Discover the Main Data Risks in AI and How Brands Can Secure Their Marketing Practices

AI the Driving Force Behind Modern Marketing… and a Source of Risk

Artificial Intelligence has become a central driver of modern marketing.
In a survey of over 1,000 marketing professionals, 71% reported using generative AI tools (American Marketing Association, 2024).

This rapid adoption comes with a paradox: AI is only as good as the quality and security of the data it relies on. These datasets customer behavior, purchase history, creative assets are now a major vulnerability. Poorly governed data can expose brands to risks of non-compliance, reputation damage, and ethical breaches.

This guide aims to help marketing teams understand these risks, strengthen data security, and build a responsible AI approach.

Why Data Represents a Major Risk in AI

AI models learn from data. Their effectiveness depends directly on the accuracy, representativeness, and confidentiality of that information.

According to the CNIL – 2024 Annual Report, one in three companies struggles to identify which personal data are integrated into their AI systems. This lack of visibility is amplified by the rise of unapproved tools within companies—a phenomenon known as Shadow AI.

Meanwhile, regulation is tightening. The GDPR already governs data collection and use, while the European AI Act, effective progressively through 2026, adds requirements for transparency, traceability, and human oversight for “high-risk” AI systems.
Brands must therefore strike a balance between innovation and compliance.

The Most Critical Risks for Brands

Data Leaks and Exposure of Sensitive Information

A poorly written prompt, a shared dataset, or pasting confidential text into a chatbot can trigger major data leaks.
In 2024, 15% of cybersecurity incidents recorded by Gartner were linked to unregulated generative AI usage (Gartner – AI Security Trends 2024).

Bias and Discrimination

Algorithms learn from human data and therefore reproduce human biases.
The Harvard Business Review highlights that AI systems left unaudited can perpetuate implicit bias, making regular auditing essential.

Data Poisoning and Adversarial Attacks

Manipulated or malicious data can distort a model’s outputs, leading to poor marketing decisions or misaligned predictions.

Shadow AI

Shadow AI refers to the use of unauthorized AI tools by internal teams.
An employee experimenting with ChatGPT, Gemini, or Midjourney using client data can unintentionally cause a confidentiality breach or a GDPR violation.

Intellectual Property and Trust

Generative AI tools rely on public or licensed datasets whose sources are sometimes uncertain.
The MIT Technology Review reports several lawsuits where companies faced legal action over AI-generated content containing copyrighted material.

The Direct Impacts on Marketing and Brand Reputation

The consequences of an AI-related incident go beyond data loss they directly affect trust, compliance, and performance.

According to the Ifop – AI Barometer 2024, 82% of French consumers are concerned about how their personal data is used for marketing purposes.
A brand involved in a privacy breach or accused of algorithmic bias may lose consumer trust for years.

The financial implications can also be severe. The GDPR allows fines of up to 4% of global annual revenue for violations. Poorly trained models can also produce flawed analyses, leading to misguided decisions and reduced campaign performance.

Best Practices for Responsible and Secure AI

Data Governance

Mapping and classifying data by sensitivity is the first step toward limiting exposure. A clear governance policy helps identify critical data flows and control access.

Security & Privacy by Design

Integrate security measures directly into workflow design: encryption, anonymization, and limited access rights help prevent leaks or misuse.

Human Validation

No strategic decision should rely solely on an algorithm.
Combining AI insights with human expertise ensures more reliable results and reduces errors or undetected bias.

Continuous Training and Regulatory Awareness

Train marketing teams on AI risks, prompt hygiene, and GDPR compliance.
Regularly monitor changes in the GDPR, Digital Services Act, and AI Act to stay ahead of regulatory requirements.

Collaborative and Supervised Tools

Platforms such as MTM—specialized in managing creative workflows and marketing asset collaboration—allow teams to integrate human validation checkpoints, version control, and secure asset sharing across departments and partners.

Toward Ethical and Trustworthy AI

The rise of AI is not just a technical issue—it is a matter of trust.
As the European Commission (2025) states:

“Trust is the currency of artificial intelligence. Without transparency, there will be no sustainable adoption.”

Brands that combine innovation, transparency, and compliance will transform regulation into a strategic advantage. Ethical data practices will become a key competitive differentiator in the marketing landscape.

Data Security : The Cornerstone of Responsible AI Marketing

Data is both the fuel and the Achilles’ heel of artificial intelligence.
For brands, using AI responsibly means embedding security, governance, and human validation into every stage of the marketing process.

By securing data flows and adopting structured governance, companies protect more than just information—they safeguard their credibility and the trust of their audiences, an intangible asset as valuable as data itself.
With collaborative tools like MTM, human review policies, and continuous vigilance, automation can truly remain synonymous with trust.

FAQ – AI, Data and Security for Marketing Teams

What are the main data risks in artificial intelligence?

The main risks include data leaks, algorithmic bias, data poisoning attacks, and uncontrolled use of AI tools (Shadow AI). These issues can damage brand reputation and regulatory compliance.

How can a company secure its data when using AI?

By implementing strong data governance, limiting access, applying Privacy by Design principles, and ensuring human validation before publishing AI-generated outputs.

What is Shadow AI and why is it dangerous for brands?

Shadow AI refers to the use of unauthorized AI tools within a company. It exposes organizations to data breaches, loss of confidentiality, and non-compliance with internal or legal frameworks.

Does the GDPR fully protect data used by AI systems?

The GDPR provides a strong foundation, but the European AI Act adds new obligations for traceability, transparency, and human oversight, especially for high-risk AI applications.

5. Why is data governance becoming a key factor in marketing?

Effective data governance ensures reliable AI models, prevents bias and data exposure, and builds consumer trust in brands leveraging AI responsibly.

Sources

• McKinsey – The State of AI 2025
• Harvard Business Review – AI and Data Governance (2025)
• MIT Technology Review – AI Transparency and Ethics (2024)
• CNIL – Rapport annuel 2024
• Ifop / Talan – Baromètre 2024 sur les IA génératives
• Commission européenne – AI Act