How Federal Agencies Can Improve Data Discovery and Classification

The federal government is facing down the challenge of big data with old-school tactics that can leave gaps within data discovery and classification. It’s time to move on to new practices, with modern technologies that can help supercharge data management.

“Most agencies perform data discovery and classification using manual tools and respond on a must perform basis, such as when required by an audit.  For most agencies, data proliferation and the network boundaries have expanded over time.  Agencies have greater responsibilities but may not have had adequate availability of their staff to monitor the comprehensive environment.  This can result in the wider network being left undiscovered or not verified for the classification of data allowed in the various data locations,” says David Ortega, principal solutions architect at Thales.

To address these gaps in classification, agencies need to seek out policy-driven, automated and repeatable solutions to advance data discovery and classification, Ortega says.

For example, Thales’ data discovery and classification tool provides a set of policies right out of the box that allows for data discovery based on predefined sensitive data types which include personally identifiable information, payment card information and healthcare records.

In addition to discovering and classifying sensitive data, these solutions need to have the ability to search for data across a multitude of different data sources: structured, unstructured and even cloud service provider data banks.

However, why should agencies that already have licenses for legacy environments or specific subsets/environments concern themselves with agencywide data discovery and classification tools?

The reason is simple, Ortega says: improving overall data protection. When using siloed data discovery and classification tools, the discovery and classification tools too often miss data or misclassify data that is under the agencies purview. The use of siloed discovery and classification tools can ultimately lead to a false sense of security.

“Discovering and protecting sensitive data using encryption across the hybrid enterprise is an essential step to mitigate risks of a potential breach such as ransomware.  If the data is encrypted, the data cannot effectively be held hostage to demand additional payments,” he adds.

And with the recent cybersecurity executive order, federal agencies should feel encouraged to take a second look and reassess the role of agencywide data discovery and classification tools.

How Agencies Can Prepare for Data Discovery and Classification Tools

Section 3(a) of the cybersecurity executive order highlights how the federal government needs to take steps to refresh its approach to cybersecurity by increasing its visibility into threats, while also safeguarding privacy and civil liberties.

In addition to issuing a call to action, the section charges federal employees to seek out security best practices that “centralize and streamline access to cybersecurity data” in the hopes of securing said data from bad actors.

Automated data discovery and classification tools can help agencies do just this, by providing greater insight into data. But what can agencies do now to ensure these tools have the greatest impact upon acquisition or implementation?

“The most important thing we’ve seen with both commercial as well as government agencies is that you have to have a solid data protection strategy with a working team that’s empowered to take action,” Ortega says. “Agencies should not hesitate to take advantage of new funding to modernize their data protection and discovery toolsets.”

Data protection strategies often outline a formal approach to safeguarding against corruption, compromise or loss. In addition to pinpointing the agency’s general approach to securing data, the framework should seek to address subpoints like methods of monitoring and review, data management and data breach prevention.

Implementing data protection strategies and modernizing toolsets aren’t the only actions agencies need to take. They also need to have comprehensive data governance policies in place to ensure data can move seamlessly from discovery to security.

In addition, ensuring data analysis is enterprise-wide and having toolsets that allow the protection of that data through encryption should be considered, Ortega says.

“The agencies’ data protection programs should focus on discover, protect and control.  Governance and tools should focus on the modern hybrid enterprise and understand that data has moved over time and that a comprehensive reset on data protection is most likely needed.  Without question, agencies should take advantage of funding opportunities as a result of the cybersecurity executive order,” he adds.