AIGC DATA COMPLIANCE: NAVIGATING RISKS AND BUILDING TRUST IN THE AGE OF GENERATIVE AI
Artificial intelligence generated content (AIGC) has rapidly emerged as one of the most disruptive technologies of the 21st century. From automated text generation and synthetic media to AI-assisted design and coding, organisations are leveraging AIGC to improve efficiency, creativity and personalisation.
Yet, with opportunity comes risk. Because AIGC models rely on massive datasets, often scraped from the internet or collected through opaque processes, they raise critical compliance challenges across privacy, intellectual property (IP), data protection and algorithmic accountability. At the same time, regulators around the world are tightening data governance, requiring organisations to rethink how they develop, deploy and monitor AIGC systems.
This article examines the compliance risks of AIGC, highlights regulatory frameworks and offers best practices for organisations. By drawing on real-world case studies from healthcare, financial services and consumer marketing, it demonstrates how compliance is not just a defensive necessity but a proactive strategy to build trust and competitive advantage.
The rise of AIGC and its data foundations
Generative AI (genAI) models, including large language models, image generators and multimodal systems, rely on training data that is vast, diverse and often unstructured. While this breadth fuels innovation, it introduces multiple risks. First, data provenance uncertainty – organisations often lack full visibility into whether datasets contain copyrighted material, sensitive personal data, or biased samples. Second, opaque model outputs – AIGC systems can produce ‘hallucinations’ or outputs that embed underlying data risks, from misinformation to confidential data leakage. And third, cross-border data exposure – many AIGC systems rely on cloud providers and international data flows, raising questions about compliance with data localisation and transfer rules.
Oct-Dec 2025 Issue
Haleon