Taking a Data-Centric Approach to Zero Trust to Protect Critical Assets

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Taking a Data-Centric Approach to Zero Trust to Protect Critical Assets

Working remotely on a massive scale is bringing new security challenges to organizations. They are witnessing their attack surface grow in today’s increasingly hostile threat landscape.

CISOs now need to rethink the fundamentals of perimeter-centric and trust-based security and how it protects enterprise-critical data, applications, users, and devices.

2020 has seen a significant rise in data breaches, and, with that, an increase in global regulations that can cost businesses millions in losses and fines. In response, companies have started implementing frameworks like zero trust that can help diminish these potential risks and secure their confidential and sensitive data.

Security models of yesteryear focused on the IT perimeter, but with the move to hybrid multi cloud environments, the increase in BYOD (bring your own device) models and the co-mingling of contractors and employees, the perimeter is no longer adequate. Companies that use zero trust strategies will be able to secure devices that need to be managed and implement analytics and response mechanisms so that security analysts have full visibility into their environments.

Read More: Performing Vulnerability Management the Right Way

The Data-Centric Approach

If an enterprise wants to gain full control of its sensitive data and derive maximum value from its investment in zero trust architecture, it needs to adopt data-centric security technology.

The zero trust models provides a clear foundation for redesigning networks so that unauthorized parties are unable to move around freely once they enter the system. By segmenting networks into smaller perimeters, enterprises can limit the amount of sensitive data available to the intruders with robust identity validation technology, and control on access to network resources.

Developing a Data-Centric Security Strategy

Creating a robust framework means creating an inventory of the organization’s sensitive data and where it resides. To protect the data, an organization should adopt strong encryption to secure the environment.

Furthermore, to fully understand the data landscape, there needs to be activity monitoring to keep an eye on who is trying to access all of that data. An organization must have a clear view of users and behavior as it relates to their most sensitive data.

Read More: Protecting wireless protocols from data breach

Without a data-centric approach security framework in place, companies will find it difficult to keep data-centric approach segmented properly within a zero trust environment. Files will be saved in the wrong location, inevitably be mislabeled, or left without appropriate protection, thereby reducing the effectiveness of a company’s network segmentation and granular access controls. Automated data-centric approach security is an effective method of ensuring that files are protected and classified according to company policy.

Data Protection

Machine learning and robust analytics allow for deep visibility into the data environment and filter out the noise from false alarms. These analytics feed an automation engine, and if any inconsistency is detected, infected users are blocked from accessing sensitive data.

Knowing where the data is and applying Identity and Access Management (IAM) tools, allows the enterprise to understand who can access that data and determine if they should have access in the first place.

Furthermore, layering in unified endpoint management (UEM) solution gives businesses full visibility and context into the data, the user accessing the data, and the device they’re using to create an end-to-end secure framework.

No matter what rules businesses have in place, with a changing business environment, there will always be incidents that violate those policies. It’s critical that companies quickly respond and take precise action to remedy the problem. In the enterprise world, it could mean wiping a user device or adjusting the segmentation of the network.