New Data Access Governance capability gives enterprises greater visibility into who has access to their unstructured data and whether those permissions are appropriate. 

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Datadobi, the intelligence and orchestration layer for unstructured data, has introduced Data Access Governance (DAG) within StorageMAP, giving organizations greater transparency into who has access to their unstructured data and whether those permissions align with corporate policy. 

The capability addresses an increasingly important challenge for organizations managing large, fragmented data estates. As more companies accelerate AI adoption and cyber attacks become more sophisticated, simply knowing where data is stored is no longer enough. Enterprises also need to understand who can reach that data, what level of access they have and whether those permissions are consistent with business and security requirements. 

StorageMAP’s new Data Access Governance capability gives administrators visibility into access permissions across complex unstructured data environments. By identifying who has access to specific datasets and comparing those permissions against business and security policies, organizations can uncover overexposed data, reduce cyber risk and strengthen governance. 

This now becomes part of a broader data management cycle spanning visibility, understanding, decision-making and execution. Simply put, enterprises cannot effectively govern, protect or extract value from data that they cannot properly see or control. 

DAG builds on StorageMAP’s existing capabilities across data discovery, classification, risk assessment, lifecycle management and AI Data Readiness. Together, these capabilities are designed to give organizations a clearer understanding of the data they hold and how it should be managed across increasingly complex enterprise environments. 

That has become particularly relevant as organizations prepare more of their data estates for AI. Poorly governed access is already a security risk, but it becomes even more significant when data is being used to train, inform or support AI systems. If sensitive or overexposed information enters an AI workflow, the governance problem can quickly extend beyond traditional storage and into models, applications and automated processes. 

The challenge therefore begins well before data reaches AI. Organizations need confidence that the information being used is appropriately governed, accessible only to the right people and aligned with internal policies. This is becoming an important part of AI readiness as enterprises look beyond experimentation and begin deploying AI at greater scale. 

The capability was tested through trials with a number of beta customers. According to Datadobi, early-access customers often discovered gaps in their understanding of which critical datasets were exposed and who could access them. In many cases, responding to audits or security incidents still depended heavily on manual investigation. 

That can be particularly difficult in environments where unstructured data is spread across different systems, storage platforms and locations. Over time, permissions can become harder to track as teams change, projects evolve and datasets move between environments. The result is that organizations may retain access rights that no longer reflect how the business actually operates. 

Data Access Governance is designed to make those issues easier to identify by giving administrators a clearer view of existing permissions and where potential exposure may exist. 

The capability also has implications for regulated organizations operating under frameworks such as GDPR, HIPAA and PCI DSS, where demonstrating that access controls exist and are functioning as intended is an important part of governance and compliance. 

“Unstructured data is at the center of every major enterprise challenge right now, including AI readiness, cyber resilience, cost management, and regulatory compliance,” said Michael Jack, Co-founder and Chief Revenue Officer for Datadobi. “But organizations cannot address any of those challenges with data they cannot see or control. Data Access Governance gives teams the visibility to understand who has access to what, identify where risk exists, and take action to bring their unstructured data under control. It is a foundational step in turning unstructured data from a source of risk and complexity into a driver of business value.” 

As enterprises push more data into AI, analytics and automated workflows, the challenge is shifting from simply storing information to understanding how that information is exposed, shared and used. Data Access Governance reflects that change. 

The next phase of enterprise data management will be shaped not only by where data lives, but by whether organizations can establish enough trust around it to use it at scale. For businesses investing heavily in AI, cybersecurity and digital transformation, that trust is quickly becoming as important as the data itself. 

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