What Is Data Protection?

Why Is Data Protection Important?

As businesses increasingly rely on digital ecosystems, the role of IT has expanded beyond local cybersecurity enforcement to global data governance and protection. Strong data protection practices are essential for ensuring that data—whether stored on-premises or in hybrid environments—remains secure and compliant with evolving industry standards and government regulations. By implementing a robust data protection strategy, organizations can maintain data integrity and prevent accidental or malicious exposure. 

IT leaders today are turning to comprehensive data protection platforms that unify security efforts across various environments, including on-premises data centers, public and private networks, and software applications. This holistic approach is necessary to prevent data breaches, manage compliance, and reduce the complexity of securing increasingly distributed data.

Data Protection Technologies

In today’s threat landscape, safeguarding sensitive information requires a multi-layered approach. Below are five key data protection technologies that help organizations secure their data, reduce risk, and comply with regulatory requirements.

Encryption: Encryption converts readable data into an unreadable format using cryptographic algorithms. Only authorized users with the correct decryption key can access the information. This technology ensures that even if data is intercepted or stolen, it remains inaccessible to unauthorized parties.

Authorization and authentication: These two technologies control who can access data and verify that users are who they claim to be. Authentication involves validating a user’s identity, typically through credentials like passwords, multi-factor authentication (MFA), or biometrics. Authorization, on the other hand, determines the level of access a user has to specific resources.

Data masking: Data masking involves obfuscating sensitive information by replacing it with a non-sensitive equivalent, such as substituting real names with random characters. This allows organizations to use or analyze data without exposing the actual sensitive content to unauthorized personnel. Data masking is particularly important during development, testing, or analytics processes where data exposure should be minimized.

Data backup: Regular data backups ensure that, in the event of a data breach, ransomware attack, or accidental deletion, critical information can be restored. Effective backup strategies involve storing copies of data in secure, offsite locations, and utilizing encryption to protect backups. In Zero Trust environments, access to backups should be tightly controlled to prevent unauthorized modifications or deletions.

Data loss prevention (DLP): DLP solutions monitor and control the flow of sensitive data within and outside an organization. They help prevent the accidental or malicious leakage of data by enforcing security policies across applications, networks, and endpoints. DLP is an essential technology for maintaining data visibility and ensuring that sensitive information does not leave the organization without proper authorization. 

These technologies, when integrated into a zero trust framework, provide a robust defense against both external and internal threats, ensuring that data remains secure regardless of where it resides.

Data Protection Regulations and Standards

Data protection regulations and standards have been established globally to safeguard sensitive information and ensure that organizations implement robust security measures. Understanding and complying with these frameworks is critical for businesses to avoid legal penalties and maintain customer trust. Below are some key regulations and standards that govern data protection practices: 

GDPR (General Data Protection Regulation): Enforced within the European Union, GDPR mandates that organizations must protect the personal data and privacy of EU citizens. This regulation also grants individuals rights over their data, including the right to access, correct, and request the deletion of their information.

HIPAA (Health Insurance Portability and Accountability Act): In the United States, HIPAA governs the protection of sensitive health information (PHI). Healthcare organizations and their partners must implement administrative, physical, and technical safeguards to secure patient data.

CCPA (California Consumer Privacy Act): The CCPA gives California residents greater control over their personal data, requiring businesses to disclose the data they collect, provide opt-out options, and comply with deletion requests.

PCI-DSS (Payment Card Industry Data Security Standard): PCI-DSS is a security standard designed to protect payment card information. Organizations that process, store, or transmit cardholder data must comply with its strict requirements, which include encryption, network security deployment, and regular monitoring of network access.

Adapting to these regulations and standards not only ensures compliance but also strengthens the overall security posture of an organization, particularly when zero trust principles are applied to protect sensitive data across the board.

Data Protection Challenges

While the benefits of data protection are clear, effectively safeguarding data presents several challenges:

Protection gaps: Tools such as DLP, secure web gateways, and security monitoring platforms often address specific aspects of data protection. However, gaps between products and teams can lead to redundant processes, a lack of visibility, and insufficient control over data exposure across different environments.

Limited visibility and control: Many data protection tools offer limited context to help organizations make informed decisions. A lack of clarity around who is accessing data, where, and how it’s being used can make it difficult to enforce granular control and ensure data is adequately protected.

User experience: Legacy security architectures that divert traffic through centralized appliances can negatively impact performance, leading to user frustration. Additionally, scaling these systems to accommodate increases in the number of users as a business grows will cause headaches for both IT and security.

How Organizations Can Protect Data

The ideal data protection solution should be designed for both performance and scalability, ensuring that users and systems can securely access data without hindering productivity. In today’s distributed work environments, this means deploying solutions that can handle data protection across on-premises systems, private networks, and third-party applications. 

A zero trust framework is increasingly seen as a best practice for data protection. By applying zero trust principles, organizations can secure data access based on context—such as user identity, device status, and application behavior—before allowing any connection. This approach mitigates risks by ensuring that only trusted entities can access sensitive information.

Benefits of Data Protection

A comprehensive data protection program provides several key benefits:

Improved security for data and applications: By gaining visibility across your entire data architecture, your organization can strengthen its defenses, regardless of whether the data resides on local servers, external networks, or distributed systems.

Enhanced access governance: Implementing data protection best practices ensures that access to sensitive data is governed by organizational policies. Only authorized users, devices, and systems can interact with protected data, reducing the risk of unauthorized access.

Regulatory compliance: As data governance becomes stricter under regulations such as GDPR, HIPAA, and CCPA, a solid data protection program helps businesses stay compliant with these laws, avoiding significant fines and maintaining customer trust.

Data Protection Trends

As the landscape of data security continues to evolve, several key trends are shaping how organizations protect their sensitive information. Below are some of the most impactful trends in data protection today:

AI and Machine Learning in Data Security 

Artificial intelligence (AI) and machine learning (ML) technologies are becoming indispensable in the fight against data breaches and other cyberthreats. These tools can analyze vast amounts of data to detect anomalies, identify vulnerabilities, and predict potential security incidents before they occur. By automating threat detection and response, AI and ML help organizations stay ahead of increasingly sophisticated cyberattacks.

Zero Trust Security

The zero trust model is increasingly being adopted as a foundational framework for modern data protection. Instead of assuming that internal systems and users are inherently trustworthy, zero trust continuously verifies identities and enforces strict access controls at every level of the network. This approach significantly reduces the risk of insider threats and lateral movement by bad actors, ensuring that sensitive data is only accessible by authorized users under the right conditions.

Data Localization Laws

More countries are enacting data localization laws, which require that data be stored and processed within a specific geographic region. These laws are often driven by concerns over national security and data sovereignty. For businesses, this trend introduces new complexities around data storage, as they must navigate a patchwork of legal requirements across different jurisdictions. Implementing flexible, compliant data protection solutions that can adapt to these localization demands is becoming a critical focus for global organizations. 

These trends highlight the ever-changing nature of data protection and underscore the need for proactive, adaptable security strategies to safeguard sensitive information in today’s complex regulatory and threat landscape.

Data Protection vs. Data Security

While the terms data protection and data security are often used interchangeably, they have distinct focuses within the broader landscape of cybersecurity. Understanding the difference helps organizations design a comprehensive strategy for safeguarding sensitive information. Here's a quick breakdown of how they differ:

Why Organizations Need Both 

To create a robust security posture, organizations need both data protection and data security working in tandem. Data protection ensures that information is properly managed, backed up, and compliant with privacy regulations, while data security defends against external and internal threats. 

Without data security, protected information may be vulnerable to attacks, and without data protection, even the most secure systems can fail to meet legal and operational standards.

Together, they provide a holistic approach to safeguarding sensitive data in a zero trust environment, where both privacy and security are paramount.

Data Protection Best Practices

Building an effective data protection program requires thoughtful planning and execution. Organizations should consider the following best practices:

Take inventory of sensitive data: Begin by identifying and cataloging all sensitive data within your organization, including where it is stored and how it flows across systems. This allows you to implement appropriate protections for the most critical assets.

Pair encryption with authentication: While authentication mechanisms are essential for controlling access to data, encryption provides an additional layer of security. By encrypting sensitive data, organizations can protect it even if it falls into the wrong hands.

Choose a trusted provider: Selecting the right data protection provider is crucial. Look for a vendor that offers comprehensive, scalable solutions that meet your organization’s unique data protection needs, whether on-premises, in the cloud, or in hybrid environments.

Zscaler and Data Protection

Through our Zero Trust Exchange™, Zscaler offers a comprehensive platform that helps organizations safeguard their sensitive data across diverse environments. The platform provides the following capabilities: 

Preventing data loss: Zscaler’s platform inspects both internet and encrypted traffic, ensuring that sensitive data remains secure during transfers, regardless of where it originates or where it’s headed.

Securing SaaS and on-premises data: Zscaler’s integrated data protection solutions allow organizations to secure both SaaS applications and on-premises data, ensuring that sensitive information is protected across the entire ecosystem.

Addressing configuration risks: Zscaler’s solutions also help detect and remediate misconfigurations, compliance violations, and other risks that could lead to data exposure, providing continuous scanning and prioritization of potential threats.

Supporting unmanaged devices: With remote work and BYOD (bring your own device) becoming more prevalent, Zscaler ensures that organizations can secure data access from unmanaged devices without the performance limitations of traditional solutions. 

Zscaler’s data protection solutions provide a comprehensive, scalable platform designed to safeguard sensitive data across all environments. By leveraging the Zscaler Zero Trust Exchange, organizations can enhance their security posture, protect against data loss, and maintain compliance with stringent regulatory standards, all while ensuring optimal performance for their users.