With the advent of Artificial Intelligence (AI) technologies, it is valuable for companies to leverage their power to bolster innovation and outperform their competitors. Yet, given the vast amount of data required to train AI models, these technologies also raise concerns about the privacy and security of data.  Companies need to navigate complex legal requirements for compliance and risk mitigation. In this guide, we hope to explain why data privacy is essential, the current state of legal regulations on AI, and how your company can best mitigate AI risks. 

Using AI At Work: Legal Issues With Data Privacy & Security

AI systems learn and make predictions using data often involving personal information like names and financial details. When companies fail to protect this data, it can lead to severe consequences, such as damage to reputation, loss of customer trust, and legal repercussions. If company or client data is misused or accessed without authorization, individuals’ fundamental rights to privacy are violated, and confidential and proprietary information is compromised. 

Data Privacy Laws Apply To AI

The laws on AI and data privacy are continually changing as new challenges arise from these technologies. Companies must comply with regulations and rules in all operational jurisdictions. For instance, the General Data Protection Regulation (GDPR) sets guidelines for collecting, processing, and storing personal data for European Union residents. For non-compliance, there are hefty fines. Other regulations include Canada’s Personal Information Protection and Electronic Documents Act and the General Personal Data Protection Law in Brazil.

How can your company best address data privacy and security risks from AI?

Best Practices For Intragating AI Into Your Data. 

There are two core issues that every company is going to have to address when it comes to artificial intelligence. The first is the inevitable integration of custom AI tools, which will train on company data or develop AI tools available to customers or subscribers. Using your own AI tools or using a third-party tool to train on your data will allow companies to develop models that are custom and explicitly designed to assist internally. Many of our clients are AI developers building tools that will be available to end-users. The second issue that every company will need to face is the use of third-party AI tools by their employees, understanding which AI tools are approved, vetting those tools for data security and privacy, and training employees on how to maximize their data privacy and security settings with each tool is mission critical.

How can you govern your data?

  • Establish a clear framework for how your company will handle AI data. This framework will define your policies and who is responsible for which responsibilities in these processes. 
  • Conduct Data Protection Impact Assessments. These will allow your company to identify privacy risks and mitigate them. 
  • Measures to protect data should be embedded from the beginning when designing and developing AI systems. This design should include Privacy by Design principles, such as differential privacy and federated learning. Our firm can guide you through this process. 

How can you minimize data vulnerability and preserve anonymity?

  • Only collect and process the minimum amount of personal information necessary. 
  • Anonymize your data through data masking, pseudonymization, and aggregation. Data masking involves replacing sensitive information with fake information, such as changing the names on documents to “John Doe.” Pseudonymization refers to the process of replacing sensitive information with pseudonyms or codes. Aggregation signifies combining sensitive information with other information into groups. 

How can you secure your data storage and transmission?

  • Strong encryption allows you to safeguard your data while it is stored and transmitted. 
  • You can utilize robust access controls and regular safety audits to protect your data. 
  • You should also consider purchasing breach detection systems to identify potential data breaches. 

How can you promote transparency?

  • Provide clear information to your consumers regarding your data practices. 
  • It would help if you also allowed your users to obtain, modify, or delete the data that your AI system is using. 

How can you ensure your employees are aware of AI data practices?

  • You can implement training programs for your employees and teach them the best practices for developing and deploying AI that ensures data privacy and security. 
  • You can work to foster an organizational culture that values ethical AI use.

How can we ensure that our employees do not compromise our property information when using third-party AI tools?

It is mission-critical to develop a set of rules to help employees understand which third-party AI tools and GPTs they are authorized to use, as well as training materials to help them understand how to maximize their data privacy and confidentiality settings. Machines that allow AI use by their employees without developing these policies and guidelines will be potentially liable if proprietary, confidential, or personal information is uploaded into these tools to become part of the training model for that tool. This framework is typically captured in an acceptable use policy for AI. You can learn more about acceptable use policies for AI here.


We hope this guide helped you learn how to address some of the prevalent data privacy and security challenges. With this knowledge, your company should be able to comply with the laws and maintain trust with your users and stakeholders. 

Our firm is happy to assist you further in your navigation of the complex legal landscape of AI technologies. 

The post Navigating Data Privacy and Security Challenges in AI: A Legal Guide for Companies first appeared on Traverse Legal.