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Data is the lifeblood of organizations. It drives decision-making, fosters innovation, and underpins business operations. However, this wealth of data is scattered across multiple cloud platforms, making it an attractive target for cybercriminals, and rendering traditional approaches to data protection obsolete. This is where data-centric security comes into play. This article will explore the concept of data-centric security, why businesses need it, and the benefits it offers.

Understanding data-centric security

Data-centric security is a comprehensive approach to safeguarding sensitive data by focusing on the data itself rather than the network or perimeter. It revolves around protecting data throughout its lifecycle, ensuring that even if security perimeters are breached, the data remains secure.

Data-centric security comprises several key components and principles, including:

• Data discovery and classification: Identifying and categorizing data based on its sensitivity is the first step in protecting it. By knowing what data is most critical, you can allocate resources and protection measures accordingly.
• Access controls and permissions: Fine-grained access controls and role-based permissions are essential to restrict data access to authorized users and roles, reducing the risk of data exposure.
• Encryption: Encrypting data at rest and in transit adds an extra layer of protection, making data inaccessible to unauthorized individuals.
• Activity monitoring: Real-time activity monitoring and auditing capabilities help detect unusual data access or transfer patterns, allowing for immediate response to potential security incidents.
• Incident response and mitigation: Effective incident response is crucial in case of a breach or unauthorized access, enabling quick identification of the issue and mitigating any damage.

Why businesses need data-centric security

The amount of data being used by organizations for day-to-day operations is increasing rapidly. The importance of adopting a data-centric approach to data protection can be summarized into three main reasons:

1. Traditional security is insufficient.

Businesses leverage multiple cloud environments, and sensitive data, such as personal information or intellectual property, are migrated and sprawled across these platforms, expanding the attack surface. Data vulnerabilities become increasingly common when network perimeters are hard to define in a hybrid work environment. Applying safeguards directly to data is needed to create more barriers that repel unauthorized data distribution. Data-centric security protects data from all kinds of threats, such as external attackers or negligent employees.

2. Apply granular access controls.

Data-centric security is a vital approach to protect your data dynamically. It enables you to have more flexibility in managing your systems and networks by providing fine-grained access controls, which are more effective than traditional access controls. This framework is particularly critical in scenarios where not every user should have access to the entire data within their department.

3. Integrate with existing tech stack.

Data-centric security is an effective way to protect a company's data from cyber threats. It can be added to existing infrastructure without disrupting normal operations or requiring drastic changes. This allows companies to gradually improve their security measures while freeing up resources for other purposes.

Benefits of data-centric security

As data becomes increasingly valuable as a competitive advantage, organizations have increased their cybersecurity spending. Investing in data-centric security controls offers many benefits, which are outlined below.

Lower data protection costs

By focusing on the data itself, data-centric security ultimately reduces the potential of costly data breaches. Many reports highlight that data is the prime target of attackers and that the cost of a data breach increases yearly. It can also lower the cost of maintaining compliance, which often requires updating equipment, systems, and their underlying technology.

A secure remote workforce

Technological changes and the need to satisfy trends for flexible work have accelerated the adoption of a hybrid workforce. However, the proliferation of remote endpoints outside the traditional corporate perimeter drastically increased the security risks posed to data. Adopting a data-centric solution that protects data wherever it goes reduces the risks highlighted by remote work.

Improve security at the file level

Ensuring data security is no longer just about focusing on the infrastructure. With the shift towards data-centric security, there is greater emphasis on securing individual files. This approach enables better tracking, storage, and protection of data. Moreover, file-level security enables the implementation of robust encryption mechanisms, along with strong access controls and policy enforcement. By prioritizing document security, it becomes easier to manage user access to resources and control what they can access and when.

Platform-agnostic data security

Data-centric security helps organizations avoid dependence on any specific system or device. Rather than relying on a particular platform, organizations can build strong cybersecurity practices that provide flexibility in data management, especially with their supply chains. This is crucial because while security infrastructure can strengthen a system, it can also create a situation where security becomes the end goal rather than the means to protect an organization's valuable data. Data-centric solutions also reduce the risks of data silos and minimize harm in case of a systemic failure.

How to create a data-centric security model

Creating a genuine data-centric security model requires adopting a defense-in-depth strategy that encloses data in successive layers of security. Defense-in-depth provides sufficient redundancies that act as barricades of increasing complexity from one layer of security to the next.

Data discovery and classification

To build a comprehensive data-centric model, the first step is to inventory your organization's data across its ecosystem. It is essential to know where your data is located and how it is stored before implementing any security measures. Next, correctly classify this data to understand its value and prioritize the level of protection it deserves. Security administrators can then deploy accurate protection measures for each category of data.

Identity and access management (IAM)

Identity and access management (IAM) plays a critical role in providing robust data-centric security. Robust IAM allows only authorized users to access sensitive data in accordance with the principle of least privilege, providing effective controls to avoid any unauthorized exposure to sensitive information.

Data governance

To be truly effective, data-centric security must adhere to industry-specific and governmental regulations, including national and international mandates, such as GDPR or HIPAA. Data governance is crucial to comply with these regulations, and organizations must periodically conduct risk assessments to ensure they are maintaining compliance.

Data loss prevention (DLP)

One of the best ways to approach data-centric security is to incorporate a data loss prevention solution that meets their business needs for data protection. DLP excels in preventing data from entering into the wrong hands or being exposed to unauthorized access by detecting and preventing data compromise from breaches, leakages, and exfiltration.

Conclusion

Data-centric security is a vital approach to protecting sensitive data in an increasingly interconnected world. It not only safeguards your critical assets but also helps with compliance, intellectual property protection, and reducing the impact of data breaches. By following the guidelines for selecting data-centric security tools and solutions, organizations can fortify their data defenses and maintain a strong competitive edge in today's digital landscape.

Invest in data-centric security measures to secure your organization's most valuable asset: its data.