Lowering Data Risk Requires Deep Insight Into Unstructured Data
In enterprises, the greatest data risks often lie in the unknown—unknown sensitivity, unknown access rights, and unknown storage locations. IT teams are responsible for managing these risks, and having transparency about what those risks are, their potential impact, and mitigation costs is critical.
Sometimes, it may even be more cost-effective to accept certain risks rather than immediately mitigate them. But when unknown risks lead to compliance violations, breaches, or data loss, the consequences can be severe—financially and operationally.
This blog explores how organizations can gain visibility into unstructured data, assess sensitivity and risk, calculate potential costs, and automate data risk management.
How to Gain Visibility Into Unstructured Data
Most enterprise solutions can report file type, size, location, and permissions. While this is helpful for basic storage management, the real risks often reside within the content of the files themselves.
Discovering and classifying sensitive data at scale is now possible thanks to AI-powered tools that can analyze unstructured data. These solutions review content, identify sensitive information, and flag it for security purposes.
AI classification provides visibility into:
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Document type (resumes, W-2s, invoices, government forms, etc.)
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Personally Identifiable Information (PII) including names, addresses, dates of birth, social security numbers, and banking information
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Foreign language content
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Organization-specific data attributes
Automating discovery, classification, and tagging of unstructured data dramatically reduces manual effort while providing structure for proper storage and security. Enterprises simply cannot manage the volume of unstructured data manually.
How to Identify and Reduce Risk in Unstructured Data
Classification alone is powerful, but the true value comes from understanding the scope of risk and financial impact. Modern enterprise data management solutions can assign value to content types and calculate liability for potential exposure, including regulatory compliance requirements.
Once financial risk is quantified, IT teams can make informed decisions about mitigation strategies—whether that involves quarantining, archiving, deleting, or changing permissions on certain data. Configurable workflows allow risk reduction steps to be automated at the file level, making ongoing management efficient and precise.
Continuous Risk Monitoring and Management
Data protection is no longer an annual project. Continuous monitoring of unstructured data across hybrid enterprise environments is now essential. AI-driven governance tools can detect misclassified data, identify sensitive content at risk, and flag incorrect storage or access settings.
This level of oversight ensures organizations remain compliant with evolving regulations while providing IT teams with real-time risk assessment dashboards. Such dashboards and reports are critical for audits and demonstrate both compliance and improvement over time.
Automating unstructured data risk management allows enterprises to manage ever-growing volumes of unstructured data while reducing operational, financial, and compliance risks.
Learn More
Read our whitepaper to discover how to automate data risk management and protect your organization in an era of explosive data growth.
