Dave Russell, Senior Vice President and Head of Strategy at Veeam Software, provides a detailed exploration of the increasingly complex challenge organizations face in balancing the need for data security and accessibility. Historically, data management has always been a delicate balance. This task started with the management of paper files but has since evolved as digitization has drastically expanded the volume of data being created, stored, and used. In recent years, the rapid adoption of Artificial Intelligence (AI) has further compounded this challenge, as businesses must now ensure that the data they use for AI applications is not only secure but also readily accessible and usable.

The explosion of data generated by digital tools and AI applications has placed tremendous pressure on organizations to maintain data that is both resilient and accessible. AI systems rely heavily on vast amounts of data, and while the initial excitement around generative AI was palpable, organizations have now turned to this technology with a focus on extracting tangible business value from the data they already have. This increasing reliance on AI has escalated the demand for data that is not only accurate but also available in real time, ready for analysis, and capable of reacting to changes as they occur. Inaccuracies or inconsistencies within the data can undermine the effectiveness of AI, rendering its outputs unreliable. As the saying goes, “garbage in, garbage out,” which highlights the importance of feeding AI systems with clean, correct data.

At the same time, businesses face mounting pressure due to growing regulatory frameworks aimed at improving data security. Governments around the world are introducing a wide range of data protection regulations designed to secure data in an era of digital transformation. Regulations such as the NIS2 Directive and the EU AI Act are particularly focused on extending the line of custody over data, ensuring that organizations are not only managing their data securely but are also fully aware of how that data will be used in emerging technologies like AI. This increasing regulatory focus on data security is also prompting organizations to reassess how they approach data storage, especially given that much of the data collected in the past was not intended to be used for AI-driven purposes. This means organizations must now adapt their data management practices to accommodate modern technologies and the evolving security standards that go hand in hand with them.

These challenges are compounded by the need for cross-functional collaboration within organizations. While the task of ensuring data security primarily falls to the chief information governance teams, it is clear that meeting the demands of AI-driven data resilience requires cooperation across various teams, including IT security, legal, and data management. Collaboration is crucial as organizations strive to navigate the balance between enabling AI applications to access the necessary data while maintaining strict data security and compliance with new regulations. According to the Veeam Data Protection Trends Report 2024, a significant number of organizations have acknowledged a “Protection Gap” between the amount of data they can afford to lose and the frequency with which their data is backed up. While this gap has been narrowing in recent years, the rapid adoption of AI technologies that require ever-larger data pools could cause this gap to widen once again if businesses do not take proactive steps to address it.

The report indicates that the gap between the desired level of data protection and actual practices remains an ongoing concern. As businesses begin to implement AI technologies more extensively, the demand for data protection that ensures both security and availability becomes more pressing. Russell points out that while this challenge has existed in various forms for many years, the nature of the systems involved and the scale of the data have changed. AI technologies, by their nature, increase the amount of data organizations need to manage while also creating additional complexities related to security and privacy. Therefore, organizations must continuously assess their data management and protection practices, especially as they integrate AI into their operations.

Russell emphasizes that, ultimately, the solution to these challenges lies in effective data backup strategies. Backups play an essential role in ensuring that data remains secure, accurate, and accessible, which is crucial as organizations adopt AI technologies. In addition to their fundamental role in protecting data, backups also serve as a valuable tool for compliance with emerging regulations. For example, organizations developing AI models or working with large language models (LLMs) can use backups to track and document how data is used, providing a vital record that proves compliance with regulatory standards. Given that AI systems can sometimes operate in ways that are not fully transparent, having a robust system of data backups ensures that organizations can trace the data used by AI applications and demonstrate that their data is secure at all stages of the process.

Data backups have long been recognized as a cornerstone of data security practices, and their importance will only grow as AI technologies become more deeply integrated into business operations. While achieving total data security is an ongoing challenge, Russell argues that quality data backups provide a safety net that organizations can rely on to mitigate risks. These backups offer businesses the confidence that they can recover from potential data losses or security breaches, all while ensuring that they remain compliant with ever-evolving regulations.

In conclusion, as AI continues to reshape industries and organizations look for new ways to leverage their data, the task of ensuring data resilience, security, and compliance will remain a top priority. The key to navigating this complex landscape lies in adopting a comprehensive approach that involves collaboration across various organizational functions, a continuous reassessment of data protection strategies, and a strong emphasis on data backups. By focusing on these critical areas, businesses can effectively manage the risks associated with AI-driven data use and unlock the full potential of these transformative technologies.