Find out if the AI tool can be transparent about its data sources
Posted: Mon Feb 10, 2025 4:08 am
Don't provide the AI application with more data than required, and don't share sensitive, proprietary data. Lock/encrypt intellectual property and customer data to prevent its distribution.
Can the vendor protect your data? “Whether a company is training a model in Vertex AI or building a customer experience in the Generative AI App Builder, private data is kept private and is not used in the broader training corpus of the underlying model,” Google says in its blog post, but “how” it does this remains unclear. Check each AI tool’s bahamas mobile database language to understand whether the data you provide to it can remain private.
Label the data from the derivative works of the owner, or the person or department that commissioned the project. This is useful because you may be ultimately responsible for any work done in your company, and you should know how and by whom the AI was included in the process.
Ensure data portability across domains. For example, a team may want to cleanse data of its intellectual property and identifying characteristics and include it in a common training dataset for future use. Automating and tracking this process is essential.
Stay up-to-date with emerging industry guidelines and recommendations, and engage with peers at other organizations to learn how they approach risk mitigation and data governance.
Before beginning any generative AI project, consult with a legal expert to understand the risks and processes to follow in cases of data breaches, privacy and intellectual property violations, malicious activity, or false/erroneous results.
Can the vendor protect your data? “Whether a company is training a model in Vertex AI or building a customer experience in the Generative AI App Builder, private data is kept private and is not used in the broader training corpus of the underlying model,” Google says in its blog post, but “how” it does this remains unclear. Check each AI tool’s bahamas mobile database language to understand whether the data you provide to it can remain private.
Label the data from the derivative works of the owner, or the person or department that commissioned the project. This is useful because you may be ultimately responsible for any work done in your company, and you should know how and by whom the AI was included in the process.
Ensure data portability across domains. For example, a team may want to cleanse data of its intellectual property and identifying characteristics and include it in a common training dataset for future use. Automating and tracking this process is essential.
Stay up-to-date with emerging industry guidelines and recommendations, and engage with peers at other organizations to learn how they approach risk mitigation and data governance.
Before beginning any generative AI project, consult with a legal expert to understand the risks and processes to follow in cases of data breaches, privacy and intellectual property violations, malicious activity, or false/erroneous results.