AI and the Future of Strategic Data Governance

Why data (and data governance) now sits at the center of AI 

When AI goes wrong, the usual culprit is messy, leaky, or poorly governed data. Take the incident at Samsung in 2023: Employees pasted sensitive code into a public chatbot, which is not how you keep your private data private. This avoidable incident led to new enterprise-wide restrictions and a rethinking of the company’s guardrails. The lesson here is not “don’t let employees use public AIs,” but rather “use governance frameworks that work.” Fortunately, there is modern guidance to draw upon. The National Institute of Standards and Technology (NIST) has put together an AI Risk Management Framework (RMF) that is both practical and sensible. 

The core value proposition: a “perfect” data strategy unlocks cross-industry AI 

Organizations that invest in clear pipelines, consistent metadata, and lifecycle controls derive more AI value and incur fewer breach costs. The burden of breaches has been hanging around the necks of organizations long enough that the average cost is now measured in millions of dollars. These clear data governance principles force AI value generation and inhibit breach costs. A sound data strategy has five pillars.  

1. Purpose-driven collection and minimization. Collect only what’s necessary, and make sure it’s tied to business use cases that you can easily identify. This isn’t just about being efficient and collecting the least amount possible; it’s about understanding your business so well that you can effortlessly define use cases for necessary data.  
2. Quality and lineage. You must define what “good data” looks like and know where your data is coming from so that every model feature is traceable back to governed data.  
3. Security and access control. This one’s fairly straightforward. Make sure the people who need access to your data can get it, and make sure the people who don’t need access can’t. The most effective way to do that is to enforce least-privilege access.  
4. Model governance. Once any given model is speaking to or with your data, you’ve got to maintain strong governance principles to ensure that the model is valid, the right kind of model, and being used in the right kind of way. Financial regulators have long required such discipline (e.g., rules like SR 11-7), a template many industries can adapt. 5. Secure-by-design operations. Use CISA as a guide for how to structure operations when you’re working with third-party services or general-purpose AI models. 

What this unlocks, sector by sector: 

• Healthcare: Using high-quality, longitudinal data, we can build clinical decision support, operational forecasting, and imaging triage—while keeping PHI safe, U.S. hospitals are increasingly turning to AI. The NHS and U.K. regulators are moving these uses of AI to the mainstream—through explicit information-governance guidance. 
• Financial services: With datasets under governance, we enable the detection of fraud, assist investigations tied to anti-money laundering, and perform real-time risk scoring—done under rigorous model-risk policies that evolved from SR 11-7. 
• Energy & utilities: With data sets neatly governed by NERC and the CIP, we use AI for predictive maintenance, class agents, and other means to enable grid stability—while tying up all the loose ends that would otherwise leave us vulnerable during identity, configuration changes, and incident response. • Public sector: We govern our data smarter, do a better job of finding it, and make it much more reusable. Using the U.S. Federal Data Strategy as playbook, we enable generative AI, and in so doing, draft much of the services content we provide. 
• Retail & consumer services: All of the above-mentioned uses of AI are either explicitly or implicitly tied to user rights—and here’s a prime example. Following Article 5 of the GDPR, uses of AI for marketing are done in a way that minimizes data use and respects user rights.

The organization shift: building the AI-ready enterprise 

An ideal strategy is not a document but the underlying capacity to implement it. A perfect strategy isn’t a document; it’s operating muscle. To use AI and manage its risks effectively, we recommend: 

A cross-functional council of data and AI experts. This council should comprise security, privacy, legal, and domain experts. They should know your product inside and out, understand the risks to your organization and your customers, and be deeply involved in using and governing data and AI. Their partnerships are indispensable. This council should jointly oversee the governance of data and AI across your organization. 

Investing at the right level. Governance is not free, and paying for it generates a far better ROI than “suddenly making everything AI-safe with insurance.” If you need to, secure seed funding from somewhere else in your organization to stand this council up and pay for the sorts of people who should make it up. And help them stay together and work well for the long term. 

Choosing the right governance model 

There is no universally applicable model, but they all share a common thread: accountability with fit-for-risk controls. 

• For regulated industries like healthcare, finance, and energy:

• Healthcare: “Adopt a privacy-preserving pipeline model,” said Recommind CEO, Eric L. Severson. “You need to have a very controlled system that ensures that the data’s privacy is being preserved, de-identifying it before it makes any appearance in a model. If human beings are not de-identified in the ML [machine learning] process, you’re violating the law, and you’re taking a privacy risk.” 

• Finance: Financial institutions are adopting a three-lines model for their AI systems: 

1. Development and business own model and data controls. 
2. Independent validation challenges key assumptions the model makes. 
3. Audit and board oversight ensure that the previous two lines are not slack in governance. 

The road ahead—summary and phased plan 

Winning organizations make data governance strategic—and do so for a reason. It’s the difference between being in the headlines for taking risks with AI and being an AI leader with a sustainable, measurable competitive advantage. Why? Because government and industry leaders are lifting the AI risk floor. The EU’s AI Act is about to unfurl some number of required “governance” processes throughout organizations. The NIST AI RMF is already seen as requiring organizations to inventory risks across the many kinds of sensitive data they hold. Forewarned is forearmed! Come up with a clear plan of how to scale this sensitive data inventory across the organization within the next 6 months. 

And use this data to inform a clear reckoning across the governance bodies of the organization about the many kinds of risks that AI (and our AI mandates) pose to the organization. Far better that we take up governance/leadership as a task set than be led via risk judgment en route to some high-stakes port of call. Construct it thoughtfully, embed it in all the right places, and allow it to serve the kinds of use cases that your customers and regulators can rely on. 

• Sources/references : 
• Reuters+2NIST+2 
• OECD+1 
• Federal Reserve+1 
• Digital Strategy EU+2NIST Publications+2 
• NERC  
• Federal Data Strategy+1  
• DPR   
• NIST Publications  
• CISAEDPB+1  
• HHS.gov  
• Federal Reserve