Organizations are processing more data than ever.

Fully leveraging different types of data is mission-critical to strategy and competitiveness across most industries today. As a result, the technology that enables sales, marketing, product, and operations is increasing in complexity to fulfill data ownership and processing requirements.

These conditions make it extremely difficult for data protection and privacy teams to execute policies that are up-to-date with the latest compliance regulations. These complex technologies fragment data sources and create barriers to entry for privacy teams.

When a lack of transparency prevents teams from gaining a complete understanding of data usage across the organization, you’re at risk of non-compliance.

Effects of Privacy Regulations on Data Classification Processes

The fast-changing regulatory landscape yields several new laws and specifications each year. This is especially true regarding the collection and processing of personal data by organizations.

For example:

The regulatory landscape continues to increase its jurisdiction and scope. It’s incumbent on your organization to adjust your privacy programs to adhere to these new requirements.

These rules are exhibiting more and more specificity regarding your obligations as data controllers and processors. As a result, your organization needs to become increasingly precise with the categorization and mapping of data.

If you automate data classification and mapping, you can enable compliance-informed decision-making for your organization. It will also help you remain agile to future regulatory changes.

Watch the webinar: Automating the Classification and Mapping of Sensitive Data​

Data Protection Programs and Your Tech Stack

Privacy and data security teams need to have ungated access to all data held by your organization. But when data exists in different platforms, databases, and software, it’s hard to know where to begin.

Without access controls, teams who are less familiar with data requirements may unknowingly end up using data in a non-compliant way. Preventing this is key.

To do that, organizations first need to classify and map data to gain a complete understanding of the data that’s under the jurisdiction of the most up-to-date regulations. The team will follow this up by correlating a purpose to the data. This enables the privacy teams for instance to help the organization understand what data-related activities are on or off-limits.

For example, you may be using data for content personalization or product improvements as stated in your privacy policy.

You’d need to have timestamped consent records about the appropriate version of your privacy policy from each user to support this activity. In most cases, you’d need to be able to trace consent across platforms in your tech stack — which can get tricky.

Automation can solve the barriers presented by your tech stack through the classification and mapping of your data across locations, formats, and types.

Classification of Employee Data

Employee data is now in scope with the CPRA (and was already in scope for the GDPR). The data you’re unknowingly collecting from your employees — such as browser history — is now a risk for your organization.

Only 30% of surveyed employees across industries indicated they never used a work device for personal activities. This information requires likely exclusion from your data collection activities, starting with a robust classification effort.

What is Data Classification?

Data classification is an intelligent solution for privacy, security, and governance programs seeking to future-proof their organizations from emerging regulatory change.

You can’t protect your organization from risks that you don’t know exist. The first step always must be to gain a complete picture of your data. Classification is a big piece of that effort.

Organizations need to create systems and workflows that break them free of silos to do their jobs effectively.

They can achieve this by integrating data from different operational sources, uses, and lifecycle stages into a single source of truth. The teams that accomplish this through automation tools are well-equipped to create a strong foundation for their privacy & security programs by classifying and mapping data across the organization.

These efforts are often left incomplete when executed manually.

For example, classifying data by hand often results in significant gaps. Under-classification poses risks for non-compliance because teams may not understand what they’ve missed, resulting in unauthorized uses of data.

How to Automate Data Classification and Mapping

Classifying data will help you effectively protect, store, and manage data from collection to destruction.

Teams seeking to automate this effort must first develop a strategy to collaborate with information security and governance teams. You’ll need to work cohesively to mitigate risk throughout the data lifecycle.

This begins with developing a policy that classifies data types by their level of sensitivity and might include the following classifications:

  • Public data may represent data you can freely disclose to the public: marketing materials, contact information, price lists.
  • Internal-only data may represent data you can’t disclose to the public: battlecards, sales playbooks, organizational charts.
  • Confidential data may represent sensitive data that could negatively impact operations if compromised or becomes part of a data breach: contracts with vendors or employee interviews.
  • Restricted data may represent highly-sensitive corporate data that could put the organization at financial or legal risk if compromised or subject to unauthorized disclosure or access: intellectual property, credit card numbers, social security numbers, protected health information.

Once you classify data according to type, you’re able to take the necessary actions to assess, implement, and refine your privacy program.

Taking the step to classify and map your data reduces the guesswork sometimes attributed to compliance-driven decision-making. This is especially true when you can trade a manual approach for an automated one.

While we’ll always need a human level of review, increasing your investment into automation allows you to save time, money, and resources. This will enable your privacy team to further cultivate their partnerships with business process owners to enhance compliance outcomes companywide.

Register for the webinar: Automating the Classification and Mapping of Sensitive Data​ on February 10 at  4:00 pm GMT

Increase Your Privacy, Security, and Governance Program’s Effectiveness with OneTrust

OneTrust Data Mapping can be the primary building block of a data governance program, especially those focusing on data privacy objectives.

This platform centralizes the information you need regarding locations, processing, and purposes of organizationally-held data.

OneTrust Data Discovery offers machine learning-based automation to find and classify the data, including unstructured data and metadata, contained within each system across your organization. This reduces manual work and provides greater assurance over the accuracy and completeness of your data map.

Partner with OneTrust to increase your privacy program’s effectiveness with:

  • 500+ out-of-the-box integrations that help you categorize and classify data such as sensitive information and personally identifiable information (PII) across all systems.
  • Custom connection builder to help you build additional connections with ease.
  • Intelligence from OneTrust AthenaTM that learns from your data and further trains our classifiers to your unique needs.

Get started with OneTrust Data Discovery and Data Mapping today by requesting a free demo.

Further resources for automating data classification and mapping: 

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