What Is Data-Centric Security?

5 min. read

Table of Contents

Why a Data-Centric Security Strategy Matters
When a Data Focus for Security Is Necessary
Data-Centric Security FAQs

Data-centric security prioritizes the protection of data itself, wherever it is stored, accessed, or used. This unique approach involves identifying sensitive data, classifying it based on its data type, and implementing appropriate security controls and policies to protect it.

The focus is on protecting data throughout its lifecycle rather than just securing the perimeter of a network or device. This approach includes various techniques and technologies, such as encryption, access management controls, data masking, and data loss prevention tools. It can be applied to on-premise, cloud-based, and hybrid IT environments.

A data-centric security framework is based on identifying, understanding, controlling, protecting, and auditing data. These aspects are necessary to secure critical data, defend against data loss, and identify changes that indicate malicious intent.

Data breaches continue to rise, and IT environments have become more complex. It is critical to adopt a data-centric security architecture to remove blind spots and comply with relevant data privacy laws. By prioritizing data protection, organizations can reduce the risk of data breaches and cyber-attacks. This, in turn, improves their ability to comply with data protection regulations.

Why a Data-Centric Security Strategy Matters

Data is the core of the business, driving decisions and defining processes and procedures, making it crucial to protect this data. A data-centric approach to security makes data the focal point for security practices. It prioritizes data protection over networks, servers, and applications. This approach ensures that data remains secure, even if other areas of the network or device are compromised.

Organizations can create a comprehensive security design that protects sensitive information by implementing intentional and high-impact security decisions. This approach doen’t overlook the security of other areas but applies security measures to them to improve data protection.

Insufficient Network Server and Application Security

Even the most secure networks are vulnerable to internal risks. Data-centric security protects against unauthorized access and data spillage by focusing on the defense of the data.

Need for Access Limitations

Data-centric security leverages fine-grained access controls. These controls ensure that users can only access sensitive resources they need to complete their tasks. Anything beyond what is necessary is prevented. This is particularly important as not all users should have access to every ounce of data in the organization.

Seamless Integration

Data-centric security can be added to existing systems without significant disruption, freeing up resources for other purposes.

Need to Protect Data at its Core

With data-centric security, data is considered the most critical asset. Security measures are implemented to protect it wherever it is stored, transmitted, or used. This ensures that the data remains secure even if the network or device is compromised.

Compliance Mandates

Data-centric security helps organizations comply with data privacy regulations by implementing appropriate security controls and policies to protect sensitive data.

Mitigating Evolving Attacks

Cyberattacks have become more sophisticated and targeted, and attackers now focus on stealing sensitive data. By implementing advanced security controls, data-centric security helps organizations protect against these types of attacks.

Defending Company Reputation

Data breaches can significantly impact an organization’s reputation and brand value. By adopting data-centric security, organizations can reduce the risk of data breaches and protect their reputation by demonstrating their commitment to protecting sensitive data.

When a Data Focus for Security Is Necessary

With the increasing amount of sensitive data used for day-to-day operations, data security has become essential to an organization’s operations. As data breaches become more sophisticated and targeted, organizations must adopt a data-centric security approach to protect sensitive data wherever it is stored, transmitted, or used.

Protecting Sensitive Data

Virtually all organizations collect sensitive data. This may include customer information, financial data, and intellectual property that must be protected. Data-centric security helps protect this data by implementing security controls such as encryption, access controls, and data loss prevention tools.

Compliance with Data Privacy Regulations

At its core, data-centric security helps organizations comply with data privacy regulations and data governance. Implementing appropriate security controls and policies to protect sensitive data helps in meeting requirements like GDPR, CCPA, and HIPAA.

Cloud Security

As more organizations adopt cloud computing, data-centric security becomes critical to protect data stored and processed in the cloud. This includes implementing encryption, access controls, and monitoring solutions to protect data in the cloud.

Insider Threats

Insider threats can pose a significant risk to an organization’s data security. Data-centric security helps mitigate this risk by implementing access controls, monitoring solutions, and data loss prevention (DLP) tools to prevent unauthorized access and data exfiltration.

Secure Data Sharing

Data-centric security helps organizations share data securely by implementing access controls, encryption, and monitoring solutions to ensure that authorized users only access and are protected during transmission.

Data-Centric Security FAQs

A data inventory is a comprehensive list of all the data assets that an organization has and where they're located. It helps organizations understand and track:

Types of data they collect, store, and process
Sources, purposes, and recipients of that data

Data inventories can be managed manually or automatically. The reasons for maintaining a data inventory vary — and could include data governance, data management, data protection, data security, and data compliance.

For example, having a data inventory can help organizations identify and classify sensitive data, assess the risks associated with different types of data, and implement appropriate controls to protect that data. It can also help organizations understand which data they have available to support business objectives, or to generate specific types of analytics reports.

Data mapping is the process of creating visual representations of the relationships and flows of data within an organization's systems and processes. It helps organizations understand how data is collected, stored, processed, and shared across different systems, applications, and third parties. Data mapping is essential for complying with data protection regulations, as it enables organizations to identify potential risks, maintain data accuracy, and respond effectively to data subject rights requests. By creating a data map, organizations can optimize data management processes, implement robust security measures, and enhance data governance.

Privacy policies are legally binding documents that outline how an organization collects, processes, stores, shares, and protects personal data. These policies inform users about the types of data collected, the purpose of data collection, data retention periods, and the rights of data subjects. Privacy policies also detail the organization's compliance with data protection laws and regulations, such as GDPR, CCPA, and HIPAA. By providing transparency and establishing user trust, privacy policies play a critical role in ensuring responsible data management practices and legal compliance.

Access control models are frameworks that define how permissions are granted and managed within a system, determining who can access specific resources. They guide the development and implementation of access control policies. Common models include:

Discretionary access control (DAC), where resource owners decide who can access their resources.
Mandatory access control (MAC), where a central authority regulates access rights based on clearances and classifications.
Role-based access control (RBAC), where permissions are granted according to roles within an organization.
Attribute-based access control (ABAC), where access is granted based on a combination of user attributes, resource attributes, and environmental factors.

Data sprawl refers to the growing volumes of data produced by organizations, and the difficulties this creates in effectively managing and monitoring this data. As companies collect more data — both internally and through the broader range of enterprise software tools in use today — and increase the amount of storage systems and data formats, it can become difficult to understand which data is stored where. This can lead to increased cloud costs, inefficient data operations, and data security risks as the organization loses track of where sensitive data is stored — and fails to apply adequate security measures as a result.

To mitigate the impact of data sprawl, automated data discovery and classification solutions can be used to scan repositories and classify sensitive data. Establishing policies to deal with data access permissions can also be beneficial. Data loss prevention (DLP) tools can detect and block sensitive data leaving the organizational perimeter, while DDR tools offer similar functionality in public cloud deployments.