Data in Check

Data in Check Mastering data governance for AI success

Contents Introduction Data governance is the backbone of secure AI adoption 1 3 Strengthening data The relationship between governance for AI governance, security, transformation and responsible AI 2 4 The role of data stewardship Updating data governance in AI transformation: Origins, framework to support quality, and reliability AI adoption Conclusion Effective data governance for AI enablement with Microsoft 365 2

Introduction Data governance is the backbone of secure AI adoption Generative AI holds the promise of business transformation—but Data quality and availability: realizing that potential depends AI systems rely on large, high-quality on the quality and availability of datasets to provide the best response your business data. possible with proper context from your organization’s unique workflows. Organizations prioritizing strong Better data quality leads to more data governance position themselves effective AI insights and predictions. to gain a competitive edge with AI. Compliance: Strong data governance Data governance helps ensure that ensures adherence to regulatory data queried and generated by AI requirements, reducing the risk of is correct, secure, controlled, legal issues and fines. and compliant. Security: Proper data labeling and management protects sensitive information from unauthorized access, inappropriate sharing, and breaches. Trust: Reliable data governance builds stakeholder confidence in AI outputs, fostering greater adoption and support for AI initiatives. 3

This e-book introduces critical data Generative AI use cases governance practices that create an AI-ready organization. It covers how to set up robust data quality Accelerate communication: Draft standards, ensure compliance with personalized content faster, freeing regulatory requirements, and up time to build relationships and implement data protection and collaborate. security measures. Whether it’s Improve efÏciency: Reduce time keeping sensitive meeting content spent on routine tasks, enhance pro- from being shared externally or ductivity, and cut costs. guarding against data breaches, Enable innovation: Help generate you’ll learn strategies for building ideas and proposals for new prod- trust in AI systems and making data ucts and services. readily available to drive actionable Personalize customer experiences: insights. By adopting these practices, Tailor content and recommendations your organization can apply the to drive loyalty and engagement. full potential of AI for continuous innovation and competitive advantage. 4

1 Strengthening data governance for AI transformation Every enterprise has policies and processes governing data usage. Data governance frameworks vary in maturity and comprehensiveness, but few have been fully optimized for AI. While many data governance best practices stay the same, such as ensuring data accuracy and consistency, other aspects need updates to maximize AI investments. Let’s look at a few key areas. Data visibility Detailed knowledge of data flow within launch, governance must now extend to managing AI-generated content, AI systems makes it possible to find and mitigate unauthorized or inappropriate ensuring that project-related documents, communications, and insights produced use. This visibility helps support security and compliance, protecting sensitive data by AI are secure. This means implementing safeguards so that only authorized team to maximize AI value. members can access and use AI to analyze or summarize project information. Traditional data governance has focused on knowing where data lives and Additionally, by managing data processing controlling access. However, as AI becomes more integrated into business and storage volumes, organizations can operations, data governance needs to better control the operational costs associated with AI. keep up with evolving security needs. For instance, with a highly sensitive product 5

1 Strengthening data governance for AI transformation Data quality User management AI amplifies the importance of high-quality With AI, users interact with data in data, as poor data quality directly affects AI sophisticated ways. They communicate outcomes. When applying AI tools to query with AI systems like Microsoft 365 Copilot your business data, you want to make sure through natural language prompts. With that data is current and trustworthy. Equally permission and protections in place, AI can important is understanding the provenance access relevant context from data—such and quality of data your teams use to build as files, chats, and emails—along with their own AI models and apps. Regular external sources via plugins to generate data audits, stringent validation processes, a response. and proactive management help ensure data integrity. By focusing on these areas, It’s crucial to monitor the data used in organizations can achieve reliable AI these processes to ensure the AI provides results that drive better decision-making correct, grounded responses. Providing and innovation. transparency through footnotes or links to original sources helps users verify the information, reducing the risk of data misuse and ensuring compliance with regulations. 6

1 Strengthening data governance for AI transformation Compliance and eDiscovery Data security The rise of AI introduces new challenges Securing AI-driven operations should in compliance and eDiscovery (the handling involve Zero Trust principles at the of data for legal cases), particularly in identity level, which minimizes the risk managing AI-generated data and adapting of unauthorized access. Regular updates to evolving legal requirements. Updating to endpoints, including devices and data governance frameworks to address applications, reduce vulnerabilities that these challenges involves developing could be exploited. Awareness of the policies that cover AI-generated content, generative AI tools in use within the such as ensuring AI-produced documents or organization allows for the blocking of communications are tracked, categorized, unsanctioned or insecure applications, and stored securely. For example, policies which in turn prevents potential security may need to be updated to ensure that breaches. By limiting access to AI tools AI-generated emails or reports are tagged and data to only trusted personnel, and archived properly for future retrieval. organizations can achieve greater data integrity and protect their AI operations Enhancing eDiscovery capabilities would from potential threats. include integrating AI tools that can search and identify AI-generated content across various platforms. For instance, during a legal inquiry, eDiscovery tools must be able to find and retrieve specific AI-generated documents, summarize relevant communications, and provide clear audit trails to prove compliance. By updating these capabilities, organizations can better manage data during legal audits or investigations, ensuring that all relevant AI-generated information is accessible and defensible in court. 7

1 Strengthening data governance for AI transformation What is Zero Trust? Zero Trust principles Zero Trust is a security model that focuses on verifying every request as Verify explicitly: Always authenticate if it came from an untrusted network. and authorize based on all available Rather than assuming everything data points. within the corporate firewall is safe, Use least privilege access: Limit user this approach adopts the principle access with Just-In-Time and Just- “never trust, always verify.” Enough-Access (JIT/JEA), risk-based adaptive policies, and data protection. Assume breach: Limit damage, control access, ensure encryption, and use data to spot threats and strengthen defenses. 8

2 The role of data stewardship in AI transformation: Origins, quality, and reliability As organizations adopt AI to assist in business decisions, they must ensure that these systems can provide correct and reliable results. The data grounding AI responses must be available, consistent, and well-documented. Data stewardship supports trustworthy Transparency: Knowing where data AI by implementing governance policies comes from and how it changes helps that track where data comes from, check confirm the accuracy of AI-generated its quality, and ensure its reliability and outputs and ensures regulatory accuracy. These practices build the compliance by providing a clear foundation for AI systems that deliver understanding of the data’s history. dependable insights, enabling informed and confident decision-making. Traceability: Data lineage allows organizations to trace errors or Data lineage: inconsistencies back to their source, Understanding origins and simplifying troubleshooting and changes to information data correction. Impact analysis: Understanding how Data lineage tracks data’s journey through data is used by generative AI tools an organization. It documents the data’s helps assess the potential impact of origins, transformations, and destinations. changes in data sources or processing For AI-powered businesses, understanding methods on output accuracy and data lineage is vital for several reasons: business outcomes. 9

2 The role of data stewardship in AI transformation: Origins, quality, and reliability Data quality: Data reliability: Quality drives the value Ensuring trustworthy data of AI responses Data reliability means data consistently Data quality directly affects the reliability meets quality standards and is available when needed. For AI-powered businesses, of AI outputs. High-quality data provides the context for AI models to deliver reliable data is crucial for promoting trust correct and valuable responses to user in AI tools and the decisions they inform. Ensuring data reliability involves: inputs. Key aspects of data quality include: Accuracy: Data must correctly Data redundancy: Implementing represent real-world conditions. backup systems that prevent loss and increase availability. Completeness: All necessary data must be present and accounted for. Regular backups: Conducting frequent backups to safeguard Consistency: Data must be consistent against data corruption or loss. across different systems and over time. Monitoring and alerts: Setting up Timeliness: Data must be up to date monitoring systems to detect and alert stakeholders to data issues in and available when needed. real time. Disaster recovery plans: Developing and testing plans to recover data and resume operations quickly after disruptions. 10

3 The relationship between governance, security, and responsible AI Together, data governance and security create the backbone for responsible AI use. High-quality, secure data ensures AI works ethically and effectively. Key areas to evaluate include data classification, access controls, encryption, incident response, and regulatory compliance. Access controls Data classification Classifying data is a critical part of These controls regulate who can access controlling how AI tools handle sensitive AI-relevant data and which applications or identities have permission to interact information. Typically, data and meetings with that data. Weak access controls can are classified as general, confidential, or lead to unauthorized exposure of sensitive highly confidential. Proper classification information, increasing the risk of breaches ensures that AI only accesses appropriate information, reducing the risk of exposing and misuse. sensitive data to unauthorized users. Data governance plays a key role in enforcing these controls by restricting Misclassification, on the other hand, can data access to authorized personnel lead to AI processing data that should be restricted, resulting in security breaches and specific applications, ensuring that sensitive data is handled appropriately. or compliance issues. Effective data governance, whether through automated This not only protects data integrity but tools or end-user policies, ensures that data also ensures that AI systems operate on secure, trusted datasets, enhancing their is classified correctly, safeguarding sensitive information and supporting the ability of AI reliability and compliance. to deliver reliable and compliant outputs. 11

3 The relationship between governance, security, and responsible AI Encryption Regulatory compliance Securing data from interception and AI applications must adhere to regulations tampering using encryption helps ensure such as GDPR or CCPA, which govern data that generative AI tools can ground their protection and privacy. Non-compliance can responses in the correct context—such as lead to significant penalties and erode trust. work-related data, files, chats, and emails— It’s also crucial to understand where AI tools without risking data leakage. process data, as many free tools may handle data globally or outside of your company’s Data governance policies can mandate usual storage locations. Data governance robust encryption practices, protecting ensures that AI applications not only run data throughout its lifecycle. This approach within legal frameworks but also keep ensures that AI tools can deliver reliable data within the right service boundaries, responses while keeping your data secure aligning with your organization’s compliance and maintaining trust. standards. This approach supports ethical AI use and helps build trust in AI technology. Incident response Sensitive organizational data can be exposed through incidents involving generative AI tools that grant unauthorized access to files, emails, or other business data that systems use to generate responses. A proactive incident response plan is crucial in these scenarios. Without such a plan, the organization risks not only exposing sensitive data but also relying on compromised outputs from the AI. Data governance includes having detailed response protocols to quickly address breaches, minimizing their impact and preserving the reliability of AI systems. 12

4 Updating your data governance framework to support AI adoption In the evolving AI landscape, it’s important to identify specific areas of your existing data governance framework that might require extra attention. Rather than overhauling the entire framework, you can focus on areas that are most likely to evolve, directing your efforts where they will most enhance the value of AI while ensuring your data stays protected and secure. Here are some key insights to consider: Adapting policies Roles and responsibilities and procedures It’s essential to incorporate AI readiness across the roles involved with data AI involves new kinds of data collection, governance. Employees familiar with processing, and usage. Updating data AI data requirements can act as stewards policies can help address relevant needs to support data quality and compliance. around data privacy, regulatory compliance, Cross-functional collaboration among and ethical use. For instance, requiring legal, IT, and data science teams can data anonymization in specific instances help tackle AI-specific challenges can protect personal information, making more effectively. it safer to use throughout the AI lifecycle. 13

4 Updating your data governance framework to support AI adoption Adapting data standards Tools and techniques and definitions for effective data Standardizing data formats, definitions, governance and quality metrics simplifies the implementation of policies governing Align with business goals: AI tool usage within an organization. Ensure your data governance Clear data standards make it easier to strategy supports organizational decide which datasets AI tools can reason aims to enhance decision-making over to provide business context and which data users can upload for analysis. and efÏciency. This ensures that AI applications use the Automate routine tasks: Use most relevant and trusted data, enhancing automation for repetitive tasks their effectiveness while supporting like data classification and access compliance with organizational policies. management to reduce manual errors and improve security. Continuous improvement Ensure high standards of accuracy: Data governance is an ongoing process, Use validation and cleansing tools to support data integrity over time. especially with AI. Regular audits and updates to your governance framework Train users: Offer training in data can help adapt it to new AI developments governance tools and procedures to and regulatory changes. AI itself can be prevent mismanagement and used to check and enhance governance ensure compliance with policies. practices, potentially finding gaps and Conduct regular audits: Regularly suggesting improvements. This proactive review data practices to ensure they approach enhances compliance and are keeping up with regulatory and efÏciency as AI technologies evolve. organizational change. 14

Conclusion Effective data governance for AI enablement with Microsoft 365 Using AI responsibly requires robust data governance. Effective data governance ensures data availability, accuracy, and security, enabling AI to deliver reliable insights and foster innovation. Prioritizing data quality, compliance, and security enhances AI capabilities, driving better decision- making and maintaining a competitive edge. Microsoft 365: Empowering AI readiness Microsoft 365 offers best-in-class productivity apps with built-in tools for data classification, control, and protection. These features support your data governance framework, facilitating confident AI adoption when you’re ready. Microsoft 365 helps you ensure: Data quality: Support accuracy and reliability for AI outputs. Compliance: Adhere to regulatory requirements, reducing legal risks. Zero Trust security: Protect sensitive information from unauthorized access, breaches, and cyberthreats. Discover comprehensive productivity tools enhanced with AI options and robust protection to help your organization work efÏciently and securely. Explore Microsoft 365 ©2024 Microsoft Corporation. All rights reserved. This document is provided “as-is.” Information and views expressed in this document, including URL and other internet website references, may change without notice. You bear the risk of using it. This document does not provide you with any legal rights to any intellectual property in any Microsoft product. You may copy and use this document for your internal, reference purposes. 15