Postpone experiments with generative

[rakhirhif8963]

Data Management Tactics You Should Consider Now
At this early stage, we don’t know what we don’t know about AI in terms of risks related to bad data, privacy and security, intellectual property, and other sensitive issues. Furthermore, AI is a broad field with many approaches, such as LLM and logic-based automation, to name a few, that need to be explored through a combination of data management policies and data governance practices. Therefore:

AI until you have an administrative system in place that controls strategy, policies, and procedures to mitigate risks and validate results.
Implement data governance guidelines: It all starts with a deep understanding of your own data, wherever it resides. Where is your sensitive and customer data located? How much intellectual property data do you have, and where are these files stored? Can you control how it’s used to ensure that these types of data don’t accidentally end up in an AI tool and prevent a security or privacy breach?
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.
Find out if the AI ​​tool can be transparent about its data sources.
Can the vendor protect your data? “Whether a el salvador mobile database 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 contractual 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.