Zero Trust for data—classifying, protecting, and governing your data

Man in a hooded sweater/sweatshirt inside a secure room, pointing at a geographic area displayed on a large monitor.

Bad actors want your data. While a lot of things can happen before a user or system get to access an organization’s data, data is still ultimately what criminals want; personal information, customer lists, credit card numbers, trade secrets – to name a few.

It is no wonder then, that protecting data is one of the primary responsibilities of organizations security and compliant teams. As organizations increase their digital transformation, there is an increasing adoption of Zero Trust principals for data.  Zero Trust principals; Verify Explicitly, Use Least Privileged Access and Assume Breach.

The core elements for a data protection strategy;

Know your data: Understand your data landscape and identify important information across your cloud and on-premises environment including, how your data gets used, what is critical, and what is sensitive.

Protect your data: Protect your sensitive data throughout its lifecycle by applying sensitivity labels linked to protection actions like encryption, access restrictions, visual markings, and more.

Prevent data loss: Apply a consistent set of data loss prevention policies across the cloud, on-premises environments, and endpoints to monitor, prevent, and remediate risky activities with sensitive data.

Monitor and remediate: Continuously monitor sensitive data to detect policy violations and risky user behavior. This allows you to take appropriate action, such as revoking access, blocking users, and refining your protection policies.

 As organizations begin their Zero Trust journey for Data the strategy needs to encompass your organizations entire digital state. 

  1. Access decisions are governed by encryption – protect your most sensitive data with encryption to restrict access to content that sensitivity labels are applied
  2. Data is automatically classified and labeled – to avoid issues with data not being labeled manually
  3. Classification is augmented by smart machine learning models – with a vast amount of data reduce challenges to adequately label and classify
  4. Access decisions are governed by a cloud security policy engine
  5. Prevent data leakage through DLP policies based on sensitivity label and content inspection – such as sensitive information such as financial data, and credit card numbers

Get started with your Zero Trust Journey for Data

  1. Watch our Zero Trust – Data webinar to explore data-centric security technologies like classification, labelling, encryption, and data loss prevention
  2. Evaluate your current Zero Trust maturity stage to determine where your organization is and how to move to the next stage

Access the Zero Trust Deployment Guide for Data for detailed deployment guidance.