What about intellectual property

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Derived outputs. The third area of ​​data governance has to do with the data generated by the AI ​​process and its ultimate owner. Let’s say I use an AI bot to solve a coding task. Usually, if something was done incorrectly and resulted in bugs or errors, I can figure out who did what by investigating and fix it. With AI, however, my organization is responsible for any errors or bad results that result from the task I asked the AI ​​to perform — even if we don’t have visibility into the process or the source data. You can’t blame the machine: somewhere along the way, a human caused the error or bad result. ? Do you own the work that a generative AI tool creates? How would you defend that in court? According to the Harvard Business Review, lawsuits are already underway in the art world.

Data Management Tactics You Should Consider Now
At this early stage, we don’t know what we don’t know bahamas mobile database 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:

Postpone experimentation with generative 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?