While online users usually appreciate the creative capabilities of Generative Pre-trained Transformers (GPTs) and powerful large language models (LLMs), most people tend to be cautious about sharing personal details. Yet this hasn’t prevented generative AI (GenAI) from expanding over the last two years. Where do developers get access to the data necessary to train powerful machines able to answer the bizarre questions one doesn’t dare ask in public?
In this article, we explore how AI model training can affect the safety of your business information and how to mitigate the threats of damage to your brand.
What Happens to Your Data When Using GenAI
The Use of GPTs: A Relationship with Mutual Benefits
It takes a lot of energy and experience for us humans to come up with ideas and formulate answers. Imagine what it must be like for a tech tool. For GenAI tools to perform effectively, they require a steady stream of data input. And the more varied the material, the more realistic, fast, and accurate their responses will be.
In AI model training, “crawlers” and “scrapers” built by developers internally or purchased from external providers explore the web, looking for public information to extract. But GenAI goes one step further by using the interactions it has with users to refine itself. In other words, the details you share can serve as a resource to train it.
This may not come as a surprise, as some platforms are very transparent about it. For example, OpenAI’s terms of service read: “ChatGPT, for instance, improves by further training on the conversations people have with it, unless you opt out.” Granting access to your personal information is often the trade-off required to benefit from free GPT tools. Customers, however, tend to overlook the terms and conditions when using tech tools, inadvertently disclosing more than they intended.
Using GenAI Holds Many Business Opportunities … and Risks
Forewarned is forearmed, and that’s certainly true when using GenAI solutions. These tools—often presented as helpful, reliable assistants—foster a sense of trust that encourages users to let their guard down. This perceived security can sometimes lead to oversharing of sensitive information.
On a personal level, LLMs can be a threat to privacy. The dangers, however, are even higher when they relate to business. While the vast majority of enterprises use AI in their daily activities—with 78% using AI in at least one business function1—only a few of them have control over how their employees use it. Customers may unknowingly include sensitive details in their prompts, which could later be reused in responses to others, potentially exposing them to competitors or malicious actors.
This prospect is not as unlikely as it may seem, as we’ve seen instances of tech giants banning the use of GenAI tools following sensitive information being leaked by an employee. If even tech leaders are vulnerable on this point, it’s no surprise that 75% GenAI customers feel it introduces new data security risks.2
The Path Towards GenAI Governance
An Overview of the Regulatory Landscape
The threats posed by AI to privacy have been quickly recognized, giving rise to national and regional regulations aimed at ensuring the development of safe and transparent tech. Regulations such as Singapore’s Model AI Governance Framework for Generative AI and Australia’s Proposed Guardrails for the Mandatory Use of AI in High-Risk Settings have been emerging around the world. After releasing an AI bill of rights in 2022, the US seems to have opted for a decentralized regulatory model, relying on the industry itself. Similarly, the UK has produced guidelines in its 2022 “A pro-innovation approach to AI regulation” whitepaper. Overall, there appears to be a global trend toward partnering with the tech industry to drive innovation while safeguarding end users.
The European Union, however, distinguished itself by implementing the EU AI Act in 2024, marking a pivotal shift in AI governance. With this act, the region aims to establish standards to prevent privacy violations. Unlike the examples presented above, decisions are taken by a standalone regulator—the European AI board, which imposes its decisions strictly and rigorously.
Governing Data Use: An Inconsistent Model
The existence of a legal framework for AI regulation could suggest that user safety is guaranteed. Unfortunately, the borderless nature of data driving LLMs means that national and regional initiatives won’t suffice to ensure protection. By 2027, more than 40% of AI-related data breaches will be caused by the improper use of generative AI (GenAI) across borders, according to Gartner®, Inc.3 To enhance online trust, regulators must bridge the gap by enforcing global policies.
Progress measurement is another key aspect of AI governance. While regulators have been relying on mutual collaboration with platforms so far, this model has only proven partially effective. Industry leaders have been reinforcing transparency regarding data use, and generally offer alternatives to comply with privacy standards. Not all tools, however, play the compliance card. Measuring developers’ commitment to ethical standards thus becomes essential to mitigate risks.
Enabling Privacy
Designing Responsible Models
Customers place trust in platforms to protect their details. A breach of this trust can lead to significant reputational harm and diminished business opportunities. AI developers benefit from using ethical models to build GenAI, which fosters user confidence and long-term success. These models usually include the following steps:
- Responsible data sourcing: Implementing strict sourcing practices to obtain clean, compliant, and consented data can minimize risks associated with confidentiality and misuse.
- Safety testing & red-teaming: Continuously testing the robustness of your model by perpetuating fake “attacks,” also called red-teaming, develops the tech’s resiliency.
- Oversight & reinforcement: Ensuring LLMs are being reviewed regularly by both AI and human resources helps fine-tune the model, flag potential bias and hallucinations, and make it more human-like. A mix of reinforcement learning with human feedback (RLHF) and reinforced learning with AI feedback (RLAIF) can bring great results.
Proactively Protecting Your Business
AI developers and regulatory frameworks play critical roles in safeguarding privacy, but businesses must also take proactive steps to prevent leaks. With only 44% of US businesses owning specific policies in place for employee use of GenAI,4 addressing data security has become crucial.
Many GenAI-related issues stem from insufficient knowledge and inadequate governance within organizations. To mitigate these risks, educating staff on the safe use of technological tools should be prioritized. Comprehensive training programs, supported by consistent internal policies and guidelines, can provide a standardized framework throughout the organization.
Reap the Benefits of AI
GenAI can be a great asset to your business, and the risks it entails should not prevent you from reaping the benefits. To realize its potential, you need to lay a solid foundation—data architecture, robust AI model training and compliance frameworks, and AI-ready talent. You can take security to the next level by investing in safe, fully embedded GenAI solutions for your business.
1 “The state of AI: How organizations are rewiring to capture value,” McKinsey & Company, March 12, 2025.
2 “60+ Generative AI Statistics You Need to Know in 2025,” Richard James, AmplifAI, June 2, 2025.
3 Gartner Press Release, “Gartner Predicts 40% of AI Data Breaches Will Arise from Cross-Border GenAI Misuse by 2027, ” February 17, 2025. https://www.gartner.com/en/newsroom/press-releases/2025-02-17-gartner-predicts-forty-percent-of-ai-data-breaches-will-arise-from-cross-border-genai-misuse-by-2027
GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.
4 “2024 AI C-Suite Survey Report, ” Littler, September 24, 2024.
