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Microsoft Copilot AI Governance Best Practices


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    High-Level Overview: AI Governance Done Right

    In today’s competitive landscape, leveraging AI solutions such as Microsoft Copilot is a powerful strategy for driving business growth across various industries. AI’s capability to analyze data and make informed decisions is a crucial factor contributing to its widespread adoption. According to recent studies, nearly three-quarters of businesses attribute their success to the continuous advancements in AI technology.

    However, the rapid pace of designing, developing, and deploying AI applications often leads to overlooked governance considerations. Neglecting proper AI Governance can have significant repercussions, such as unaddressed data protection issues, financial losses, reputational damage, and ethical breaches. Despite perceptions that governance may be cumbersome or obstructive, it is crucial for ensuring responsible and effective AI deployment.

    For example, decisions made with AI-enabled tools can introduce biases that unfairly disadvantage specific individuals and communities. AI Governance introduces a standard of accountability, requiring organizations to consider the societal impacts these systems can produce and ensure they’re implemented fairly, transparently, and in alignment with human values and respect for individual rights.

    AI Governance: The Strategy

    AI Governance encompasses a set of frameworks, policies, and best practices designed to guide the development and deployment of AI technologies such as Microsoft Copilot. It aims to minimize risks related to bias and enhance the benefits.

    This governance covers various aspects, including AI algorithms, decision-making processes, data privacy and security, and AI’s potential economic and social impacts.


    The core areas of AI Governance include the related AI model management and the AI model’s achievement parameters.

    In reviewing the AI model management elements, they must outline the explicit goals of what your business is trying to address and test use cases for which the AI model’s output should address.

    You can perform tests against the AI platform to ensure accurate results, and any deviations can be addressed and modified accordingly.

    Your AI model’s achievement parameters are a more granular level of business requirements and specific short-term, mid-term, and long-term goals your organization wants to achieve. Monitoring your AI platform and its intelligence output levels is easier than you may think.

    You can assist the AI model in learning the organization’s specific verbiage and tailoring the system to adapt quickly so that your end-user training can be performed more confidently. The platform’s overall technical requirements can then be tailored or modified to meet these requirements and the secondary requirements that come out of the lessons learned from this testing.


    The success of AI models depends on high data quality; therefore, effective data governance is essential for their success. It helps to answer some questions:

    • What type of data is it, and what is the related data source?
    • How can data be transformed or modified?
    • Who modified the data last time, and who can modify it?
    • Does data contain any sensitive information?
    • Does it comply with privacy regulations specific to your vertical and/or business (i.e., HIPAA, PII, FINRA, etc.)

    Companies provide data to focus on the process and decision; they do not bother with the data quality. Bad data leads to bad outcomes. Companies that utilize bad data to make decisions will get just that: bad decisions and results. For AI models to make accurate decisions, it is essential to have high-quality training data coupled with effective management techniques. 

    Copilot AI Governance is a necessity, a required, and a core part of your organization’s underlying data strategy. Implementing a successful AI Governance strategy requires ensuring data quality and protecting it from misuse. Start by setting clear priorities and aligning governance tasks with the complexity of these AI efforts. 

    Setting your specific milestones and related granular goals and implementing a monitoring capability to review its performance to ensure it meets your organization’s needs is critical.

    Getting the key stakeholders and related users who will be overseeing your organization’s AI solution involved at the very early stages is critical so that these practices and strategies around Microsoft Copilot’s AI Governance are followed and enforced. 

    Your AI solution will grow and expand over time, and so will the maturity level of your AI Governance model.

    The model must also be reviewed regularly, and by establishing a Microsoft Copilot AI Governance committee within your organization, you will ensure that the specific AI-platform-related responsibilities of the team members who will perform the required tasks and at what intervals they are documented and understood.

    Identifying and reviewing key metrics around your company’s AI efforts is vital so they are measured. There are proven methods that EPC Group has developed to track these key data points, review the overall AI Governance strategy’s performance and enforcement, and identify areas of concern or improvement.

    Lastly, by following this strategy, you will be able to identify new areas of opportunity to implement AI solutions around specific business processes or initiatives to gain a competitive edge over your competitors.

    Few organizations have implemented AI Governance, and fewer have done so correctly. These governance policies will be a crucial factor in ensuring that your organization has the correct AI solutions implemented and that IT security and related overall infrastructure are in place so that you can thrive.

    The overall number of hours (i.e., actual additional time your team members can spend working on other tasks that increase revenue) that can be gained each month by using AI solutions correctly in your business is quantifiable, and this is where the fundamental differences are made, and can be tracked from an ROI perspective.

    Implementing a Successful AI Governance Strategy

    A successful Microsoft Copilot AI Governance strategy keeps the company safe and brings the data standards into implementation. If AI Governance is appropriately implemented, the trust in your organization’s data and AI systems can be improved. 

    Note: EPC Group has seen the AI Governance initiatives we have implemented for our clients, and specifically around Microsoft Copilot, uncover security and related governance issues with other existing software solutions and mission-critical platforms. EPC Group’s AI Governance framework can be reused, and its steps can be applied to look at different platforms, such as your Microsoft Power BI, SharePoint Online, or Power Platform solutions.

    For example, if you want to use the full potential of Microsoft Copilot’s features with Power BI, code writing, and additional real-time query features, your AI Governance strategy and the related configurations that come with it will allow for that when done correctly.

    AI Governance and the confidence it brings with it ensure that team members will be more confident in the systems’ decisions with data. It can also build trust in the analysis and models generated by data scientists, leading to improved accuracy through better data quality. 

    EPC Group’s AI Consulting practice has seen the ROI on these practices actually pay for the project in a matter of months. The actual ROI calculations of AI efforts are quantifiable and vastly outpace any previous technologies ever implemented.

    Another core element that this strategy assists with is accountability and management of AI at all the various levels within the organization to ensure AI solutions do not lead to a possible security risk. Implementing this can help identify potential causes of unethical responses or internal data breaches.

    Best Practices for AI Governance Success

    AI Governance best practices guide the responsible and effective implementation of artificial intelligence (AI) systems within an organization. Here are five foundational best practices for AI Governance.


    Successful AI Governance depends on robust internal governance mechanisms. Working groups composed of AI experts, business leaders, and key stakeholders can provide expertise, focus, and accountability, helping organizations craft policies for how AI is used within a company.

    Internal governance structures can meet numerous governance objectives, including defining the business use cases of AI systems, assigning roles and responsibilities, enforcing accountability, and assessing outcomes.


    Transparent communication is vital for all groups with a stake in how AI is developed and used. These stakeholders may include employees, end users, investors, and community members.

    By explaining to each stakeholder group how AI works, how it is used, and the anticipated benefits and drawbacks for them, organizations can foster transparency and trust with those most likely to be affected. Developing formal policies around stakeholder engagement helps establish how communication will be conducted.


    Well-governed AI systems respect the privacy and autonomy of individuals and avoid discrimination that can unfairly disadvantage specific populations. Risks that require mitigation include using poor-quality training data and a sample size of related input queries that do not properly align with your organization’s users. This can lead to poor data sampling methodologies that lead to bias and incorrect output. Risk management strategies help ensure the models are being used responsibly.

    EPC Group’s 10 Core Principles to AI Governance

    EPC Group has been involved in a large number of AI initiatives and has been working closely with Microsoft and OpenAI on the latest technology improvements in AI. We have outlined the following ten core principles that you can follow in your AI Governance effort:

    1. Establish Clear Purposes and Scope Boundaries: Define specific use cases, data types, output types, and access rules for generative AI models before deployment. Regularly reassess these parameters as capabilities evolve.

    2. Secure Executive Sponsorship and Leadership Buy-In: Ensure strong leadership support to improve data quality, security, and management. Promote a culture of ownership and continuous integration within individual teams, facilitated by top-down communication and recognition.

    3. Implement Rigorous Access Controls: Utilize authentication, authorization levels, watermarking, mandatory disclosures, prompt engineering techniques, compliance monitoring, and restrictions on third-party integrations to control access.

    4. Maintain Comprehensive Activity Logs: Keep detailed records of system use, including prompts, parameters, outputs, user details, and timestamps, to enable auditing, trend analysis, oversight, and accountability.

    5. Apply Robust Validation Processes: Validate AI outputs for accuracy, consistency, coherence, appropriate tone, legal and ethical compliance, and lack of potential harm before public release or sensitive use. Manual review is critical.

    6. Engineer Safety into Systems: Incorporate features like Selective Question Answering to allow models to refuse unsafe or unethical prompts. Implement rate limiting to curb the spread of misinformation.

    7. Establish Effective Oversight Procedures: Set up precise human and automated oversight mechanisms to regularly review operations audit logs, validate outputs, assess emerging risks, and strengthen governance controls.

    8. Cultivate a Responsible AI Culture: Provide extensive training on the responsible and ethical use of AI systems. Foster organizational awareness of risks, encourage reporting of questionable uses and promote a culture of trust and collective responsibility.

    9. Mitigate Biases through Diversity: Ensure diverse training data and team participation in model development, governance, validation, and oversight to address and mitigate biases.

    10. Ensure Transparency and Accountability: Disclose model capabilities, limitations, data sources, errors, risks, and uncertainties to set proper expectations. Implement review and appeal procedures for questionable outputs and welcome external audits.

    Ensuring Your IT Security Is AI Ready

    Keeping up with the latest threats isn’t easy for cybersecurity professionals. Recent advancements in artificial intelligence, which has been called the AI revolution, are upping the ante even further. Security leaders must find strategies to level the playing field and evolve defenses in tandem with the sophistication of attacks.

    AI’s effects on the threat landscape are already being felt globally. Most companies are seeing more AI-driven cyber-attacks, and there is a driving concern that implementing AI within your organization may cause an internal threat.

    By implementing this Microsoft Copilot AI Governance strategy for your organization and developing an AI Roadmap for your organization, which EPC Group has published, you can avoid these threats and utilize the technology to deter and identify them before them occurring, as well as ensure all team members on your AI Governance committee and those IT team members administering the solutions know where to look and how to address any related issues. 

    We have seen many companies not wanting to necessarily address the AI-driven attacks or related threats by adding more team members or updating their current IT security solutions with AI-powered ones and driving towards purchasing add-on AI-driven IT security solutions that connect to their existing platform.

    There is a delicate balance here, and EPC Group has identified the solutions for these areas and related concerns.

    There are proven methods and solutions to implement to get your organization running a robust AI solution that will drive unbelievable ROI from a cost reduction, sales improvement, and productivity perspective if critical principles are followed and these steps are implemented in order so that your AI foundation is laid correctly.

    Aligning Your Business Strategy with AI

    As organizations increasingly integrate AI into their operations, it is crucial to develop robust business strategies to ensure effective and responsible use of these AI technologies. AI Governance is pivotal in aligning AI initiatives with organizational goals while safeguarding ethical standards and regulatory compliance. The following are some critical suggestions that EPC Group has developed to assist you in your efforts:

    Establish Robust Data Management Protocols

    Data is the foundation of AI, making robust data governance a cornerstone of AI Governance. Your company will greatly benefit from performing the work required to implement these best practices to ensure data privacy, data quality, and security. This includes establishing data ownership, ensuring data accuracy, and implementing robust data protection measures. Additionally, transparent data usage policies and clear guidelines on data access and sharing are essential.

    Engage with AI Consulting Partners with Real Experience

    Collaboration with external stakeholders, including industry partners, regulatory bodies, and academic institutions, is crucial for staying abreast of best practices and regulatory changes in AI Governance. Engaging with these stakeholders helps organizations benchmark their governance practices, gain insights into emerging risks, and engage in the broader conversation on the responsible use of AI.

    Implementing these business strategies can help organizations navigate the complexities of AI Governance, ensuring that AI technologies are deployed responsibly and effectively. By aligning AI initiatives with business objectives, fostering an ethical AI culture, and continuously monitoring and improving AI practices, organizations can harness AI’s transformative potential while safeguarding against risks and maintaining stakeholder trust.

    Continuously Assess and Address AI Risks

    Continuous monitoring of AI systems is vital for identifying and mitigating risks. Organizations should implement robust monitoring frameworks that track the performance and impact of AI models. This includes regular audits to ensure compliance with governance policies and regulatory standards. Developing contingency plans to address potential AI failures or misuse is critical for effective risk mitigation.

    AI Transparency Assurance

    Transparency must be a driving principle for an effective AI Governance initiative. Organizations should maintain clear documentation of AI models, including their development processes, data sources, and decision-making logic. Providing stakeholders with access to this information fosters trust and enables independent audits. Additionally, establishing clear accountability structures ensures that individuals responsible for AI initiatives are answerable for their outcomes.

    Your Organization, Its Business Goals and AI

    The initial step in developing a practical AI Governance framework is to ensure that AI initiatives are aligned with the company’s overall business goals. This ensures that AI projects contribute to the organization’s strategic goals, such as improving operational efficiency, enhancing customer experience, or driving innovation. Clear articulation of how AI initiatives support business objectives helps in securing executive sponsorship and resource allocation.

    AI Transparency Assurance

    Transparency must be a driving principle for an effective AI Governance initiative. Organizations should maintain clear documentation of AI models, including their development processes, data sources, and decision-making logic. Providing stakeholders with access to this information fosters trust and enables independent audits. Additionally, establishing clear accountability structures ensures that individuals responsible for AI initiatives are answerable for their outcomes.

    Your Organization, Its Business Goals and AI

    The initial step in developing a practical AI Governance framework is to ensure that AI initiatives are aligned with the company’s overall business goals. This ensures that AI projects contribute to the organization’s strategic goals, such as improving operational efficiency, enhancing customer experience, or driving innovation. Clear articulation of how AI initiatives support business objectives helps in securing executive sponsorship and resource allocation.

    Promote Ongoing Education and Adaptation

    The field of AI is rapidly evolving, making continuous learning and improvement essential. As with anything, investing in ongoing training and real-time development of your users will ensure they are kept abreast of the latest changes and updates around AI and its related government practices.

    Fostering a mindset of continuous improvement enables organizations to adapt effectively to emerging challenges and opportunities in the AI landscape.

    Form a Multidisciplinary AI Oversight Team

    Establishing a cross-functional Microsoft Copilot AI Governance committee is critical for overseeing AI deployment across the organization. This committee should include representatives from various departments such as IT, legal, compliance, risk management, and business units. 

    The diverse perspectives within the committee ensure that all potential risks and opportunities associated with AI are adequately addressed. The committee’s responsibilities include setting governance policies, reviewing AI projects, and ensuring compliance with regulatory standards.

    EPC Group has developed a granular strategy around forming your organization’s internal AI Governance committee and identifying the team members and related executive stakeholders who can ensure the long-term success of your organization’s AI transformation.

    EPC Group's AI Consulting Services

    EPC Group is the industry leader in AI Consulting Services and has written ground breaking strateiges around proper implementation of Microosft Copilot. From organization’s beginning their AI journey to those ready to push the limits.

    Proven Results

    EPC Group has engaged with thousands of clients over the past 27 years and has been on the forefront of Artificial Intelligence and has the proven track record to ensure your initiatives success.

    AI Roadmap Development

    EPC Group will guide you from the very beginning of your AI journey. We will help you in selecting the right path, picking the key strategies and implementation methods and ensuring you can manage your AI effort internally.

    We work with clients of all sizes and in all vertices and also hold records on some of the largest deployments ever successfully undertaken.

    EPC Group has experts in not only all areas of AI consulting but also experts in:

    – Microsoft Fabric Consulting

    – Power BI Implementations

    – Power Apps Development

    – Data Warehousing

    – AI Roadmap Development

    – Microsoft Copilot Consulting Services

    – Microsoft 365 Full Stack Consulting

    – Data Scientists with Proven Track Records

    AI Governance: The Summary

    Implementing AI Governance best practices is vital for organizations seeking to exploit the potential of their AI initiatives fully.

    Practitioners recognize that proactive governance helps prevent poor decisions, stimulates creativity, optimizes capital, and ensures the realization of project or company benefits when implemented correctly.

    As AI Governance continues to evolve, companies will need to develop robust Copilot AI Governance capabilities to maximize the effectiveness of their AI projects. Looking forward, the advancement of AI Governance will require organizations to continuously refine these capabilities to maintain competitiveness and fully leverage their AI investments.


    Errin O’Connor, the AI architect and visionary behind this white paper, has been instrumental in driving EPC Group’s AI practice. Errin O’Connor founded EPC Group over 25 years ago and is a Microsoft Press 4-time best-selling author on Power BI, SharePoint and Microsoft 365 and is currently working on a new AI and Microsoft Copilot best practices publications.

    Published: July 2024