This is the fifth article in our series on data management and its role in building responsible AI. As AI becomes more integrated into our lives and businesses, protecting sensitive and personal data has never been more critical.

In this article, we explore how your organization can approach data privacy and security using effective governance strategies. Drawing from the DAMA-DMBOK Framework and the latest AI Index Reports, we highlight practical steps you can take—such as setting clear policies and using the right tools—to protect data and build trust in your AI systems.

Stay with us as we continue to share how strong data practices can help you develop AI that is secure, ethical, and responsible.

Primer

In today’s digital world, almost everything we do generates data. From online shopping and banking to using AI chatbots and mobile apps, our personal and professional information is constantly being collected, stored, and analysed. While this brings many benefits, it also raises serious questions about how this data is used and protected.

One of the biggest concerns today is the privacy and security of sensitive data. This includes personal details, health records, financial information, and company secrets. When this kind of data is misused or exposed, it can cause real harm such as identity theft, financial loss, and damage to people’s trust.

As AI systems become more powerful and data-hungry, the need for strong data governance strategies grows. Organizations and governments around the world are now working to create rules and processes to keep data safe and make sure it is used responsibly.

Understanding the Challenge

According to the AI Index Report 2024, privacy, security, and trust are top concerns for both companies and the public. Businesses are facing new risks as AI systems can accidentally leak private data or become targets of cyberattacks. Meanwhile, people are becoming more worried about how their data is collected and used. In fact, over half of Americans now say they are more concerned than excited about AI.

The DAMA-DMBOK2 guide on data management explains that data privacy means protecting people’s personal data, while data security means keeping data safe from unauthorized access or harm. Together, they are key parts of responsible data governance.

But protecting data isn’t easy. Many companies still don’t have clear policies in place, and even when they do, those rules aren’t always followed. In the rush to adopt AI, some organizations forget to check if their systems meet privacy and security standards.

Building Strong Governance Strategies

Good governance starts with clear roles, rules, and responsibilities. According to DAMA-DMBOK2, organizations need to define who oversees data, set up privacy policies, and regularly check for risks. They also need to train their staff and use the right tools—like encryption, access controls, and monitoring systems—to protect sensitive information.

One effective approach is to create a Data Governance Framework. This is a set of rules and best practices that guide how data is handled across an organization. It should include:

• Privacy policies that follow laws like GDPR or HIPAA.
• Security standards for how data is stored, shared, and accessed.
• Audit and monitoring processes to detect problems early.
• Clear ownership of data assets and who can use them.

According to the AI Index Report 2025, governments are stepping up too. In 2024 alone, U.S. agencies introduced 59 AI-related regulations (more than double the previous year). This shows that public institutions are taking data protection more seriously and are building frameworks for responsible AI.

Writer’s Personal Reflection

As someone who works with data and follows the development of AI closely, I believe that strong data privacy and security strategies are not just technical needs—they are ethical responsibilities. It is not enough to build smart systems. We also need to build systems we can trust.

I have seen firsthand how easily sensitive data can slip through the cracks, even in well-meaning organizations. That is why I think every company, big or small, should have a clear plan for managing and protecting its data. It’s not about perfection. It is about progress and transparency.

In the end, the goal is to make sure that data serves people, not the other way around. Good governance helps us do just that. It builds trust, reduces risk, and makes sure we are using technology in a way that respects human dignity and privacy.

References

1. AI Index Steering Committee. AI Index Report 2024. Stanford Institute for Human-Centered AI, 2024.
2. DAMA International. DAMA-DMBOK: Data Management Body of Knowledge, 2nd ed., Technics Publications, 2024.
3. AI Index Steering Committee. AI Index Report 2025. Stanford Institute for Human-Centered AI, 2025.
4. Pew Research Center. “Public Trust and Concerns About Artificial Intelligence.” In AI Index Report 2024, Chapter 9.