The Gifts of Generative AI: What to Use, What to Avoid
The gifts of generative AI: mistakes to avoid and best practices
Generative AI is establishing itself today in businesses and even daily life. Capable of writing, analyzing and structuring information in seconds, it offers immense potential. But this power also carries major risks: compromised confidentiality, factual hallucinations, discriminatory biases, regulatory non-compliance.
For responsible use, it is crucial to know the “don'ts” : these prohibited or strongly discouraged practices which expose you to legal, ethical and operational risks. This article details the most common mistakes and associated best practices.
Verification and reliability: avoiding blind trust
Generative models sometimes produce false responses, called hallucinations:
Do not publish generated responses without proofreading and verifying sources
Don't assume that AI knows the news in real time: it can provide outdated or made-up data
Do not ask the AI to draft binding legal documents without proofreading by an attorney
Example: an American lawyer presented a brief in court containing references to case law invented by ChatGPT. Result: disciplinary sanctions and damage to credibility.
Confidentiality and sensitive data: donations to respect
Loss of confidentiality is the first pitfall to avoid. Generative AI models must not become an unintentional channel for data leaks.
Do not enter personal data of a third party without their explicit consent. → Risk of privacy violations and GDPR sanctions
Do not paste confidential contracts, company secrets, or proprietary source code into an external service. → Loss of control over intellectual property rights
Do not store contractually protected data (e.g. customer data) in the AI tool without clear agreement and protection measures (encryption, on-premise hosting)
Do not assume that AI service logs are deleted automatically. → Check the data retention and localization policy
Example: in 2023, Samsung accidentally exposed internal code by pasting it into ChatGPT for analysis. Result: leak and dissemination of its industrial secrets.
Oversight, bias and regulatory compliance
Generative AI cannot function without human oversight and a robust compliance framework. It carries a double risk: making erroneous decisions if used alone, and reproducing discriminatory biases.
Do not entrust AI with decisions with vital issues (medical diagnosis, legal decisions, industrial safety) without expert supervision
Don't let AI completely replace human journaling for compliance, ethics, or editorial quality
Don't let AI make HR or hiring decisions without regular bias audits and a clear legal framework
Do not deploy a model in a product without robustness and safety testing
Do not minimize the impact of systemic biases: a continuous correction plan is needed
The central role of the EU AI Act:
Adopted in 2024, the European AI Act constitutes the first large-scale global regulation on artificial intelligence. It classifies uses according to their level of risk:
Unacceptable risk (e.g. behavior manipulation, mass facial recognition) → prohibited
High risk (e.g. recruitment, credit, justice) → subject to strict obligations (documentation, traceability, audits, human governance)
Limited or low risk (e.g. chatbots, text generators) → information obligations and transparency
In short:
Don't assume that AI automatically complies with GDPR or the AI Act
Document the legal bases of processing and human supervision mechanisms
Depending on the level of risk of your use, regularly audit your models to detect and limit bias
Example: recruitment AI deployed without control systematically discriminated against women in technical positions. Under the AI Act, this type of breach could result in severe financial penalties (up to 7% of global annual turnover).
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