In this digital era, data has become the most important currency around which e-commerce and business revolve. Data-centric security highlights this by providing greater emphasis on the data itself rather than the technologies, and infrastructure, surrounding it.
What Is Data-Centric Security?
Data-centric security revolves around the actual data, focusing on core attributes like its lifecycle and dependability rather than the risks associated with inadequate security infrastructure protecting it. As a result, it involves protecting data wherever it is, whether at rest, in motion, or in use.
This makes sense since most of the data an organization generates rarely stays within the confines of its corporate network. Instead, it is shared with third parties, advertisers, and other outside collaborators.
Data-centric security represents a paradigm shift from the traditional security route organizations follow to protect data, which mainly consists of beefing up their digital infrastructure.
While technology is still involved in data-centric security, its solutions are more geared towards providing layers of governance, policies, and best practices to protect data.
This focus on data extends to how it is stored, where it is located, and how it is accessed.
What Are The Advantages of Data-Centric Security?
As data becomes increasingly valuable as a competitive advantage, organizations have increased spending on their cybersecurity apparatus. Yet, this hasn’t truly mitigated cyber attacks, hacking, and other security breaches from occurring.
Lowering the Compliance Cost of Data
By focusing on the data itself, data-centric security ultimately reduces the incidence of data security breaches. It also lowers the cost of maintaining compliance, often requiring constantly updating equipment, systems, and their underlying technology.
Improved Handling of a Remote Workforce
Technological changes and the Covid pandemic accelerated the adoption of a remote workforce. However, the proliferation of remote endpoints outside corporate infrastructure and networks drastically increased security risks posed to data.
Adopting a data-centric solution that protects data wherever it goes reduces the risks highlighted by remote work.
Guaranteeing File-Level Security
Data-centric security involves more than a pivot from the traditional infrastructure-focused approach. It applies more granularity to data security by leaning more heavily on file-level security.
This, in turn, makes it easier to track, store, and safeguard your data. In addition, file-level security facilitates the implementation of robust encryption mechanisms, along with strong access controls and policy enforcement.
Without this document security, you can more reliably control what and when users can access resources.
Creating Data Security Independent of Device or System
Data-centric security relieves organizations of the burden of being beholden to any system or device. By building strong cybersecurity regardless of platform, they have more leeway for data management, especially with their supply chains.
This is vital because while security infrastructure can fortify a system, it often results in presenting or providing security as an end in itself instead of the means to an end – which is protecting an organization’s data crown jewels.
Moreover, data security independent of a system mitigates the risk or possibility of an attack on the organization’s data. Data-centric solutions also reduce the incidences of data silos and harm when a systemic failure occurs.
How to Create a Data-Centric Security Model
Creating a genuine data-centric security model brings security down to the data level.
Defense-in-depth
Defense-in-depth is the most salient feature of a data-centric security model. It entails adopting a military strategy that encloses data in successive layers of security. These concentric rings of security may start with the desktop as the outer layer, then move to network access and operating system controls before presenting authentication.
Defense-in-depth provides sufficient redundancies that act as barricades of increasing complexity from one layer of security to the next.
Data Discovery, Identification, and Classification
The first step in building a meaningful data-centric model is auditing and taking inventory of your organization’s data across its intranet, databases, cloud systems, and various platforms.
Before an organization can keep its data secure, it needs to know where it is located and how it is stored. The next step is properly classifying and labeling the data because you cannot accurately deploy protection until you know the value of the data you are dealing with.
Once data classification has been achieved, possibly with the means of automation, infosec teams can prioritize the level of protection each category of data deserves.
For instance, intellectual property information like patents and company secrets might need to be protected differently from, say, credit card details.
Identity and Access Management (IAM)
Identity and access management is a critical part of data-centric security. IAM ensures that only authorized users can access an organization’s data.
Coupled with the principle of least privilege (PoLP), it provides the necessary controls so that users are exposed to only the data required to perform their duties.
Governance and Compliance
To be truly effective, data-centric security must adhere to industry-specific and governmental regulations, including federal and international mandates. One of the most all-encompassing is the General Data Protection Regulation (GDPR) of the European Union (EU).
If your organization operates in the healthcare industry, then HIPAA laws cover the storage, handling, and overall confidentiality of patient information.
As a result of data regulations, organizations adopting data-centric solutions must periodically conduct risk management audits to ensure they are maintaining compliance with data governance rules.
Data Loss Prevention (DLP)
One of the best ways to approach data-centric security is to incorporate a data loss prevention solution. DLP excels in preventing data from entering into the wrong hands or being exposed to unauthorized access.
It detects and prevents data loss from data breaches, data leakages, and data exfiltration. DLP uses encryption and data masking to obfuscate and protect the data from unauthorized access and illegal tampering.
How can Fortra Secure Collaboration help you with Data-Centric Security?
Fortra Secure Collaboration possesses the correct tools to aid organizations in their data-centric security journey. With secure file collaboration technology, like digital rights management (DRM) and information rights management (IRM), Fortra Secure Collaboration can help complement your DLP solution and extend your data protection strategy across your enterprise.
Fortra Secure Collaboration solutions provide data security that travels with your digital crown jewels wherever they go. Moreover, our solution works independently of the platforms, applications, and databases you use.
To learn more about data loss prevention and how we integrate with DLP solutions, like Fortra DLP, here.
