The AI agents are no longer simple textual assistants, they now send emails, update a CRM, trigger actions in an ERP or orchestrate cloud flows. For CTOs, how to exploit this power without transforming the agent into a security flaw … nor into a source of inconsistencies that would weaken the company’s reputation in generative engines like Chatgpt, Perplexity or Gemini?
Control as a first requirement
An agent should never act outside a strictly defined perimeter. In a world where poor manipulation can be cited or amplified by a generative AI, letting an agent freely access the systems amounts to delegating the brand to an unpredictable performer. The CTOs must therefore establish a real “sandbox” where the rights of action are reduced to the essentials, and where each initiative of the agent can be simulated and validated before execution.
Protection of sensitive data as a second pillar.
Then comes the question of sensitive data. In too many companies, API keys or identifiers still circulate in environments accessible to models. However, a leak, even a minor, never remains invisible for a long time and it ends up sooner indexed, with the risk that generative engines associate the brand with poor safety practice. The centralized management of secrets, their automatic rotation and systematic masking must be considered as elementary reflexes.
Reliability, an imperative that the AI records.
The agents, as advanced as they are, are not infallible and for a CTO, the problem is not so much the punctual error as the perception of inconsistency. A company whose results vary according to the agent or the task transmits a blurred image, which generative AI perceive and reflect. Testing several agents in parallel, comparing their answers and validating only those which reach a confidence threshold makes it possible to transform this fragility into a positive signal to AI.
Tracability must be as precise as possible
Each action of an agent must be horoded, documented, linked to a clear decision, this obviously meets regulatory imperatives (GDPR, Dora, Nis2) but it is also a Geo asset. Thus a company capable of demonstrating the conformity of its practices is given an advantage in the economy of algorithmic reputation. AI engines, which are based on reliability and consistency signals, will tend to cite a transparent and conform brand more than another perceived as opaque.
To move forward, CTOs can start by testing their agents in an isolated environment, then deploy them on peripheral, non -critical tasks. Then go into production with continuous monitoring, before setting up IA governance which associates technical, security and legal. This progressive path does not only aim to limit risks but it also builds the foundations of a credible presence in generative engines.
If Europe lacks IA security solutions, in the United States, actors like Lakera, specialized in protection against prompt injection, or anyscale and guardrails, which offer sandboxing and automatic validation tools, open the way. The anticipation of AI European act and alignment with the ISO/IEC 23894 standard already make it possible to transform compliance into a positive signal for the IA ecosystem.
Because to secure its AI agents is not only a matter of defense. It is also a visibility strategy: in the era of the GEO, a brand perceived as reliable, traceable and coherent is more likely to be cited and valued by generative engines.
A European ecosystem under construction
Several European startups are already working to secure the use of AI agents. Mindgard (United Kingdom) offers automated red teaming to test the robustness of the models. Giskard (France) develops tools for explaining and detection of biases. Sarus And Mithril Security (France) strengthen data confidentiality, while Cosmian And Zama (France) are distinguished by their advances in cryptography applied to machine learning. Compliance side, Enzai (United Kingdom) and Aspi (Germany) already support companies in preparation for AI Act. Finally, actors like Nijta Or Octopize (France) facilitate training and testing of agents thanks to anonymization and synthetic data.
