A strong AI governance defense is essential to reduce risks, but it's not enough. Organizations need to develop a robust offense strategy to drive business value, accelerate digital transformation, and stay competitive in today's fast-paced technology landscape. This article explores the importance of balancing defense and offense in AI governance, and how chief data officers (CDOs) can lead the charge.

The rapid evolution of generative AI (genAI) capabilities, accelerated investments, and amplified data risks through AI applications have created a pressing need for organizations to develop a comprehensive AI governance strategy. A strong defense is crucial to protect organizations from risks, but it's only half the battle. A well-planned offense strategy is necessary to channel efforts towards areas where AI can generate business value and drive digital transformation.

According to Kurt Muehmel, head of AI strategy at Dataiku, "Governance is a strength that ensures AI is aligned with business objectives, produced efficiently, follows internal best practices, and promotes reusing components. AI governance thought of this way becomes not an obligation but a competitive differentiator." A strong offense guides business objectives, outcomes, and capabilities, focusing on areas where AI can drive business value.

Developing an AI governance strategy requires IT and data science teams to address deficiencies in how organizations balanced innovation and governance in the past. This includes learning from past experiences, such as the shift-left security culture in devops and the adoption of finops disciplines in cloud modernization. CDOs play a critical role in defining AI governance, as they have many data-related responsibilities that are foundational for safe and secure AI practices.

Kjell Carlsson, head of data science strategy and evangelism at Domino, emphasizes that "AI governance is needed to help organizations drive the trust and adoption necessary to transform the business using AI." CDOs must provide visibility, auditability, reproducibility, and control, and implement platforms that orchestrate, streamline, and automate governance activities.

Henry Umney, managing director of GRC Strategy at Mitratech, highlights the key priorities on the CDO roadmap for data and AI governance, including creating a clear definition of AI, building an AI model inventory, and benchmarking existing governance and risk management structures against frameworks like NIST AI RFM. CDOs must prioritize these practices effectively and communicate to business stakeholders how governance enables efforts to deliver business value.

Jeremy Kelway, VP of engineering for analytics, data, and AI at EDB, notes that "a CDO's roadmap should balance the adoption of transformative technologies like genAI with the critical need to maintain data sovereignty." This goes beyond risk reduction, as data sovereignty covers governance, observability, and jurisdictional boundaries, laying the groundwork for offensive strategies that drive growth, sharpen competitive capabilities, and enhance customer experiences.

Business leaders and other stakeholders must be involved in the AI governance strategy, including the chief information security officer, who should review data security posture management (DSPM) platforms to secure confidential or regulated information stores. CDOs should craft an AI vision statement, define a data strategy, and manage a roadmap aligning with a plan to drive AI offense capabilities.

Ana-Maria Badulescu, senior director of the AI lab, office of the CTO at Precisely, advises building a data and AI strategy and a culture of rapid yet responsible AI from day one, as adding it later is much more challenging and costly. The CDO roadmap should go beyond governance, providing a comprehensive, integrated solution that covers data quality, data observability, data catalog, data security and privacy, and location intelligence.

Heather Gentile, director of product management of AI risk and compliance at IBM, suggests reinforcing that the results of a model are only as good as the data on which it is built and trained. The transparency and explainability of governance also successfully accelerate and scale AI initiatives and business impact.

CDOs, data governance, and data scientists must also consider AI-specific capabilities, such as modelops, which monitors ML models for drift and other conditions necessitating retraining. An AI Data Bill of Materials (AI DBoM) is essential for responsible AI at scale and should be part of the CDO's governance strategy, tracking all data feeding AI models and ensuring quick project turnarounds with full transparency.

Rahul Auradkar, EVP and GM of Unified Data Services at Salesforce, notes that reducing tech debt caused by different data governance controls, manually classifying and tagging data, and the rise of data-driven decision-making has increased governance priorities for CDOs today.

Elements of an offense strategy in AI governance include driving efficiencies through AI-driven automation and internal data marketplaces, building customer trust and increasing engagement through personalization engines and ethical AI, improving services by using predictive AI to anticipate needs and reduce churn, accelerating product development with AI-powered market insights, and helping businesses stay ahead with cross-industry collaboration and strategic data sharing.

By treating governance as a built-in capability of data products rather than a separate control layer, organizations can accelerate innovation and time-to-value while improving their risk posture through standardized, reusable patterns. Data products with embedded governance controls become powerful building blocks for growth that help launch new customer solutions faster and expand into new markets more easily.

In conclusion, a comprehensive AI governance strategy that combines defense and offense is crucial for organizations to drive business value, reduce risks, and accelerate digital transformation. CDOs play a critical role in leading this charge, and by prioritizing AI governance, organizations can unlock the full potential of AI and stay competitive in today's fast-paced technology landscape.