How Automated Data Discovery Can Lower Your Data Risk

04.22.2022

Automate Your Data Risk Management

Lowering Data Risk Requires Deep Insight Into Unstructured Data

When it comes to enterprises and their data, the true risk lies in the unknown; unknown sensitivity, unknown access rights, unknown storage locations and volumes. As the stewards of an organization’s data, IT will always carry risk. And that can be okay as long as there is transparency and a clear definition as to what that risk is, how it has or hasn’t been mitigated, and any potential costs associated with that risk. Sometimes it can be more cost-effective to keep the risk versus mitigate it. But if data or compliance issues arise from an unknown risk, that’s when companies lose money and people lose jobs. This article discusses the capabilities and processes needed to gain visibility into unstructured data, determine data sensitivity and risk, the potential cost of that risk, and ways to automate data risk management.

A lack of understanding of the risk lurking within an organization’s unstructured data is no longer acceptable. This is why IT is investing more in ways to gain visibility into their unstructured and dark data – so they know what they are dealing with. This can be a part of cyber resilience efforts or just good hygiene to protect the organization from unknown risk. 

How to Gain Visibility Into Unstructured Data

Most enterprise data management solutions can identify file type, size, location and file permissions across content and storage platforms. This provides great information for managing the storage and security of that information. But the real challenge is that much of unstructured data’s risks live inside the contents of the files themselves. Discovering and classifying sensitive data within this enterprise content is not something organizations have ever been able to do at scale until recently.

Seeing into your dark, unknown data, requires artificial intelligence (AI) that can review and compare your unstructured data to known data types using advanced pattern matching algorithms. This technology can identify what the content is, then find sensitive information that needs to be flagged for security purposes.

AI classification of unstructured data can give you visibility into:

  • Document type (resume, W-2, invoice, standard government forms, etc.)
  • PII including names, ages, addresses, dates of birth, phone numbers, social security numbers, banking information, etc.
  • Foreign language detection
  • Data attributes unique to an organization 

Having a solution that can automate this discovery, then also classify your content and apply metadata, tags, and labels as appropriate to your unstructured data is a game changer in data management. There is too much data for people to manually manage. Enterprises need data management that can see into unstructured data and provide the necessary structure for proper security and storage.

How to Identify and Reduce the Risk in Unstructured Data

Seeing into unstructured data and classifying it is powerful. But the real value comes in being able to gain an understanding of the scope of risk, any financial impacts, and to then automate data risk reduction steps. As anyone in IT knows, carrying financial risk is acceptable as long as you know the potential impact and it is less costly than the fix. The key here is knowing the financial risk so you can make those decisions.

Intelligent enterprise data management will assign a value to each content type and match that up to the financial liability of that data being lost or exposed and will calculate the liability for regulations that your business adheres to. This allows IT to balance their financial risk with the cost to control, manage, and mitigate that risk. 

That said, there are also ways that data management solutions can work to continually reduce data risk. Data can be modified or transferred at a file level appropriately through configured workflows based on risk level and other data properties. This can include quarantining the data, archiving or deleting it, and changing permissions.

Continuous Risk Monitoring and Management is Now a Must

In today’s highly regulated climate of data protection, enterprises need to find new ways to reduce their data risk. Additionally, organizations should maintain the expectation that data breaches can’t be completely prevented. This is now no longer an annual project; it now requires continuous monitoring of the data being created across the enterprise hybrid environment. Data management with governance technology leverages AI to continually monitor content for sensitive data, incorrectly applied classification labels, or incorrect storage and security of that data that may expose the business to financial risk.

The constantly evolving data regulation landscape is becoming too much to handle for legal and IT teams to manage manually. They need enterprise data governance solutions to keep up with changing regulations and update risk exposure models and calculations accordingly. IT now must have risk assessment dashboards and reports that provide a real-time view of data vulnerabilities and associated risks. These reports are also critical for meeting the needs of regulatory audits and demonstrating both improvement and compliance. Enterprise data management that can see into unstructured data to identify and mitigate risk is a must-have for IT to continue to properly manage this continually growing pile of information and its associated risks.

Learn more about how to automate data risk management in our latest whitepaper.

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