A recent survey revealed that nearly three-quarters of business leaders plan to implement generative AI within the next 12 months. However, almost 80 percent were not confident in their ability to regulate access and governance of these AI applications, citing concerns around data quality, data security, and data governance. Unlike traditional systems that rely on fixed data sets and a standard query-response model, generative AI enables direct, natural language engagement, causing a shift in how users interact with technology and how data is accessed and processed.
This new data usage model marks a significant departure from previous applications, which tightly controlled and curated the use of structured and unstructured data. As such, our approach to data governance must evolve to prioritize data protection measures that ensure the confidentiality, integrity, and availability of information—principles that have long been foundational in data security—regardless of where that data resides. As we navigate this new landscape, it’s essential to rethink our strategies and frameworks to address the challenges posed by generative AI.
New Strategies for Data Governance
Data governance is essential because it dictates how data is accessed and used in AI applications and involves safeguarding the confidentiality, integrity, and availability of data, no matter where it resides. According to ePlus’ survey, business leaders are most concerned about data quality (61%), security (54.5%), and governance (52%), with data often siloed across various legacy systems. That’s why a robust protection program should prioritize data classification, identification, encryption, tokenization, real-time monitoring, and the management of mission-critical data sets. AI initiatives must break down these silos and modernize legacy data platforms to ensure proper data flow and integration.
It’s also essential to maintain visibility and control over data flows, access, and associated risks throughout the data lifecycle. This requires a clear understanding of where data is located, who has access to it, and ensuring compliance with relevant regulations.
Building a Strong Security Culture
Driving a strong culture of security within organizations is vital to a successful and holistic AI integration plan. While technology serves as the enforcement and execution point of a robust security program, comprehensive training for all employees—ranging from IT professionals and application developers to end-users—is equally crucial. Those engaging with generative AI agents and applications need to be well-informed about acceptable use and data protection practices to strengthen the organization’s overall security posture.
Security professionals must prioritize compliance and effectiveness to drive successful AI initiatives. It is key to align data governance programs with regulatory standards and assess their effectiveness concerning the data used by AI applications to achieve positive outcomes. Most importantly, aligning data strategy with business objectives allows organizations to maximize their AI investments, leading to cost savings, improved resource efficiency, and better experiences for employees, customers, partners, and stakeholders.
Developing a Comprehensive Data Management Strategy
Successful AI implementation requires a comprehensive data management strategy, including modernized data platforms to accommodate scalable processing and performance requirements and transitioning from isolated data repositories to a unified data platform to enable the enforcement of security and data policies effectively. Conducting data strategy assessments and reviewing data governance controls helps organizations understand their current data landscape and align data management practices with their AI goals.
Finally, integrating services across AI applications involves bringing together the right teams to build, support, and secure AI infrastructure. Managing this infrastructure and providing feedback loops for continuous improvement ensures optimized security controls, financial management, and a strong governance program.
Organizations that prioritize a holistic, data-led AI adoption strategy will seamlessly move from AI curious to AI ready, and ultimately to AI mature, putting them in an environment to succeed in today’s hyper-competitive AI landscape.
