Introduction to Card Data Protection: Why It Matters in 2025

[Student]

In the digital age, credit card data breaches remain one of the most serious threats to businesses and consumers. As of 2025, the number of data breaches continues to grow, with thousands of incidents affecting millions of users. For example, recent breaches include attacks on large companies, where attackers steal financial data, leading to financial losses and loss of trust. To prevent such incidents, security methods such as data masking and database-level encryption are used. These approaches not only minimize risks but also help comply with standards such as PCI DSS 4.0, which, as of March 2025, requires mandatory encryption of card data at rest and in transit. In this educational overview, we will examine these methods in detail: from basic concepts to techniques, advantages, disadvantages, examples, and trends for 2025. We will also touch on tools and best practices so you can understand how to apply them in practice.

1. Data Masking: Concept and Types​

Data masking is a cybersecurity technique that replaces sensitive information with fictitious, yet realistic, data. This allows the data to be used in non-production environments (for example, for testing or analytics) without risking the leakage of real card numbers. Unlike encryption, masked data cannot be recovered without additional mechanisms, rendering it useless to attackers.

Types of camouflage​

Based on 2025 sources, several types of camouflage are distinguished, each suitable for different scenarios:

• Static Data Masking: Creates a copy of the database where sensitive data is permanently replaced. Ideal for development and testing. Advantage: Complete isolation from the original data. Disadvantage: Requires additional storage.
• Dynamic Data Masking: Masking occurs in real time when the database is requested, depending on user permissions. For example, an analyst sees only the last four digits of the card (****-****-****-3456), while an administrator sees the full number. Advantage: Doesn't change the original database. Disadvantage: May slow down queries.
• Deterministic Data Masking: Replaces values with consistent values (e.g., all "John Doe"s are replaced with "Jane Smith"). Useful for preserving relationships in data.
• On-the-Fly Masking: Used when transferring data between systems without storing a masked version.
• Tokenization: Although this is a separate method, it is often referred to as masking - the real card number is replaced with a token (random identifier).

Camouflage techniques​

Techniques allow masking to be tailored to specific data, such as card numbers:

• Substitution: Replacing real data with fictitious data. Example: A card number is replaced with a random number that is valid according to the Luhn algorithm.
• Data Redaction: Hiding parts of data, such as masking all but the last four digits.
• Shuffling: Rearranging the values in a column while maintaining the structure.
• Format-Preserving Masking: The data changes, but the format remains (the 16-digit card number remains 16-digit).
• Randomization: Replacement with random values.
• Null values (Nulling): Replace with NULL for non-critical fields.

Advantages and disadvantages​

Advantages: Reduces the risk of data breaches, ensures compliance (GDPR, PCI DSS), preserves data usefulness for testing, and minimizes internal threats. Disadvantages: May disrupt application logic if masking is not realistic; requires careful planning.

Camouflage tools in 2025​

AI-enabled tools for PII (Personally Identifiable Information) detection are popular in the 2025 market:

• PFLB: Flexible big data tool, supports structured/unstructured databases.
• K2View: With AI for PII detection, dynamic and static masking.
• Oracle Data Masking: Integrates with Oracle DB, creates data subsets.
• IBM InfoSphere Optim: For enterprise, supports multiple technologies.
• Delphix: Automates data masking for DevOps. These tools prevent leaks by replacing data in non-production environments and ensuring compliance.

Python Masking Example (for Educational Purposes)​

Here is a simple code to mask the card number:

def mask_credit_card(card_number):
# Mask all but the last 4 digits
return '*' * (len(card_number) - 4) + card_number[-4:]

card = '1234567890123456'
masked = mask_credit_card(card)
print('Masked:', masked) # Output: ************3456

This demonstrates basic editing techniques.

2. Database-Level Encryption​

Encryption converts data into an unreadable format using a key. For card data, it is required for protection at rest and in transit.

Encryption types​

• Transparent Data Encryption (TDE): Encrypts the entire database or files. Supported by SQL Server and Oracle. Advantage: Automatic, does not change applications.
• Column-Level Encryption: Only sensitive columns (e.g., card_number) are encrypted. Example: Always Encrypted in SQL Server.
• Application-Level Encryption: Data is encrypted in the application before being written to the database. Protects against insider threats.
• Encryption in transit: Uses TLS/SSL for data transfer.

Encryption algorithms​

Popular in 2025: AES-256 (standard for PCI DSS), RSA for asymmetric encryption. Trends include post-quantum cryptography (PQC) for protection against quantum threats. Best practices: Key rotation, use of KMS (AWS KMS, Azure Key Vault); AI for key management (58% of enterprises in 2025).

Advantages and disadvantages​

Advantages: Even if leaked, data is useless without the key; reduces the impact of breaches (72% of organizations). Disadvantages: Slows performance; key management is complex.

Python Encryption Example​

Simple hash (for demonstration; in reality, use crypto libraries like cryptography):

import hashlib

def encrypt_data(data, key):
return hashlib.sha256((data + key).encode()).hexdigest()

card = '1234567890123456'
encrypted = encrypt_data(card, 'secret_key')
print('Encrypted (hash):', encrypted)  # Вывод: 4144e1126e6e64fe1b7349d9d2d462458918105e3b9087761f3f0cb71f2f0675

In reality, use AES for reverse encryption.

3. Tokenization and additional methods​

• Tokenization: Replacing the card number with a token. Actual data is stored in a secure vault. Benefit: Minimizes the storage of sensitive data in the database.
• Access control (IAM/RBAC): Restricting access to data.
• Audit and monitoring: Operation logs for anomaly detection.
• DLP (Data Loss Prevention): Blocks unauthorized data theft.
• Multi-layered protection: Combination with WAF, API security.

4. Standards and Trends for 2025​

PCI DSS 4.0 requires encryption and masking of cardholder data. Trends: AI in compliance (58% for key management), PQC, digital sovereignty (BYOK/HYOK). In 2025, encryption will reduce the impact of breaches, with a focus on integrated platforms.

Conclusion​

By combining masking, encryption, and other methods, you can significantly reduce the risk of data breaches. To practice, start with a data audit and tool selection. If you need clarification, ask!