There are certain risks that the unchecked generative AI possesses with the overabundant information it may hold. Companies run the risk of disclosing their valuable assets when they feed private, sensitive data into open AI models. Some businesses choose to localize AI models on their systems and train them using their confidential data in order to reduce this danger. However, for best outcomes, such a strategy necessitates a well-organized data architecture.

Risks of Unchecked Generative AI

The appealing elements of generative AI and Large Language Models (LLMs) are their capabilities to compile information to produce fresh ideas, but these skills also carry inherent risks. If not carefully handled, gen AI can unintentionally result in issues like: 

Personal Data Security 

AI systems must handle personal data with the utmost care, especially sensitive or special category personal data. Concerns about unintentional data leaks that could lead to data privacy violations are raised by the growing integration of marketing and consumer data into LLMs.

Contractual Violations 

It is occasionally illegal to use consumer data in AI systems, which has negative legal repercussions. As companies adopt AI, they must carefully negotiate this treacherous terrain to ensure they uphold contractual commitments.

Customer Transparency and Disclosure 

The goals of current and potential future AI regulations focus on a transparent and lucid disclosure of AI technology. For instance, the business must disclose whether a person or an AI is handling a customer's engagement with a chatbot on a support website. Maintaining trust and upholding ethical standards depend on adherence to such restrictions.

Legal Challenges and Risks for Businesses 

Recent legal actions against eminent AI companies highlight the significance of handling data responsibly. The importance of strict data governance and transparency is highlighted by these lawsuits, which include class action cases involving copyright infringement, consumer protection, and data protection issues. They also suggest possible conditions for exposing the origins of AI training data.

Since their use of copyrighted data to build and train their models, AI giants have been the main targets of various lawsuits. Allegations of copyright infringement, consumer protection violations, and data protection legislation violations are made in recent class action lawsuits filed in the Northern District of California, including one filed on behalf of authors and another on behalf of victim users. These submissions emphasize the value of treating data responsibly and could indicate that in the future it will be necessary to identify the sources of training data.

Moreover, businesses possess serious risks when they significantly rely on AI models, not just AI developers like OpenAI. The case of how many of the apps implement improper AI model training may taint entire products. The parent business Everalbum was forced to destroy improperly gathered data and AI models after the Federal Trade Commission (FTC) accused Everalbum of misleading consumers about the use of face recognition technology and data retention. This forced Everalbum to cease in 2020.

How to Mitigate AI Risks? 

Despite the legal challenges, CEOs are under pressure to adopt generative AI if they wish to increase their business’ productivity. Businesses can create best practices and get ready for new requirements by using the frameworks and legislation currently in place. AI systems are covered by provisions in existing data protection regulations, such as those requiring transparency, notice, and the protection of individual privacy rights. Some of these best practices involve:

• Transparency and Documentation: Businesses are recommended to clearly mention the AI usage, and document AI logic, applications and potential impacts on the data subjects. Also, businesses must keep a record of data transactions and detailed logs of confidential information in order to maintain proper governance and data security.

• Localizing AI Models: By ensuring that models are trained on pertinent, organization-specific information, internal localization and training with private data can lower data security risks and boost efficiency.

• Discovering and Connecting: Companies must utilize generative AI to unveil new perspectives and create unexpected connections across different departments and information silos.

• Preserving Human Element: Gen AI should improve human performance rather than completely replace it. To reduce model biases and data inaccuracies, human monitoring, critical decision review, and content verification of AI-created information are essential.