The first step in mining unstructured metadata for compliance needs is to get visibility into all metadata across file and object storage.
Enterprise IT compliance needs are constantly evolving. New regulations are passed every year, and internal policies change. Disruptive new technology trends, such as generative AI, create additional concerns and requirements. The risk of fines, legal exposure, or breaches from unmanaged unstructured data is massive and growing; all hands across IT and the business need to help reduce the risks.
In this article, we will focus on the impact of compliance on enterprise data storage teams dealing with massive volumes of growing unstructured data and how a strategic program for unstructured metadata management can intelligently assist by discovering and acting on files at risk of security and compliance violations.
Enterprise IT compliance for data storage requires understanding regulations (like GDPR, HIPAA), implementing strong security measures (encryption, access controls), managing data lifecycles (retention, disposal), maintaining audit trails, and ensuring data residency. Steps include data inventory, risk assessments, clear policies, employee training, and regular audits to demonstrate adherence to standards like ISO 27001 and SOC 2.
There are several trends making IT compliance more complex right now:
- States are passing more privacy bills, with 20 passed and five more in committee as of July 2025. The EU’s GDPR requirements are considerable for companies with European operations or customers.
- Updates to major standards like PCI DSS 4.0 and the System and Organization Controls (SOC) 2 framework are introducing enhanced authentication requirements and a stronger focus on risk management and cloud privacy.
- Sustainability remains a force in the global economy, with the EU Corporate Sustainability Reporting Directive (CSRD) leading the way for mandated reporting on ESG performance.
- The EU’s AI Act is known as the gold standard, while other countries and regions are enacting their own versions of controls around AI systems and data use.
- Industry-specific regulations, such as HIPAA in healthcare, carry robust data security, access, audits, and monitoring burdens on IT.
While most large organizations have compliance departments that work in concert with cybersecurity, analytics, and data warehouse teams, data storage teams also have an instrumental role to play. Their ability to discover, enrich, and leverage file metadata can help identify regulated and protected data sets that are being stored and shared outside of compliance rules.
See also: Data vs. Metadata: The Overlooked Challenge in Data Management
How Unstructured Metadata Management Supports IT Compliance
Storage system-generated file metadata provides useful context and detail about unstructured data, which can help track data lineage, data owners, usage, and access, and demonstrate adherence to regulations like GDPR and HIPAA. This “data about data” acts as a foundational layer for data governance. By enriching metadata with additional tags describing file content, IT can locate sensitive data that might have been inadvertently moved to non-compliant locations or copied and stored insecurely, such as:
- PII and PHI data
- Internal proprietary data, such as intellectual property
- Confidential customer documents such as contracts, invoices, and payment information
- Sensitive project data
- R&D files
- Hidden sensitive data within other documents, such as shared meeting notes and transcripts
- Legal hold and surveillance data.
Once an organization queries, tags, and classifies data sets for security and compliance keywords, users can manage data to support compliance and governance activities. Managing data security is crucial, especially in the age of AI, where unstructured data is the fuel for AI.
Data Lineage
Metadata tracks the date of creation, movement, and modifications to data, which helps demonstrate how sensitive information has been handled to meet regulatory requirements. For example, a file tiered from on-premises storage to the cloud may still be accessible from the original location, but its data lineage should show that it is now stored in the cloud. This is especially important to track if the data is then fed to AI.
Policy Compliance
By identifying data owners, access rules, and usage guidelines through metadata, companies can ensure that sensitive information is protected and used only as authorized. Metadata monitoring is also important to implement policy-based retention and deletion policies based on the age of data and file type. For instance, in healthcare, some medical images must be retained for longer than others, depending upon the disease category and/or demographic.
Auditing
A comprehensive unstructured data catalog that indexes data across storage can report on data movement and usage to regulators, such as laws for data collection and processing under GDPR, or to track data governance for AI. It can also identify ex-employee data and duplicate data that can be purged to reduce the attack surface and deliver one version of the truth.
Centralized Discovery
A centralized metadata catalog for unstructured data allows users to easily search for and understand data assets across the enterprise, bringing structure to these data sets across storage silos. This not only allows IT to locate sensitive and protected data quickly to avoid data leakage and mishandling, but it also allows any authorized user the ability to find the files that they need quickly for their projects. In the age of AI, this easy searchability and identification of required data sets is pivotal to gaining a competitive advantage.
Real-Time Monitoring & Mitigation
Automated tools and processes help IT and cybersecurity teams monitor data quality and changes in real-time, as data is constantly on the move and in transformation. IT needs ways to automatically scan for PII, for instance, and confine it if discovered in an insecure or non-compliant location.
Ransomware Protection
The ability to identify, tag, and continuously move “cold data” that hasn’t been accessed in a year or longer is a huge advantage because it reduces the overall attack surface by automatically tiering it to an object locked storage location, such as Azure Blob or AWS S3. Now, the cold data can’t be modified or accessed by cyber-criminals, and IT can deploy its strongest anti-ransomware protection on high-priority, active data. An unstructured data management strategy aligned with a ransomware protection strategy not only reduces risk but can dramatically decrease costs.
See also: Mining Metadata for Business Value: Why Context Matters
Getting Started with Metadata Management
The first step in mining unstructured metadata for compliance needs is to get visibility into all metadata across file and object storage. An unstructured data management solution can index and organize this metadata rapidly to show initial trends, such as the amount of data in storage, growth rates of data, the amount of rarely accessed data, orphaned, and duplicate data.
Standardization also plays a big role. A consistent tagging taxonomy or catalog ensures that teams across projects and storage environments apply the same definitions. Deciding whether to tag at the directory or file level is another key consideration. Directory-level tagging is far easier to manage since it reduces the overall tag volume, but it requires careful oversight to avoid misclassifying files that don’t belong.
Custom metadata enrichment is where organizations can add real value. By tagging files with dimensions such as project or PII, data owners support precise queries and more powerful analytics downstream. Collaboration is crucial here: IT can manage the infrastructure, but accurate tagging depends on input from the scientists, researchers, or business users who understand the data itself.
Automation is the only way to handle the sheer scale and complexity of modern metadata. Unstructured data management platforms and catalog tools can apply, track, and persist metadata across hybrid environments, far beyond what native storage systems can support. They can automate workflows to find and move protected data from the wrong locations continuously and in accordance with internal policies and industry regulations.
An iterative, systematic metadata management program for structured and unstructured data can reduce risk in a time when threats are proliferating, while making all enterprise data more discoverable and useful for IT and departments alike.
