Assessing Risks Related to Generative AI Models in Regulated Environments

Generative artificial intelligence (AI) has ceased to be a futuristic concept and has become a tangible transformative force in the business world. These AI systems, capable of creating original content, open unprecedented avenues for innovation. However, their rapid deployment raises critical questions concerning security, data protection, and compliance, especially within an increasingly complex regulatory environment. For companies, understanding and assessing AI-related risks is no longer optional but a strategic necessity to ensure sustainable and responsible development. This article proposes a comprehensive methodology to identify, analyze, and mitigate the inherent risks of generative AI systems, ensuring their implementation is safe and compliant.

Introduction: The Stakes of Generative AI in a Regulated Context

The rise of generative artificial intelligence represents a true revolution, offering powerful tools that redefine creation and innovation processes. Nevertheless, the use of these AI systems is not without risks. The challenges are numerous and strike at the very heart of company operations: from data management to legal liability and intellectual property protection. One of the main challenges lies in complying with an evolving regulatory framework designed to govern artificial intelligence and protect fundamental human rights. Navigating this complex environment requires a proactive risk management approach for any business seeking to leverage generative AI without exposing itself to sanctions or reputational damage.

Definition of Generative AI and Its Applications

Generative AI is a branch of artificial intelligence focused on creating new content. Unlike traditional AI systems that analyze or classify existing information, generative AI models produce original data that can take the form of text, images, music, or computer code. These systems learn from vast datasets to then generate unique and relevant outputs.
Applications in business are broad and rapidly expanding:

• Marketing content creation: writing blog articles, social media posts, or video scripts.
• Software development: generating code, assisting with debugging, and creating technical documentation.
• Design and artistic creation: designing logos, illustrations, or product mockups.
• Customer support: deploying advanced chatbots capable of naturally understanding and responding to user queries.
• Healthcare: assisting diagnosis by generating reports or analyzing medical images.
  Although powerful, these tools depend on complex models whose use must be carefully managed to avoid inherent risks.

  The Regulatory Context

  In 2025, the regulatory environment surrounding artificial intelligence has been significantly strengthened, with the European Union leading the way. The goal is clear: to foster innovation while ensuring that AI systems placed on the European market are safe and respect fundamental rights.
  The European Artificial Intelligence Regulation (AI Act)
  Phased in since 2024, the AI Act is the world’s first comprehensive regulatory framework for artificial intelligence. Its philosophy is based on a risk-tiered approach, classifying AI systems into four categories:

1. Unacceptable risk: These AI systems are deemed contrary to the European Union’s values and are therefore banned. This includes, for example, government-run social scoring systems.
2. High risk: This category covers AI systems used in critical fields where they can significantly impact health, safety, or fundamental rights (e.g., recruitment, medical diagnosis, credit granting). Such systems are subject to very stringent requirements regarding risk management, data governance, transparency, human oversight, and cybersecurity throughout their life cycle.
3. Limited risk: For these systems (such as chatbots), the primary requirement is transparency. Users must be clearly informed they are interacting with artificial intelligence.
4. Minimal risk: The vast majority of AI systems fall into this category (e.g., spam filters, video games) and are not subject to additional obligations.
   This regulation has extraterritorial reach, meaning that any company, even non-European, must comply if its AI systems are used within the European Union.
   GDPR and personal data protection
   The General Data Protection Regulation (GDPR) remains a cornerstone of regulation. Generative AI models are often trained on huge volumes of data scraped from the internet, which may contain personal data. Compliance with GDPR is therefore a major concern. Companies must ensure they have a valid legal basis for processing this data, respect principles of data minimization and purpose limitation, and maintain transparency toward data subjects. The interplay between the AI Act and GDPR is central to compliance strategy.
   Other relevant sectoral regulations
   Beyond these two major regulations, sector-specific rules continue to apply. In finance, healthcare, or transportation, for example, additional requirements regarding security, model validation, and liability may be imposed by national supervisory authorities, such as the ACPR in France for the financial sector. Companies must thus perform a comprehensive analysis to identify the full legal framework applicable to their AI uses.

   Identification of Risks Related to Generative AI Models

   Risk assessment is the starting point for any compliance initiative. The risks associated with generative AI are multifaceted and require careful analysis. Precisely identifying each potential risk is a crucial step in the management cycle.

   Risks Related to Personal Data Protection

   Data management lies at the core of risks tied to generative AI systems. These systems are data-hungry, and their massive data processing generates significant privacy risks.
   Data collection, processing, and storage
   The first risk concerns the legality of training data collection. Using massive volumes of web-collected data without verifying origins or obtaining individuals’ consent can constitute a direct GDPR violation. Furthermore, storing these vast datasets poses major security challenges. Robust measures must be implemented to ensure confidentiality and the integrity of this information.
   Risks of data breaches and information leaks
   Centralized training databases are prime targets for cyberattacks. A more subtle risk is “regurgitation leaks”: an AI model may generate content that verbatim reveals sensitive personal data contained in its training set. Similarly, confidential company information (prompts) entered by users into a third-party AI system could be used to train future models, creating a risk of strategic information leakage.
   Consent and right to erasure
   GDPR grants individuals rights over their data, including consent and the right to erasure (“right to be forgotten”). For generative AI models, applying these rights is extremely complex. How to obtain consent from millions of people whose data was used for training? How to erase specific information once it has been encoded within the model’s “weights”? This is a major technical and legal challenge exposing companies to non-compliance risks.

   Intellectual Property Risks

   The use of generative AI systems raises new, complex issues around intellectual property. These risks must be evaluated to protect company assets and avoid litigation.
   Copyright and related rights
   A significant risk arises when AI systems are trained on copyrighted materials (texts, images, code) without authorization from rights holders. This can expose companies that develop or use such systems to infringement lawsuits. Legal frameworks around “Text and Data Mining” (TDM) provide certain exceptions, but their scope remains under debate.
   Patents and trade secrets
   The risk of disclosing trade secrets is high. If an engineer uses a public generative AI service to optimize proprietary algorithms or draft parts of a patent application under development, this confidential information could be captured and reused by the AI service provider, destroying its value. A strict usage policy for these tools is therefore indispensable.
   Generated content and usage rights
   The question of ownership over AI-generated content is another point of contention. Who is the author: the user who wrote the prompt, the company that developed the AI, or the AI itself? Most jurisdictions do not grant copyright protection to works without human creative involvement. Companies must thus exercise caution regarding protection of content they generate via these tools and thoroughly review service terms of use.

   Security and Reliability Risks

   The performance of an AI system is not only measured by the quality of its outputs, but also by its robustness and security. Assessing these risks is fundamental to ensure user trust.
   Algorithmic bias and discrimination
   AI systems learn from the data they are provided. If this data reflects societal biases (social, racial, gender), the AI will reproduce and amplify them. For example, a recruitment aid system trained on historical data might systematically discriminate against certain profiles. The risk of engendering discriminatory decisions is a major ethical and legal issue directly targeted by the AI Act for high-risk systems.
   Vulnerability to malicious attacks
   AI models face specific attack types. “Data poisoning” involves injecting poisoned data into the training set to corrupt the model. “Prompt injection” aims to manipulate instructions given to AI to generate malicious content, circumvent security filters, or disclose confidential information. Ensuring AI system security is an ongoing effort.
   Reliability and accuracy of results
   One of the best-known risks of generative AI is “hallucination,” whereby the model produces factually incorrect information confidently presented. Relying on unverified outputs for important decisions can have serious consequences. It is imperative to implement human oversight processes to validate the relevance and accuracy of generated content, especially in professional environments.

   Liability and Transparency Risks

   Deploying complex AI systems redefines responsibility chains and increases the need for transparency to guarantee fair and equitable use.
   Liability in case of error or damage
   If an AI system makes a mistake causing harm (for example, bad legal or medical advice), who is responsible? The model developer, the company deploying the system, or the end user? The current legal uncertainty makes assigning liability complex. The AI Act begins to clarify the obligations of different actors, but rigorous contractual risk management is necessary.
   Transparency and explainability of algorithmic decisions
   Many AI models, especially deep learning-based ones, operate as “black boxes.” It is difficult to precisely explain how a decision was made. This lack of transparency is problematic because the AI Act requires explainability for high-risk systems so decisions can be understood and contested. Transparency is a pillar of trust.
   Responsibility of the involved parties (developers, users)
   Liability is shared throughout the AI lifecycle. Providers must guarantee the compliance of their high-risk AI systems. Companies deploying these systems (“deployers”) are obliged to use them according to instructions, ensure human oversight, and monitor their operation. End users must also be trained on these tools’ limitations.

   Risk Assessment Methodology

   Once risks are identified, they must be evaluated in a structured way. This methodology helps prioritize mitigation actions and allocate resources effectively.

   Data Protection Impact Assessment (DPIA)

   For AI systems processing personal data at scale or in sensitive ways, conducting a Data Protection Impact Assessment (DPIA) is often a legal obligation under GDPR. This process aims to:
5. Describe the intended data processing.
6. Assess its necessity and proportionality.
7. Identify and evaluate risks to the rights and freedoms of data subjects.
8. Define measures to mitigate these risks.
   The DPIA is a fundamental tool for managing privacy-related risks.

   Assessment of Intellectual Property Risks

   This specific evaluation should include an audit of training data to ensure it is either royalty-free or used under an appropriate license. It is also necessary to define a clear internal policy on employees’ use of generative AI tools, forbidding, for example, the injection of confidential or trade secret information.

   Tests and Simulations to Evaluate Reliability and Security

   It is crucial not to rely solely on vendors’ claims. Companies must actively test AI systems. This includes robustness testing to assess resilience to failures; cybersecurity testing (red teaming, penetration testing) to identify vulnerabilities; and bias testing to detect and measure potential discriminatory behavior before deployment.

   Analysis of Potential Impacts on Liability and Transparency

   This analysis involves mapping all actors involved in the AI system’s lifecycle and clarifying their respective roles and responsibilities, ideally through contracts. It is also necessary to evaluate whether the system’s transparency level meets the AI Act’s requirements, particularly for high-risk systems, and whether human control measures are effectively in place.

   Risk Mitigation and Best Practices

   Risk assessment must lead to a concrete action plan. Mitigation involves a combination of technical, organizational, and ethical measures.

   Implementation of Technical and Organizational Security Measures

   Security must be integrated from the design phase (“security by design”). This includes technical measures such as data encryption, strict access management, and logging activities to enable auditing. On the organizational side, it is vital to train teams, define usage charters, and establish incident response processes.

   Definition of Clear Processes for Data and Intellectual Property Management

   Robust data governance is imperative. This involves documenting the origin of training data, managing its lifecycle, and ensuring its quality. A process must also be set up to handle GDPR rights requests. Similarly, a clear intellectual property policy governing tool usage and generated content management is indispensable.

   Development and Implementation of Ethical Policies

   Legal compliance is a baseline, not an end in itself. Forward-thinking companies go further by developing an ethical AI charter. This charter sets out principles the company commits to uphold (fairness, transparency, responsibility) and guides the development and deployment of all AI systems, thereby strengthening trust among customers and employees.

   Compliance and Collaboration with Regulatory Authorities

   Compliance is not a one-time project but an ongoing process. The regulatory and technological environments evolve rapidly. It is therefore crucial to maintain active legal and technical monitoring. Establishing constructive and transparent dialogue with regulatory bodies, such as the CNIL in France, helps anticipate changes and demonstrate good-faith compliance efforts.

   Conclusion: Toward Responsible and Safe Generative AI

   Generative AI offers an extraordinary opportunity for innovation and competitiveness for businesses. However, its adoption cannot come at the expense of security, ethics, and respect for fundamental rights. Risk assessment and management must not be seen as obstacles but as catalysts for trust and sustainable development.

   The Importance of a Proactive and Preventive Approach

   Adopting a proactive and preventive approach is the only way to navigate this complex environment with confidence. Waiting for an incident to occur or a sanction to be imposed is a risky strategy. By integrating risk management early in the AI project lifecycle, companies can innovate more safely and responsibly, turning regulatory constraints into competitive advantages.

   Collaboration and Sharing of Best Practices

   Given the complexity of challenges posed by artificial intelligence, collaboration is essential. Sharing best practices between companies, sectors, and academia accelerates the emergence of standards and common solutions. By joining forces, economic actors can build a robust and reliable AI ecosystem.

   The Ever-Evolving Nature of Regulation and Associated Risks

   The journey toward trustworthy AI is just beginning. AI system technology evolves at a breathtaking pace, and the regulatory framework will continue to adapt. Today’s risks might not be tomorrow’s. Constant agility and vigilance are thus required so companies can continue innovating while mastering the risks related to artificial intelligence.

   Additional Resources

   To deepen your understanding of the challenges and solutions, here is a selection of useful resources.

   Links to Regulatory Texts and Practical Guides

• Official text of the European Union AI Act
• CNIL website: Artificial Intelligence dossier
• Guidelines from the European Data Protection Board (EDPB)

  Useful Resources on AI Risk Assessment and Security

• ENISA (European Union Agency for Cybersecurity) publications on AI security
• AI Risk Management Framework by NIST (National Institute of Standards and Technology)

  Links to Organizations and Experts in Responsible AI

• CNIL’s Digital Innovation Lab (LINC)
• Hub France IA
• Institut Montaigne – AI & Society Program