As organizations increasingly integrate artificial intelligence (AI) into their operations, the importance of robust data governance cannot be overstated. Data GRC (Governance, Risk Management, and Compliance) form the bedrock upon which effective AI programs are built. These frameworks ensure that data is managed properly, data objectives are achieved, uncertainty and risks are mitigated, and compliance is maintained to ensure that organization acts with integrity, all of which are crucial for the ethical and efficient use of AI.
Here are key data governance principles necessary for the successful deployment of AI programs in organizations:
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1. Ensure High-Quality Data for AI Systems
AI systems rely on high-quality data to generate accurate and reliable outputs. Data governance is critical in establishing processes that ensure data accuracy, completeness, and consistency. Automated data classification, validation, normalization, enrichment, entitlement, and tracking of lineage and chain of custody applied by data governance policies help maintain data integrity throughout its lifecycle. This is essential because AI algorithms can only perform as well as the data they process; any errors or inconsistencies in the data can lead to flawed decisions and insights. [read this blog on AI hallucinations]
2. Facilitate Data Accessibility While Maintaining Data Security
Data democratization is crucial for leveraging AI across different sectors of an organization. However, broadly accessible data increases the risk of data breaches and leaks. Data governance frameworks address this by implementing sophisticated access controls, such as role-based access and data masking, which ensure that individuals only access data necessary for their roles. These frameworks can dynamically adjust access based on real-time analysis of events and risk factors, striking a balance between data accessibility and security. [read more on Zero Trust data security]
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3. Comply with Regulatory Standards
With the proliferation of data protection regulations like GDPR and HIPAA, in combination with AI governance regulations like the EU AI Act, organizations must ensure that their AI applications comply with legal standards. Data governance provides a structured approach to compliance, incorporating features such as automated data handling rules, chain of custody, regular audits, and detailed reporting functions. These governance measures help organizations avoid hefty fines and reputational damage while using AI to process sensitive and personal information.
4. Manage the Complete Data Lifecycle
Data generally loses its value over time and risks associated with retaining the data in operational systems increase as more people have access to it, systems age and the security features of the systems generating the data are not regularly maintained. Effective AI relies on data that has value to ensure the reliability and relevance of the AI system. Eliminating data that is no longer appropriate or representative of the subject matter is essential to the AI system’s effectiveness. [read this whitepaper on data retirement]
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5. Support Scalability and Adaptation
AI requires an ever-increasing amount and variety of data to train its models and keep them up-to-date. A data governance platform and its classification framework must be able to scale and adapt to meet an organization’s changing data needs, technological advancements, and regulatory requirements.
6. Enhance Data Literacy and Culture
A successful AI implementation requires technological capability and a robust organizational culture around data. Data governance is pivotal in promoting data literacy, providing a framework to help employees understand the importance of data quality and data processing aligned with privacy and ethical standards. This cultural shift is necessary for organizations to fully leverage AI and make informed decisions based on AI-generated insights.
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Data governance is not just a support function but a fundamental component of any AI strategy in organizations. [Episode 42: The Growing Importance of Data Governance in an AI-Driven World] By ensuring data trustworthiness, security, and compliance, data governance enables AI technologies to operate effectively and responsibly. Organizations prioritizing robust data governance will be better positioned to harness the power of AI, drive innovation, and maintain a competitive edge in the data-driven digital landscape while ensuring their data is consistently, securely, and compliantly managed.
Join GRC 20/20’s Michael Rasmussen and Archive360 as we explore this topic in detail in the upcoming webinar: Steering Clear of the Pitfalls: Essential Data Governance Strategies for Effective AI Compliance.
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UPCOMING WEBINAR JUNE 6th!
Steering Clear of the Pitfalls: Essential Data Governance Strategies for Effective AI Compliance
*Livestreaming Globally* Register now to learn how Data Governance and AI Should deliver:
- Foundation of Trust
- Regulatory Navigation
- Avoiding Shadow AI
- Risk Management
- Practical Frameworks
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Michael Rasmussen is an internationally recognized pundit on governance, risk management, and compliance (GRC) – with specific expertise on the topics of enterprise GRC, GRC technology, corporate compliance, and policy management. With 30+ years of experience, Michael helps organizations improve GRC processes, design and implement GRC architecture, and select technologies that are effective, efficient, and agile. He is a sought-after keynote speaker, author, and advisor and is noted as the “Father of GRC” — being the first to define and model the GRC market in February 2002 while at Forrester. Michael has contributed to U.S. Congressional reports and committees, and currently serves on the Leadership Council of the OCEG and chairs the OCEG Technology Council, OCEG Policy Management Group, and the OCEG GRC Architect Group.
