Microsoft Copilot AI Governance Best Practices

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

UNDERSTANDING DATA GOVERNANCE

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.)

WHAT ARE THE CHALLENGES OF AI GOVERNANCE

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. 

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

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

BUILDING INTERNAL GOVERNANCE STRUCTURES

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.

EVALUATING AI’S EFFECT ON YOUR ORGANIZATION’S TEAM MEMBERS

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

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. 

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:

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.

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.

AI Governance: The Summary