How to Mask Sensitive Data in PostgreSQL ?

In 1986, PostgreSQL emerged from a rich history at UC Berkeley, establishing itself as a sophisticated, open-source relational database system. By 1996, it had evolved into PostgreSQL, reflecting its support for SQL language. Known for its fault tolerance and reliability, it complies with ACID principles and incorporates features like write-ahead logging and multi-version concurrency control. 

Its open-source nature and support from a global community ensure continuous improvement and robust security, making it a top choice for developers and administrators seeking a reliable, scalable, and cost-effective database solution. Given the database's extensive use in handling critical data, ensuring that this data is not lost or compromised is crucial for maintaining the integrity and reliability that PostgreSQL is known for. This guide will explore why DLP is vital for PostgreSQL while offering insights and strategies to safeguard your data effectively.

Understanding Data Exposure Risks in PostgreSQL

According to Google's January 2023 Threat Horizons report, PostgreSQL is a common target for attackers, ranking third in frequency behind SSH and Jenkins. Weak passwords, poorly configured PostgreSQL instances, manual deployments of PostgreSQL, and misconfigurations that could be exploited due to improper authentication methods, user roles, and access permissions are the primary culprits. 

In fact, a recent Wiz research revealed vulnerabilities in Azure PostgreSQL that could expose user databases. These vulnerabilities, known as ExtraReplica, affected the Azure Database for PostgreSQL Flexible Server and included a privilege escalation vulnerability and a cross-account authentication bypass. This posed a major security risk and could potentially result in customer data being accessed without any trace of the attacker's presence.

This brings us to the important point - the risks involved with PostgreSQl.

Common Data Exposure Risks in PostgreSQL

• Improper user privileges: PostgreSQL's user privileges, if too broad or outdated, can result in unauthorized access to sensitive data. For instance, users with unnecessary elevated privileges could misuse their access, potentially leading to unauthorized data modifications, deletions, or disclosures. Modifying PostgreSQL to grant superuser privileges to users on cloud services can lead to potential security vulnerabilities. This change allows users to execute commands with elevated permissions, potentially compromising data security and integrity. Additionally, in Cloud SQL, the ability to elevate privileges and change ownership of database objects to a superuser role can be exploited for unauthorized actions, posing a significant security risk such as altering database structures or accessing sensitive data.

• Malware: PostgreSQL databases are at risk of compromise through malware-infected devices belonging to legitimate users. For example, malware could empower attackers to execute unauthorized SQL queries. Infected devices used by legitimate users can be pathways for attackers to access sensitive data, resulting in potential data corruption or theft.

• Susceptible databases: Default configurations or unpatched vulnerabilities make databases vulnerable to attacks. In the case of PostgreSQL, this might involve exposed ports or default admin credentials. Unpatched and default-configured databases are easily exploitable and can lead to unauthorized access and potential data breaches.

• SQL injection: SQL Injection poses a significant threat to PostgreSQL databases, as it involves injecting malicious SQL statements to compromise data integrity and security. This can result in unauthorized data manipulation, theft, or complete database compromise.

• PostgreSQL misconfigurations: PostgreSQL misconfigurations, such as inadequate security settings or accidental exposure of sensitive data, can put a company at risk for data breaches. These misconfigurations can leave the database vulnerable to unauthorized access or loss of critical information, leading to potential compliance issues and damage to the organization's reputation.

• Lack of effective auditing: Regularly auditing and monitoring PostgreSQL databases is crucial for detecting suspicious activities or breaches. Security breaches can go unnoticed without proper attention and prolong data exposure, putting sensitive information at risk of damage.

• Sandbox risks: Failure to properly isolate development environments from production in PostgreSQL could result in unintentional data exposure or leaks during testing processes.

Other Threats Include

• Data exfiltration and manipulation, resulting in data breaches
• Denial-of-service (DoS) and distributed denial-of-service 
• (DDoS) attacks 
• Packet sniffing
• Database backup exposure
• Known and unknown database vulnerabilities
• Human error leading to phishing or other social engineering attacks 
• Insider threats

Data Masking Techniques

Data masking techniques in PostgreSQL include:

Bulk data masking

PostgreSQL is equipped to handle extensive datasets with its bulk masking techniques. These methods, implemented through scripting or built-in functions, allow for the efficient processing of large amounts of data while ensuring data integrity and operational performance during the masking process. To achieve this, PostgreSQL utilizes database functions, triggers, and external tools that seamlessly integrate with the platform for optimal results.

Pros: 

1. Streamlined handling of extensive data sets through bulk masking for improved efficiency.
2. Preservation of data integrity while implementing masking for enhanced security.
3. Safeguarding sensitive data from unauthorized access by utilizing bulk masking.

Cons:

1. Potential complexity in implementing bulk masking, particularly when involving scripting or external tools.
2. Possible impact on database performance depending on the scale of application.
3. Limited adaptability to address all unique data protection requirements, potentially necessitating custom solutions.

Limitations of Data Masking Tools

Native data masking in PostgreSQL has limitations, which can cause various issues. For instance, in a financial database where sensitive customer information needs to be masked, the basic set of native functions for masking may not suffice.

In such cases, a custom solution is usually implemented. However, if this solution is not robust enough, it could potentially expose sensitive information like bank account details during certain queries or to unauthorized users, leading to a data breach. Moreover, complex masking logic could also negatively impact the database's performance and slow down query responses - particularly with large volumes of data.

This could significantly affect critical banking operations and overall system productivity. Companies should have a comprehensive and efficient data masking solution specifically designed for PostgreSQL databases to avoid these issues and ensure secure operations.

Securing PostgreSQL with DLP

One of the first steps in ensuring data security is identifying what information must be protected. This includes determining the types of data a company processes, where it is stored, and who has access to it. It's important to assess the strength of controls in place for keeping sensitive information secure, which can be done by searching through company databases for PII, financial records, health and insurance data, and any other information that could potentially lead to identity theft or put the business at risk if accessed by unauthorized users. The next step is to secure PostgreSQL with Data Loss Prevention (DLP).

Here is how a powerful DLP can keep your database safe:

• Access controls: This involves assigning specific access levels to different users. For instance, a database administrator may have complete access, while a data analyst may only read-only access to certain tables. This helps prevent unauthorized access to data.
• Data encryption: Securing data through encryption while it is at rest and in motion prevents unauthorized access. For example, implementing TLS/SSL protocols ensures that any data intercepted during transmission cannot be read.
• Activity monitoring and auditing: Monitoring database queries and modifications can help identify unauthorized access or unusual behavior. For instance, receiving an alert for unauthorized login attempts allows for a swift response to potential security breaches. 
• Data masking and redaction: Data masking and automated redaction are valuable tools in situations where database access is necessary for developers or analysts, but sensitive information should remain hidden. An example is masking customer phone numbers within a sales database.

How Strac DLP can Mask Selective Sensitive Data in PostgreSQL

Strac SaaS DLP is an endpoint DLP and CASB that protects business data by discovering (scanning), classifying, and remediating sensitive data like SSN, driver license, credit Cards, bank Numbers, IP (Confidential Data), etc. across all databases (Postgre) communication channels like O365, Slack, GWorkspace (Gmail, Google Drive), Email, One Drive, Sharepoint, Jira, Zendesk, Salesforce, etc. and also endpoints like Mac, Windows.

With built-in search filters and customizable rules, Strac can identify financial, and medical records to ensure their protection. It also enables targeted searches in specific databases or tables to ensure complete visibility into all data the organization processes. This helps limit data exposure and facilitates setting appropriate levels of protection for optimal security measures.

Strac quickly identifies columns containing the sought-after information and then creates data masking, auditing, and security rules. These rules allow database administrators to limit data exposure and ensure compliance with HIPAA, PCI-DSS, SOX, and GDPR regulations.

We employ several techniques for protecting data within a database table. Tokenization involves substituting sensitive information with a unique and meaningless identifier known as a token. For instance, a credit card number like 1234 5678 9012 3456 could be replaced with the token -T4Ngz9sLsZ, which holds no significance beyond the payment processing system. On the other hand, format-preserving pseudonyms generate synthetic identifiers from sensitive data that maintain the original format and length.

For example, the name John Doe might be transformed into Charles Smith, while a date of birth like 12 01 1923 could become 02 13 1982.Meanwhile, masking selectively reveals some parts of the data while replacing the rest with Xs or other characters. For instance, an email address such as johndoe@example.com might be masked as @example.com or je@e.com.

Strac will establish a connection to the database instance and apply masking based on the provided configuration.

Let's check out an example: Below is a table with five fields: user_id, name, email, company_name, and phone.

In the above table, we will apply different redaction experiences:

user_id: we will keep user_id as-is. So, values of user_id will be the same after redaction

name: we will generate a pseudonym, so it will be fake data that will be format preserving

email: we will mask the username and keep the domain name. Note: we will not apply length preserving on username

company_name: we will keep only the first character and mask remaining while preserving length.Phone: we will tokenize the phone number and generate a token

Enhance your data security and protect your database with Strac's data classification and DLP integrations. Schedule a demo today.