, our models of reflection, our decision-making logics to tools that do not reflect our own cultural and regulatory frameworks?
Towards a sovereign, reliable and secure approach of the Genai
The answer can neither be the abandonment of these tools nor their blind adoption. What to build is a reasoned and sovereign capacity for integration. It has its place, even the obligation, in the modernization of cybersecurity operations.
At Gatewatcher, we firmly believe that for the Genai to hold its promises, it must be integrated fluid into the existing environment, while guaranteeing a high level of security and control. This integration should not require radical changes or technological ruptures, but rely on simple, effective solutions adapted to the specific needs of each organization. Our approach is based on a simple and secure solution, allowing teams to interact with all their cyber solutions via a single assistant, while guaranteeing total control over data flows.
Despite everything, to avoid the automatic delegation of reflection or decision-making, it is essential to train users to adopt a critical posture with regard to the results produced by AI. They must ask themselves essential questions: “What is the AI to me neutral?” Is it relevant? Is it in accordance with my standards and adapted to my sector or to my legal framework? By integrating these principles, AI allows teams to focus on high added value tasks, while automating recurring processes and accelerating remediation actions.
