Data Masking for ScyllaDB

Introduction to Data Masking for ScyllaDB

Data masking has become an essential practice for securing sensitive information in modern data architectures. It is increasingly important, especially in distributed systems like ScyllaDB, which is widely used for high-performance data storage. Data masking allows organizations to protect sensitive data by concealing it while ensuring that authorized users can still access necessary information for testing, analysis, and other non-sensitive operations.

In ScyllaDB, as in other NoSQL databases, masking can be challenging because of the lack of native masking solutions. However, ScyllaDB’s compatibility with Apache Cassandra opens the door for potential solutions, including custom masking techniques. This article will guide you through various methods for implementing data masking in ScyllaDB, focusing on both static and dynamic approaches.

Why Data Masking Matters in ScyllaDB

Protecting Personal Information

Personal Information, such as credit card numbers, emails, and personal details, must be protected. Data masking ensures that even if data is exposed, it cannot be used maliciously. For ScyllaDB users, the absence of a built-in masking feature can be a challenge. Nonetheless, there are ways to implement data masking strategies, either through custom solutions or third-party tools.

Static vs Dynamic Data Masking

Masking types can generally be classified into two categories: static masking and dynamic masking. Static data masking creates a copy of the data with masked values, while dynamic data masking modifies the data during access to keep the original data hidden.

ScyllaDB: Open-Source Data Masking Solutions

Currently, ScyllaDB does not offer a built-in data masking solution. However, developers can create custom solutions depending on their use cases. Let’s explore how you can build a basic data masking approach for a ScyllaDB table.

Example ScyllaDB Table

Consider the following ScyllaDB table:

CREATE TABLE test_keyspace.mock_data (
    id uuid,
    address text,
    credit_card text,
    email text,
    name text,
    phone text,
    PRIMARY KEY (id)
)

Static Data Masking: A Simple Approach for ScyllaDB

In-Place Masking

One of the simplest ways to mask data in ScyllaDB is by using in-place masking. This method involves creating a new table with the sensitive data replaced by masked values. Here’s an example Cassandra Query Language (CQL) command to achieve this:

CREATE TABLE test_keyspace.mock_data_masked AS 
    SELECT id, address, 
           'XXXX-XXXX-XXXX-' || substr(credit_card, -4) AS credit_card, 
           'XXX@' || substr(email, position('@' IN email)) AS email, 
           substr(name, 1, 1) || '***' AS name, 
           'XXX-XXX-' || substr(phone, -4) AS phone 
    FROM test_keyspace.mock_data;

This query creates a masked version of the mock_data table, replacing sensitive data fields with partially obscured values.

Static Masking: Advantages and Disadvantages for ScyllaDB

Pros: – Simple to implement: Requires only a few lines of CQL code. – No impact on performance: Since the data is masked at the storage level, querying the masked data does not require additional processing.

Cons: – Storage overhead: A separate table is required for storing masked data. – Lack of flexibility: Static masking does not offer the same flexibility as dynamic masking, especially when you need to apply the mask to new or changing data.

Dynamic Data Masking: A More Advanced Solution

Implementing Dynamic Data Masking

For more flexibility, dynamic data masking modifies the data at the query level, ensuring that sensitive information is masked only when retrieved. Here’s an example of how you can implement dynamic data masking in ScyllaDB using Python and FastAPI.

from fastapi import FastAPI, WebSocket
from cassandra.cluster import Cluster
import re

app = FastAPI()
cluster = Cluster(["127.0.0.1"])
session = cluster.connect("test_keyspace")

def mask_data(row):
    return {
        "id": row.id,
        "address": row.address,
        "credit_card": "XXXX-XXXX-XXXX-" + row.credit_card[-4:],
        "email": re.sub(r"(^[^@]+)", "XXX", row.email),
        "name": row.name[0] + "***",
        "phone": "XXX-XXX-" + row.phone[-4:],
    }

@app.websocket("/query")
async def proxy_query(websocket: WebSocket):
    await websocket.accept()
    while True:
        query = await websocket.receive_text()
        if not query.lower().startswith("select"):
            await websocket.send_text("Only SELECT queries allowed")
            continue
        rows = session.execute(query)
        result = [mask_data(row) for row in rows]
        await websocket.send_json(result)

In this solution, a reverse proxy acts as a proxy between the client and the ScyllaDB database. The script ensures that sensitive data is masked before being sent to the client.

Dynamic Masking for ScyllaDB: Pros and Cons

Pros: – More flexible: You can apply masking dynamically, without altering the database schema. – Real-time processing: The masking happens at query time, ensuring that data is always up to date.

Cons: – Performance overhead: Masking happens in real-time, which can impact performance, especially for large datasets. – Requires additional setup: You need to set up a proxy layer, which adds complexity to the system.

Using DataSunrise for ScyllaDB Data Masking

Overview of DataSunrise

While custom solutions are effective, managing large-scale data masking across multiple tables and databases can become complex. In such cases, using a third-party tool like DataSunrise can simplify the process. DataSunrise offers both static and dynamic data masking solutions and can act as a database firewall to manage sensitive data securely.

Implementing Static Data Masking with DataSunrise for ScyllaDB

DataSunrise provides a user-friendly interface that allows you to configure static data masking with just a few clicks. The tasks can be applied to individual fields or entire tables, ensuring that your sensitive data is securely masked.

Benefits of Using DataSunrise for Static Data Masking:

• Rule-based configuration: Easily create and manage masking rules.
• No need for custom scripts: DataSunrise provides an out-of-the-box solution, saving development time.
• Scalability: Mask data across multiple tables and databases with minimal effort.

Dynamic Data Masking with DataSunrise and Regular Expressions

DataSunrise also supports dynamic data masking, allowing you to apply rules dynamically to the incoming queries. This feature is particularly useful when dealing with incoming queries or real-time data modifications.

Benefits of Dynamic Masking with DataSunrise:

• Real-time protection: Data is masked as it is accessed.
• Customizable rules: Use regular expressions to fine-tune the masking process.
• Simplified management: Apply different rules across various datasets and environments.

If you want to explore more advanced features of DataSunrise, consider booking a personal online demo or downloading the trial version here.

Best Practices for Data Masking in ScyllaDB

Starting Simple

1. Start simple: Use basic scripts and queries during the testing phase to minimize complexity.

Managing Masking Rules

1. Keep masking rules manageable: Avoid overly complex rules that can lead to maintenance challenges.

Outsourcing Security

1. Outsource security to trusted providers: Leverage third-party tools like DataSunrise for advanced masking features and reliable security compliance.

Conclusion

Data masking is an essential aspect of securing sensitive data in distributed systems like ScyllaDB. Whether you choose a static or dynamic approach, it’s important to consider the specific needs of your project. While open-source solutions can provide flexibility, third-party tools like DataSunrise can offer a more scalable and user-friendly option for managing sensitive data across your entire system.

By following the guidelines and techniques outlined in this article, you can significantly enhance your data protection and ensure compliance with industry standards.