Jeff Foster, Redgate Software
Advances in AI will fundamentally change the use and management of data in enterprises, making embracing and understanding this shift crucial. For organizations that want to adopt AI at scale, the status of their databases is a key success factor.
Poor data quality, weak governance or evacuation supervision may even be the most ambitious AI program. In this case, the role of database administrators (DBAs) is becoming increasingly strategic and increasingly central in enterprise AI preparation.
Modern DBAs are no longer just guardians of performance and usability. They are managers of data ethics, security and compliance. Because this data is used in AI systems, usage becomes more complex and riskier, such as misconfigured permissions or algorithm biases that grow. Good news? By directly addressing the complexity of the database, DBA teams can create a foundation of trust and reliability that not only makes AI possible but can be productive.
Here are four key strategies for managing your database environment and preparing to successfully adopt AI.
1. Establish data governance around AI ready
In any data-driven organization, strong governance is not negotiable, and this is especially important when AI enters pictures. AI is only as good as fuel data. This means that well-defined ownership, strict access protocols, data quality metrics and strong lifecycle management are the basis for success.
Businesses should invest in data catalogs and genealogical tools to invest in their origins, how they can be converted, and how they can be used. This is essential to understand the input and output of AI models and defend these decisions under regulatory scrutiny. And, when it comes to compliance, don’t ignore data masking, especially when using production data in development or training environments. This is no longer the best practice; it is in line.
2. Think of auditing and monitoring as a continuous process
One-time audits no longer cut it, especially when AI systems rely on changing data to make real-time decisions. Continuous audits powered by data observability tools help ensure that your data remains trustworthy, models remain transparent, and your processes remain compliant.
In the context of AI, it is important to track how data flows into the system and how it is used. Tools should record AI model inputs and outputs, highlight exceptions, and exhibit any biased or inconsistent metrics. This not only prevents compliance risks, but also improves model accuracy and over time.
3. Align access controls with security and compliance goals
Security is a fundamental concern for any IT team, but it can be pressing when it comes to AI systems. As databases become easier to use, a wider range of stakeholders including data scientists, developers and third-party platforms, the risk of unauthorized access increases significantly
A strong access policy begins with multi-factor authentication and role-based access control. But it must be done further, and combined with regular licensing comments and strong access logging. Visibility access to who accessed what data, when and for what purpose is crucial – not only for security, but also for auditing and governance. It also enables organizations to connect database access to a wider enterprise workflow, thereby increasing transparency and accountability.
4. Make monitoring and documentation part of AI workflow
Performance and safety monitoring can no longer be isolated for treatment. To support enterprise AI, integrated and continuous monitoring must be captured not only uptime or query speed, but also the integrity and movement of the data itself.
Investing in 24/7 database monitoring ensures early capture and quickly address any potential issues, whether it is access patterns, pattern changes or a surge in security anomalies. Automation plays a crucial role here, helping teams expand oversight without increasing overhead.
Final Thought: Database Complexity is a Hidden Barrier to AI Success
A successful enterprise AI startup does not start with the model, it starts with the data. By addressing the complexity of the database, improving visibility, and aligning security and compliance efforts, IT teams can build a foundation that supports AI, rather than breaking it.
In this new era, DBA and IT leaders play a vital role in transforming innovation into impact. With the right strategies and tools, they can ensure that their organizations are not only ready, but also AI-Ai-Ristient.
Jeff Foster is the Director of Technology and Innovation at Redgate Software, Cambridge, UK, which helps solve complex database management problems throughout the DevOps lifecycle.
