Governance using artificial intelligence begins with data governance which is certainly not a newconcept. That is, when data is collected, organizations need a certain policy level and insight to govern their datamanagement. To a large extent, data policy has remained background in nature, although data-driven businessesusually require data governance to be of prime concern. However, in the past few years, in the face of thetwenty-first century challenges of changing events, data governance has been at the forefront of discussionsregarding almost everything in the media and the boards of various organizations that are taking the first stepstowards their AI. In addition, the recent increase in government participation in data privacy is playing aprominent role in this development. His main focus was on the risks of artificial intelligence and themaintenance of its framework in the face of rapid machine learning development. This has contributed to thevarious institutions and companies starting to verify that data governance has not established an investigation ina way that leads to trading the massive shift towards the demanding machine in the current era of artificialintelligence. As with artificial intelligence we bring the new governance requirements related to artificialintelligence that require a proper framework that is applied transparently. Currently, with the spread of datascience and tools that put data across the facility and become available to the many and not only to the elite few(such as data scientists, or even analysts). Explain that companies are using more data in many ways than in thepast, and this represents a great value for companies, as in fact, they have witnessed great success in using data,which led to business adoption of this approach. However, this may also present new challenges for these Firmsthat business IT companies unable to handle data requests create a power struggle between two trends that slowsthe overall progress of AI in the facility. This has also led to a fundamental shift and organizational change inthe type of data governance that data can be used, while It also protects data from risk, which is an answer tothis topic. With this in mind, this work explores how many companies participate in defining the scope of AI. Itis needed in order to strengthen its governance approach. With data governance as a basis for this, while it mayrequire an organizational change to verify it in the long run, which may allow the organization’s AI to becomein the scope in which it is responsible and sustainable, this work details the framework of the AI workgovernance framework that it intends to adopt by the various institutions
