How Organizations Manage to Understand Millions of Unstructured Data Files at Scale

For an ever-growing segment of organizations, making sense of unstructured data is fast becoming imperative. It is also far more challenging. Unlike structured data that’s stored in rows and columns, text-based, and easy to search in relational databases and data warehouses, there is no defined data model with unstructured data. Text is difficult to search and includes PDF, images, and video files. The data live in various forms in applications, data warehouses, and data lakes. Examples of unstructured data include emails, messages, and conversation transcripts.

Forbes reports that unstructured data is growing 55-65 percent per year, and organizations responsible for them soon will need to secure them to demonstrate regulatory compliance. By connecting unstructured data sources, you can gain a credible inventory of all unstructured data, discover hidden data that could put your organization at risk, and validate and enforce file entitlements. In this post, we’ll explain the process and approach to taking on the challenge of discovery and identification with unstructured data. We will also tell you how Imperva Data Security Fabric Data Discover and Classify simplifies this process.

Getting to know your unstructured data

Most organizations have little insight into what most of the unstructured data files they manage contain or what risk exposure they hold. Many threats from insider mishaps, malicious actors, cyber-attacks, ransomware, and other threats lurk in enterprise environments with files spread across on-premises and cloud data repositories. Gaining visibility and creating a framework to profile the data is a business imperative for data governance, compliance, security, and privacy. For a large enterprise, the data volume can be overwhelming, and automated tools are needed to help sort it out.

There are six essential questions every organization must ask to know their unstructured data:

  1. Is the data known and managed?
  2. Where is it?
  3. What is it?
  4. What policies are applicable?
  5. How is your data governed?
  6. Who is accessing it?

The first step in the approach is to establish enterprise-wide data classification

Once you get a credible inventory of your unstructured data mapped against your security and privacy policies, you can more easily discover the dark data in your repositories and determine if it can provide value to your organization or put your organization at risk and validate and enforce your organization’s data entitlements.

Bear in mind that unstructured data sources are much more diverse than structured data sources, encompassing hundreds of file and source types. A consistent, enterprise-wide classification framework must be agentless to enable you to understand both on-premise file servers regardless of data source type and cloud-native data sources like Google Workspace and Office 365. The framework should also provide exhaustive metadata about your data and feature ElasticSearch index rather than SQL to allow reports in seconds.

Imperva DSF Data Discover and Classify automates the process

Imperva DSF Data Discover and Classify enables you to leverage the automation capabilities of the Imperva Data Security Fabric (DSF) to execute data search, discovery, and classification of unstructured data at an enterprise scale, so you can find exposed sensitive data and protect it before it is discovered by auditors or hackers.

Imperva DSF Data Discover and Classify provides visibility into the exact location, volume, and context of sensitive data. Automated, cross-directory searches enable data professionals to do an extensive scan across multiple data source repositories simultaneously – in seconds. This finds the information required for an auditor question, an individual’s data lookup, or a data deletion request with maximum accuracy at scan speeds up to 100,000 words per second.

The Imperva DSF Data Discover and Classify engine analyzes metadata to determine file owner, data type, data category, and other information. It presents these findings to the Imperva Data Security Fabric hub for risk and security analysis. The DSF hub enables users to assess massive numbers of files for their current access profile, so security teams can ascertain whether any regulated data types may have over-privileged file entitlements. Imperva DSF has a built-in workflow manager to help automate remediation workflows should action be required. In addition, it can integrate with other enterprise tools that an organization might already be using, such as ServiceNow, improving collaboration across governance, compliance, and security teams.

Your organization’s ability to maintain data compliance and satisfy regulatory obligations relies (or soon will rely) on your ability to discover and classify unstructured data at scale. For example, data compliance with privacy regulations such as GDPR, CCRA, depends on maintaining an accurate inventory of your client’s, employee, and supplier’s Personal Data. GDPR specifically requires that retained Personal Data remain classified, and provisions with the regulation specify that you must implement “state of the art” security measures to protect it. Data protection is essential to all compliance regulations that empower regulators to impose non-compliance fines and penalties in the event of data exposure or breach. Imperva DSF Discover and Classify will help you mitigate non-compliance risk and the potential for data breaches from an unstructured data source. Classifying data by sensitivity categories such as Restricted, Confidential, Internal-only, or regulatory categories such as PII, Personal, HIPAA, and others, makes it easier for staff to apply the appropriate compliance and security controls.

Efficient data governance team collaboration on retention and deletion is almost impossible without a centralized tool to help manage the process. Imperva DSF Data Discover and Classify enables organizations to implement continuous governance processes through regularly scheduled data scans and inventory reporting, simplifying tracking and change management.

The unstructured data reporting features provide governance professionals with information that helps them collaborate on data management projects to determine which data files are no longer relevant to the business or identify which files contain a hidden business value. Obsolete files can be earmarked for deletion which helps the organization reduce IT infrastructure and maintenance costs.

Another way to fortify your data management initiatives is to leverage the unstructured data intelligence from a trusted data catalog of a supported platform such as Collibra.

Data discovery and classification is a foundational compliance and security process. Imperva DSF Data Discover and Classify automates the process so your governance, compliance and security staff can manage the process for unstructured data enterprise wide. Imperva DSF Data Discover and Classify helps simplify compliance, save time, save money and protect your organization from the risks of data breaches through sensitive data in unstructured data files. The datasheet explains Imperva DSF Data Discover and Classify in greater detail.

Contact Imperva to learn more.

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*** This is a Security Bloggers Network syndicated blog from Blog authored by Bruce Lynch. Read the original post at: