Types of Data Protection Explained: Encryption, Masking, Redaction, Deletion, and Moving

Beth Osborne

March 18, 2026

Types of Data Protection Explained: Encryption, Masking, Redaction, Deletion, and Moving

Data drives modern businesses. While it delivers immense value, it also presents risk, and organizations must safeguard sensitive and regulated data. To meet compliance mandates while preserving data utility, organizations rely on multiple types of data protection techniques, including encrypting, masking, redacting, deleting, or moving data.

Each aligns with different risk and operational requirements. In large, complex environments, challenges arise when centralized policies don’t govern these actions. As a result, inconsistent remediation and security gaps are common.

Let's explore various remediation options, when to use each approach, and what to look for in a data protection solution so you can enforce security uniformly and at scale.

What Is Data Protection?

Data protection describes practices organizations use to safeguard sensitive information. It encompasses data security, access, and availability.

Many businesses have a defined data protection program, built around:

Compliance Regulations

PCI DSS, HIPAA, GDPR, CCPA, GLBA, and FISMA

Cybersecurity Best Practices

NIST, IAM, MFA, and Perimeter Defenses

Data Retention Policies

How Long? What Data? Move or Delete?

Safe Data Use Controls

For Use in Analytics, Development, and Testing Environments

Data protection evolves constantly. Regulatory changes, emerging threats, and the rapid advancement of AI all introduce new risks and requirements. As organizations adapt, they need a comprehensive platform capable of managing data protection across all environments and data types.

Types of Data Protection

Depending on data type, regulatory requirements, and data use, some protection methods are more effective than others. Below is an in-depth guide on each type, explaining how it works and how it supports compliance.

Encryption: Protect at Rest and in Transit Without Disruption

Modern data encryption streamlines and automates protection. Certificate-free and policy-based encryption enables seamless access for authorized users and applications. You don't have to choose between compliance and usability. Both are possible with the right platform.

How It Works

Effective encryption should move with your data, delivering persistent protection across desktops, servers, and file shares. It eliminates coverage gaps by supporting a broad range of platforms, including Windows, MacOS, Linux, Unix, and IBM i.

Truly seamless encryption doesn’t disrupt users. It allows applications and workflows to interact with encrypted files through embedded encryption. By applying encryption at the application level, organizations can ensure sensitive data is never written to disk in unencrypted form, significantly reducing exposure risk.

When to Use It

Encryption is a foundational best practice for data security and compliance mandates. Beyond protecting sensitive information, encryption plays a critical role in audits and internal governance. Encryption audit logs provide verifiable proof of compliance, eliminating time-consuming manual evidence collection. By delivering persistent protection throughout the data lifecycle, encryption mitigates risk and secures data before a breach occurs.

Regulatory Support

Encryption allows you to meet regulatory rules associated with HIPAA, GLBA, PCI DSS, and FISMA. Such regulations mandate encryption of data in all locations, including storage, transmission, and external environments.

Use Case Spotlight: Compliance at Scale

FISERV, a financial services company, uses consistent, automated at-rest and in-transit encryption. They secure sensitive data on 50,000 desktops and 1,200 servers. They encrypt over 1 million files daily. Encryption helps them meet compliance requirements and prevent disruption to users, applications, and workflows.

Masking: Minimize Exposure Risk While Preserving Utility

Data masking allows you to create a sanitized version of sensitive data to avoid unintended or unauthorized access. Balancing data value and risk is a common struggle, but masking prevents exposure while maintaining data utility.

How It Works

Masking replaces sensitive alphanumeric characters with random characters in the same format. This process renders the data non-sensitive. The data is still usable for critical business functions, without exposure risk.

A robust masking solution should consistently secure sensitive data across the enterprise, spanning platforms such as Oracle, SQL Server, Postgres, Hadoop, DB2, Azure, AWS, Snowflake, Salesforce, and more.

Centralized masking policies should combine continuous discovery with automated remediation. This approach identifies newly created or relocated data and masks it immediately, preventing exposure risk.

When to Use It

Since you maintain referential integrity with masking, you can use the data for safe development, testing, and analytics. Masking is also the best choice to obfuscate sensitive data before it enters AI models to mitigate the risk of leaking IP or proprietary data.

Regulatory Support

Masking ensures compliance with PCI, HIPAA, and GDPR.

Use Case Spotlight: Lower-Level Environments

A global automotive services company, Holman, uses data masking to protect Oracle databases. By masking in lower-level environments, developers can securely work with full data sets.

Read the Case Study

Redaction: Reduce Data Exposure and Compliance Risk

Data redaction keeps sensitive data secure yet usable by the organization. It empowers teams to collaborate and share data internally and externally without the risk of exposing PII or confidential information. It also renders data of no value to a hacker should a data breach occur.

How It Works

Data redaction removes sensitive elements from data. Examples include Social Security numbers, credit cards, or other PII. However, it retains the structure to preserve context for operational use.

When to Use It

Data sharing and analysis are the central use cases for redaction. Since the context remains, various groups can still leverage data but without exposing sensitive information. Marketing and analysts can safely derive insights. Healthcare providers can securely share treatment outcomes.

Regulatory Support

Redaction can satisfy mandates like GDPR, CCPA, HIPAA, and PCI.

Use Case Spotlight: Safeguarding PCI Data

JPMorgan Chase safeguards PCI data by redacting critical credit card information within the Payment Tech and Merchant Services organization. With this capability, the company eliminates sensitive data mishandling, exposure, or loss and can pass internal and external PCI audits.

Read the Case Study

Deletion: Optimize Your Retention Strategy

Deletion is another essential form of data protection. By discovering and removing outdated data, organizations reduce exposure and minimize risk. Policy-driven deletion identifies sensitive information that qualifies as redundant, obsolete, or trivial (ROT). It then removes it before it becomes a liability.

How It Works

Deletion policies should work in tandem with automated discovery. The discovery process surfaces ROT data in every location, from file shares and endpoints to cloud or on-prem databases.

It will then automatically delete any data that has exceeded retention requirements. If preferred, organizations can quarantine data before deletion for final review. This capability ensures there's no valuable data loss.

When to Use It

Automated discovery and deletion of data you no longer need to keep applies across all environments. These features enable enterprise-wide visibility and action.

Regulatory Support

Many regulations require organizations to retain certain records for defined periods. You should dispose of data once it is no longer required.

Use Case Spotlight: Aging Out Data Deleted

A Fortune 500 U.S. insurance company uses discovery and deletion to reduce their risk footprint. Once regulations no longer require them to preserve data, they review and notify end users of the deadline to act. After the deadline, the policy deletes the obsolete data.

Moving: Ensure Protected Information Is Secure

The final type of data protection is the relocation of sensitive elements. Organizations may generate data in one environment but need to move it to meet compliance and privacy standards.

How It Works

Policy-driven discovery identifies the location of all protected data categories. After discovery, automated policies can move data to a more secure location.

When to Use It

One common reason for moving data is for longer-term storage with stronger security controls, such as encryption. In some cases, the data's original repository requires transfer to an encrypted location. Data retention policies can also drive data movement. Once you've met compliance requirements for storing data, you can select data and move it to an archive.

Regulatory Support

Moving data to an encrypted folder can fulfill the requirements of HIPAA, GLBA, PCI DSS, and FISMA.

Use Case Spotlight: Protecting Credit Card Numbers

Any organization that receives and stores credit card data must abide by strict regulations. While PCI may enter the business in one system, the organization may need to move it to a secure, encrypted environment. Centralized discovery and remediation policies make that movement simple and consistent.

Types of Data Protection: PK Protect Has You Covered

PK Protect delivers a data-centric security platform that combines discovery and remediation. You can select the protection option that fits your requirements and customize prebuilt policies as needed.

Secure information and ensure compliance without impacting productivity. Uncovering, classifying, and protecting your data more effectively starts with PK Protect. Request a custom demo of the platform today.

Let's explore various remediation options, when to use each approach, and what to look for in a data protection solution so you can enforce security uniformly and at scale.