Usability Meets Security: Finding Harmony in AI-Driven Regulatory Times

Dave Russell, Senior Vice President and Head of Strategy at Veeam Software, explores the challenges organizations face in keeping their data secure, accessible, and compliant in today’s rapidly evolving digital landscape. Managing data has always been a critical concern, but the rise of digitization and artificial intelligence (AI) has dramatically increased the complexity of this task. As the volume of data grows exponentially and regulations tighten worldwide, organizations are under mounting pressure to strike a delicate balance between data usability and resilience.

The adoption of AI has significantly amplified the demand for enterprise data. AI systems, particularly those requiring real-time data to function effectively, depend on accurate and relevant information to produce meaningful results. While the initial excitement surrounding generative AI has cooled, businesses are now seriously integrating the technology to unlock value from their existing data. According to a McKinsey Global Survey on AI, 65% of organizations worldwide are using AI regularly. This surge in adoption highlights the critical importance of ensuring data resilience, as errors or inconsistencies in data can compromise AI outputs. The mantra “garbage in, garbage out” underscores how the quality of data directly impacts AI’s effectiveness. Organizations must also exercise caution in feeding sensitive, mission-critical, or customer data into AI systems, as mismanagement could have significant repercussions.

Simultaneously, governments around the world are introducing new regulations aimed at ensuring responsible data use. These include frameworks like NIS2 and the EU AI Act, which emphasize greater accountability for how data is stored, accessed, and applied, particularly in AI applications. Such regulations are reshaping how organizations approach data governance, requiring them to consider not just the current use of data but also its long-term implications as new technologies emerge. For many organizations, this means rethinking their data strategies and fostering collaboration across departments to ensure compliance. Achieving this requires a unified effort, with data governance teams working closely with IT, security, and operational teams to address evolving risks and responsibilities.

The challenges of maintaining data resilience are not new; they have evolved alongside advancements in technology and the increasing complexity of business environments. Despite these changes, the fundamental principles of data management remain constant. However, as AI becomes more deeply embedded in organizational processes, the scale of these challenges continues to grow. The Veeam Data Protection Trends Report 2024 reveals that 76% of organizations experience a “Protection Gap” between how much data they can afford to lose and how often their data is protected. While progress has been made in narrowing this gap, the exponential growth of data driven by AI adoption risks widening it unless organizations take proactive measures.

To address these evolving challenges, organizations must prioritize collaboration across teams. Risk assessments must be redefined to accommodate the demands of AI and align with regulatory requirements. This collaborative approach will not only help organizations meet compliance standards but also enable them to leverage their data more effectively for innovation and growth. While regulations provide a framework for action, organizations should not rely solely on these external mandates. Proactively monitoring and adjusting risk levels is essential, particularly when integrating transformative technologies like AI.

A cornerstone of effective data management is the implementation of robust data backup systems. Data backups are indispensable in ensuring data accuracy, security, and accessibility, even as AI-driven applications introduce new complexities. These systems also provide a clear record for demonstrating compliance with regulations. In an environment where AI systems often operate as opaque “black boxes,” reliable data backups offer an anchor of accountability, ensuring organizations can track and secure their data at any point in its lifecycle.

Although total data security is unattainable, organizations can significantly mitigate risks by adopting resilient data management practices. Quality data backups offer a critical safety net, enabling organizations to recover from errors or disruptions while maintaining operational continuity. As AI continues to redefine the role of data in business, organizations must remain adaptable, integrating resilience and security into their strategies to meet the demands of the future.

Ultimately, navigating the complexities of modern data management requires a forward-looking approach that combines innovation with responsibility. By fostering collaboration, prioritizing resilience, and embracing robust data governance, organizations can unlock the transformative potential of AI while safeguarding their most valuable asset—data. In doing so, they position themselves for long-term success in an increasingly data-driven world.