Looking for a Governance framework for GenAI, does anyone have any helpful insights to share?

The AI Governance Framework  is designed to help organizations leverage the power of generative artificial intelligence (GenAI) while managing its associated risks. 

Here are the key components of this framework:
1. Risk Identification and Mitigation
Risk Identification: Recognizing potential risks associated with the use of GenAI, including ethical, security, and operational risks.
Mitigation Strategies: Developing actionable strategies to mitigate identified risks and ensure safe AI deployment.
2. Implementation Roadmap
Roadmap Development: Creating a detailed roadmap and timeline for implementing governance measures.
Stakeholder Engagement: Involving key stakeholders, including AI specialists, auditors, regulators, and executives, in the governance process.
3. Compliance and Regulation
Regulatory Alignment: Ensuring that AI practices comply with relevant regulations and standards.
Audit and Monitoring: Regularly auditing AI systems and monitoring their performance to maintain compliance.
4. Transparency and Accountability
Transparency Measures: Implementing measures to ensure transparency in AI operations and decision-making processes.
Accountability Structures: Establishing clear accountability structures to hold individuals and teams responsible for AI governance.
5. Ethical Considerations
Ethical Guidelines: Developing and adhering to ethical guidelines for the use of GenAI.
Bias and Fairness: Addressing issues of bias and fairness in AI systems to promote equitable outcomes.
6. Data Management
Data Governance: Implementing robust data governance practices to ensure data quality, security, and privacy.
Data Integration: Integrating GenAI with organizational data while maintaining data integrity and confidentiality.
These components collectively ensure that organizations can harness the benefits of GenAI while managing its risks effectively.

Reference link : https://www.genai.global/solutions/framework

Use copilot and copilot studio for building gen ai easily 

We focused of governance framework on the data first, and trainning later. For data we categorize it based on the source. For example, we use at least 3 categories: Verified data(wich is data we are certain that comes from the source declared), Authorized data(wich comes from devices, tools or sites that have compleated an authorization process but we cant be certain the person authorized is there) and Unverified data(Wich comes from annonimous sites or tools or from surveys).

Based on it, we take the governance model in "tracks" wich allows us to have an overall view of the models, data and testing of each "track".

Basiclly, we "propose" a track that inclues the model, type of data to use(both new and reusable) and the hypotetical testing and expectations that would come of it. 

In resume, the governance framework we are using goes from regulating the data we get, to manage the expectations of the model, and to finally asses the results of it.

We utilized the below rules:
1. Categorize internal/external  plus audited (facts, authorized data providers)/ unaudited (email content, chats etc which might or might not be true) data
2. Build a data governance process to select the right data sources to be utilized for the generative AI to make sure only audited data is consumed for training purposes
3. Provide data sources along with answers to prevent hallucination and increase user trust
4. Perform thorough testing on the different user inputs and make sure to start with a soft release that provides interactive data fetch before  you are confident enough to allow it to perform a transaction