The World Technological Sector is in full swing around artificial intelligence (AI) and machine learning (ML).
Companies have understood that they must integrate it into their strategic compass: according to Gartner, by 2027, more than 90 % of new software applications will integrate ML models. If this dazzling progression promises major advances in terms of innovation, it is also accompanied by increasingly strict regulatory supervision. The authorities are now requiring unpublished levels of rigor, transparency and responsibility on the part of the teams in charge of the deployment of AI and machine learning in production. How do governments set up safeguards to supervise the development of these technologies? And how can companies adopt a proactive posture to protect themselves from regulatory risks?
The perception of regulators in the face of AI and machine learning
The development of AI and machine learning progresses at a frantic pace. It is based on massive volumes of data, complex algorithms and continuous training of models, considerably expanding the risk assessment field in terms of software development.
From now on, regulators’ concerns go far beyond the simple quality of code or data security. They examine the agents through the prism of their potentially harmful impacts, their ethical implications and their effects on society. Data confidentiality issues or intellectual property related to model training are at the heart of their concerns, as well as possible drifts: algorithmic biases producing discriminatory or hallucinations generating disinformation.
IA / ML regulations are required worldwide
In Europe, the authorities have already set up a strict regulatory framework supervising the deployment of AI -based solutions. Since August 2, 2025, AI Act has required providers of AI models of strict obligations in terms of transparency, security and documentation. These regulations classify AI systems according to their level of risk, minimal, limited, high or unacceptable, and fixes, for each, specific requirements, going as far as prior certification put into production. Failure to comply with these obligations can lead to heavy financial sanctions, up to 6 % of the world’s annual turnover in the event of a serious offense, or several billion euros for the largest companies.
In France, there is not yet a national law specific to AI. The regulatory framework is mainly based on the application of the European regulation AI Act, which entered into force within the Union on August 1, 2024 and applicable in France since that date. This text requires French companies new obligations in terms of transparency, security and governance for any AI system, in particular those with high impact.
Other countries are following the plunge and in turn adopt their own regulations, often inspired by existing executives such as ACT or the ISO 42001 international standard. This multiplication of compliance requirements creates a new, more fragmented supply chain model, which complicates operations management and considerably increases the administrative burden of companies.
Faced with regulatory pressure, the Mops becomes a strategic imperative
Imagine an ecosystem in which no software version could be put into production without having validated all the required tests. Any derogation would be recorded, drawn, and each validation formally documented in a centralized system. This is precisely what continuous compliance automation allows. By applying checks from design and ensuring compliance with regulations at each stage of the development cycle, up to deployment, it becomes possible to eliminate ad hoc audit verifications.
The evidence from different tools and processes are consolidated within a single source of truth, accessible to all stakeholders: developers, AppSEC teams, security managers, listeners and business departments. It is also a major advantage for ML engineers and data scientists, who benefit from the certainty of working on reliable models, validated and in accordance with the policies in force.
Adopting an integrated approach, based on coherent tools, aligned processes and a controlled production chain, makes it possible to create a confidence environment in which compliance evidence is generated automatically. More and more information security managers (CISO) are increasingly agree: the most effective way to integrate security is to automate it through a structured, robust and reproducible process.
Artificial intelligence, machine learning, LLM, and generative AI have become essential for businesses. With the emergence of agentic AI, it becomes more crucial than ever to adopt a secure and responsible approach in their development. This requires treating traditional issues, vulnerabilities, personal data protection (PII), business risks, while retaining the agility necessary to deal with new threats. To anticipate future regulatory requirements and preserve confidence, organizations must set up rigorous processes, based on traceability, transparency and respect for internal policies, at each stage of the life cycle of IA / ML models.
