As artificial intelligence becomes increasingly embedded in enterprise decision-making and operations, the quality, security and governance of organisational data are emerging as critical determinants of AI success. With businesses moving beyond data accumulation towards trusted, contextual and AI-ready data, robust governance frameworks are becoming essential to ensure reliable AI outcomes, regulatory compliance and responsible innovation.
Fragmented data environments, legacy systems, inconsistent ownership and evolving privacy and cybersecurity requirements, however, continue to challenge organisations seeking to scale AI responsibly. At the same time, generative AI is introducing new risks around data lineage, model reliability, bias, confidential information and the propagation of inaccurate information.
In this interview with AI Spectrum, Saurabh Misra, Senior Manager – Technology Risk, Governance, Audit & Cybersecurity, and Member of the ISACA Emerging Trends Working Group, discusses how the definition of valuable data is changing in the AI era and why data quality and trust are becoming strategic differentiators. He also shares insights into strengthening data governance, balancing AI innovation with privacy and security, managing generative AI risks, and the role of boards and business leaders in building AI-ready enterprises.
As AI becomes central to enterprise operations, how has the definition of “valuable data” changed? Is having more data still an advantage, or is the focus shifting toward having higher-quality, trusted, and AI-ready data?
The conversation has shifted from “more data” to “better data.” AI systems are only as effective as the data they are trained and operated on. Large volumes of fragmented, outdated, or poorly governed data can amplify bias, increase operational risk, and reduce confidence in AI-generated outcomes.
Today, valuable data is accurate, complete, contextual, well-governed, and trusted. It is also AI-ready, meaning it is consistently classified, lineage is understood, quality is measurable, and access is appropriately controlled. Organizations that invest in trusted data foundations will achieve more reliable AI outcomes than those simply accumulating larger datasets. In the AI era, data quality has become a strategic differentiator rather than just a technical consideration.
Many organisations struggle with fragmented data, poor data quality, and legacy systems. In your view, what are the biggest barriers to building a robust data governance framework, and how can enterprises overcome them?
The biggest challenge is rarely technology. It is organizational alignment. Data often resides across multiple business units with inconsistent ownership, conflicting standards, and legacy platforms that have evolved over many years. Without clear accountability, governance becomes fragmented and reactive.
Successful organizations treat data governance as a business capability rather than an IT initiative. This requires executive sponsorship, clearly defined data ownership, enterprise-wide standards, and continuous monitoring of data quality. Modern governance platforms, metadata management, and automation can significantly improve visibility, but equally important is fostering a culture where data is recognized as a strategic enterprise asset rather than a by-product of business operations.
With increasingly stringent privacy regulations and a growing cyber threat landscape, how can organisations strike the right balance between enabling AI-driven innovation and ensuring data security, compliance, and ethical use?
Innovation and governance should not be viewed as competing priorities. Organizations that embed privacy, security, and ethics into AI initiatives from the outset can innovate with greater confidence and resilience.
This begins with adopting principles such as privacy by design, security by design, and responsible AI governance. Strong identity and access management, data classification, encryption, continuous monitoring, and human oversight should become foundational controls rather than afterthoughts. Equally important is establishing governance structures that bring together technology, legal, risk, compliance, and business leaders to evaluate AI initiatives through both an innovation and risk lens. Organizations that integrate governance into the development lifecycle will be better positioned to scale AI responsibly.
Generative AI is amplifying both the opportunities and risks associated with enterprise data. What new governance practices or technologies should organisations adopt to ensure AI systems are trained and operated on accurate, secure, and trustworthy data?
Generative AI introduces new governance requirements because models can rapidly propagate errors, bias, or confidential information if underlying data is poorly governed. Organizations therefore need stronger controls around both data and AI models.
Key practices include maintaining robust data lineage, implementing continuous data quality monitoring, establishing model governance frameworks, and applying human oversight for high-impact decisions. Technologies such as retrieval-augmented generation (RAG), prompt controls, role-based access, AI model monitoring, and automated policy enforcement can significantly improve reliability while reducing risks associated with hallucinations and unauthorized data exposure. Governance should extend across the entire AI lifecycle, from data acquisition and model development to deployment, monitoring, and retirement.
Data governance has traditionally been viewed as an IT or compliance responsibility. How is this perception changing, and what role should business leaders and boards play in making data governance a strategic business priority?
Data governance is increasingly becoming a board-level responsibility because data now underpins strategic decision-making, customer trust, regulatory compliance, and AI adoption. Boards are recognizing that poor data governance creates not only operational and cybersecurity risks but also strategic and reputational risks.
Business leaders should define the value expected from enterprise data while ensuring accountability for its quality, security, and ethical use. Boards should regularly review governance metrics, data-related risks, AI initiatives, and investment priorities. By treating data as a strategic asset rather than simply an IT resource, organizations create stronger foundations for innovation, resilience, and long-term competitiveness.
Looking ahead, what do you see as the defining characteristics of an AI-ready enterprise? What practical steps should organisations take today to transform their data from a potential liability into a long-term competitive advantage?
An AI-ready enterprise is characterized by trusted data, strong governance, secure technology foundations, skilled people, and leadership committed to responsible AI adoption. Technology alone is insufficient. Organizational readiness is equally important.
Practical priorities include establishing enterprise-wide data governance, improving data quality, modernizing legacy environments, strengthening cybersecurity, and clearly defining ownership for critical data assets. Organizations should also invest in AI governance frameworks, workforce capability building, and continuous monitoring of AI performance and risk. Those that build trusted, well-managed data ecosystems today will be better positioned to realize sustainable business value from AI while maintaining stakeholder confidence and regulatory compliance.
