Sensitive Data Discovery: Best Practices and Tools for Secure Management


Discover the importance of sensitive data discovery, best practices for secure management, and tools like DbVisualizer and CLI utilities to protect your data effectively.

Sensitive data discovery is a critical process for identifying, managing, and protecting sensitive information within databases. Proper handling of sensitive data is essential for compliance with data protection regulations, safeguarding against breaches, and maintaining trust with clients and stakeholders.

This article will explore the importance of sensitive data discovery, outline best practices for managing this data, and provide tips on tools and techniques to ensure security throughout the process.

Let’s dive in!

Why Sensitive Data Discovery Matters

Sensitive data includes information such as personal identification numbers, financial records, health data, and other personally identifiable information (PII). This information, if compromised, could lead to identity theft, financial loss, or legal consequences. Here are some key reasons why managing sensitive data securely is paramount:

  1. Compliance: Regulations such as GDPR, HIPAA, and CCPA mandate strict guidelines for handling sensitive data. Non-compliance can result in hefty fines and legal action. The largest fine for GDPR violation was 1.2 billion euros!
  2. Security: Data breaches can cause severe financial and reputational damage to organizations. Protecting sensitive data minimizes the risk of unauthorized access and misuse.
  3. Trust: Customers and stakeholders expect organizations to handle their data with the utmost care. Demonstrating robust data protection practices builds trust and loyalty.

Main Areas to Consider in Sensitive Data Discovery

When embarking on sensitive data discovery, several key areas must be addressed to ensure comprehensive and secure management. Time to dig into them all!

1. Data Classification

Classify data to identify which datasets contain sensitive information. This involves categorizing data based on its level of sensitivity and the potential impact of its exposure.

Example with Terminal/CLI:

1 # using grep to find sensitive data patterns in files 2 grep -rE "(SSN|credit card|password)" /path/to/data

2. Data Masking

Mask sensitive data in non-production environments to prevent unauthorized access. Data masking replaces sensitive data with fictitious yet realistic data.

SQL example:

  1. Connect to your database.
  2. Run a SQL query to mask sensitive data:
1 UPDATE customers 2 SET credit_card_number = 'XXXX-XXXX-XXXX-1234';

3. Encryption

Encrypt sensitive data both at rest and in transit to protect it from unauthorized access. Encryption ensures that even if data is intercepted, it remains unreadable without the decryption key.

Example with Terminal/CLI:

1 # encrypting a file using OpenSSL 2 openssl aes-256-cbc -salt -in sensitive_data.csv -out encrypted_data.csv

4. Access Controls

Implement strict access controls to limit who can view or manipulate sensitive data. Use role-based access control (RBAC) to assign permissions based on user roles.

Example with DbVisualizer:

  1. Navigate to the "Security" section.
  2. Assign roles and permissions to users:
1 GRANT SELECT ON customers TO data_analyst;

5. Audit and Monitoring

Regularly audit and monitor access to sensitive data to detect and respond to suspicious activities. Maintain logs of data access and modifications for accountability.

Example with Terminal/CLI:

1 # using auditd to monitor access to sensitive files 2 auditctl -w /path/to/sensitive_data -p rwxa -k sensitive-data-access

Tools for Sensitive Data Discovery

When it comes to sensitive data discovery, selecting the right tools is crucial. Here are tailored recommendations based on specific use cases.

1. Writing SQL and managing the Database

Recommended Tool: DbVisualizer

A great place to start is to get an overview of the data at hand, via a secure and local database client. DbVisualizer stands out for its unparalleled reliability in connecting to all popular databases, offering exceptional stability and dependability. It operates entirely on your client, making it safe to handle sensitive data.

Its features allow you to do basically everything you need directly from its user-friendly interface. DbVisualizer is the top choice for many banks and is trusted by major organizations like NASA, Tesla, Apple, Google, Meta, Netflix, and Uber.

2. Data Classification

Recommended Tool: Apache Atlas

Apache Atlas is an open-source data governance and metadata framework that helps in classifying, cataloging, and managing data assets. It provides capabilities to define, implement, and manage data classification and metadata management policies across the enterprise.

3. Data Masking

Recommended Tool: Informatica Dynamic Data Masking

Informatica offers robust dynamic data masking solutions that ensure sensitive data is protected in real time without impacting the integrity of the database. It provides on-the-fly masking to obfuscate data as it's accessed based on user roles and permissions.

4. Encryption

Recommended Tool: OpenSSL

OpenSSL is a widely used command-line tool for encrypting files and data. It supports various encryption algorithms and provides secure data encryption both at rest and in transit.

Example with OpenSSL:

1 # encrypting a file 2 openssl aes-256-cbc -salt -in sensitive_data.csv -out encrypted_data.csv

5. Access Controls

Recommended Tool: Role-Based Access Control (RBAC) Systems

Implementing RBAC systems such as those available in database management tools (e.g., PostgreSQL, MySQL) helps to enforce strict access controls. These systems allow the assignment of specific roles and permissions to users, ensuring only authorized personnel can access sensitive data.

6. Audit and Monitoring

Recommended Tool: Splunk

Splunk provides powerful monitoring and auditing capabilities that can track access to sensitive data, analyze logs, and detect suspicious activities in real time. It is particularly useful for maintaining comprehensive audit trails and ensuring data access compliance.


Sensitive data discovery is an essential practice for safeguarding sensitive information within your organization. By implementing best practices such as data classification, masking, encryption, access controls, and regular auditing, you can ensure that sensitive data is managed securely.

Handling sensitive data properly is essential. To set up a process that handles sensitive information properly, you could use tools like DbVisualizer, Apache Atlas, Informatica, OpenSSL, RBAC systems, and Splunk. Enhance your ability to discover and protect sensitive data effectively!


What is sensitive data discovery?

Sensitive data discovery is the process of identifying and managing sensitive information within databases to ensure it is protected from unauthorized access and complies with data protection regulations.

Why is data masking important?

Data masking is significant because it allows the use of realistic data in non-production environments without exposing actual sensitive information, thereby preventing unauthorized access.

How can encryption protect sensitive data?

Encryption protects sensitive data by converting it into an unreadable format that can only be decrypted with the correct key, ensuring data remains secure even if intercepted.

What are some tools for sensitive data discovery?

Tools for sensitive data discovery include DbVisualizer for database management, Apache Atlas for data classification, Informatica for data masking, OpenSSL for encryption, RBAC systems for access control, and Splunk for auditing and monitoring.

What are access controls?

Access controls are security measures that restrict who can view or manipulate sensitive data based on user roles and permissions, ensuring that only authorized individuals have access.

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