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Keeping Data Secure: The Governance Polices, Processes, and Oversight to Protecting Data

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Keeping Data Secure: The Governance Polices, Processes, and Oversight to Protecting Data
Companies are not doing enough to protect their data. Instead, they are mainly reacting after a problem occurs, but not anticipating or being proactive before an issue starts. That's the word from a number of industry experts who are sounding the alarm on AI applications and agents running on unstructured data that threatens to swamp even the most well-designed enterprise security infrastructures.
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Explore practical strategies for AI governance, from trusted data and security to transparency and human oversight. This DBTA Super Guide features expert insights and an actionable checklist to help organizations manage risk and put responsible AI into practice.
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High availability and operational resilience are the goals of every CIO. They know that hardware fails, networks go down, software has bugs, and people make mistakes. Strategies are put in place to mitigate against such incidents, but there are times when IT operations management is betrayed by the very systems that are supposed to help keep technology and services running.
For much of the past decade, cloud storage has been thought of as the inevitable future of enterprise infrastructure. The promise was simple: Eliminate capital expenditures, scale infinitely, and only pay for what you use. For some workloads, that model still makes sense. But as data volumes explode and the economics of infrastructure shift, organizations are beginning to take a closer look at the long-term financial implications of relying primarily on cloud storage.
As enterprises move from AI experimentation to intelligent applications and agents, context engineering is emerging as a critical foundation for success. Delivering the right context at the right time across structured data, unstructured content, business rules, and real-time signals is becoming essential for improving AI accuracy, relevance, and reliability.
Data has long been considered the "Achilles Heel" of artificial intelligence. Data managers and their organizations have been pressing forward full throttle to build data estates that can handle the relentless AI surge. Yet, beneath all this activity is a persistent concern about the readiness of data environments to support enterprise-scale AI efforts.
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