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7 Best Practices for Secure Salesforce DevOps with Data Masking_AutoRABIT

7 Best Practices for Secure Salesforce DevOps with Data Masking

7 Best Practices for Secure Salesforce DevOps with Data Masking_AutoRABIT

Data masking is a critical security consideration for protecting sensitive Salesforce data, but only if it’s used appropriately.

Why it Matters: Properly protecting personal identifiable information (PII), financial information, medical data, and other types of sensitive information requires a comprehensive approach. Failing to keep this data safe leads to compliance failures and loss of consumer trust.

  • Regulated industries work with their clients’ most sensitive information, which makes them main targets for cybercriminals.
  • Falling out of compliance leads to costly fines and penalties.
  • Finance companies, for example, have been targeted by cybercriminals more than 20,000 times over the last 20 years, causing $12 billion in losses.

Here are 7 best practices for data masking in Salesforce:

  1. Identify Your Sensitive Data
  2. Classify Data Based on Level of Sensitivity
  3. Secure Integration Points
  4. Continuously Monitor Access
  5. Provide Ample Training
  6. Implement Data Masking
  7. Leverage a Comprehensive Data Security Platform

1. Identify Your Sensitive Data

The first step is to take time to better understand the sensitive data stored in your Salesforce environment.

Sort through your data to identify what requires extra levels of protection—PII, financial data, intellectual property, and medical information.

Ensure you can locate the data that requires heightened security. If you don’t know where it is, you won’t be able to properly protect it.

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2. Classify Data Based on Level of Sensitivity

7 Best Practices for Secure Salesforce DevOps with Data Masking_AutoRABIT

Not all types of sensitive data require the highest levels of protection. Take the information that you’ve identified as sensitive and put it through another round of analysis.

Classify your sensitive data based on its level of sensitivity and whether it is addressed by regulatory requirements.

This helps prioritize data masking efforts and ensures that the most critical data receives the highest level of protection.

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3. Secure Integration Points

Your Salesforce environment is likely connected to third-party applications. The points at which these applications connect can be an entry point for bad actors.

Secure the connectors between Salesforce and external systems to prevent unauthorized access to sensitive data.

Data masking can be used—along with encryption and authentication—to protect data that moves between these environments.

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4. Continuously Monitor Access

7 Best Practices for Secure Salesforce DevOps with Data Masking_AutoRABIT

Unauthorized access might not always be malicious, but even team members who don’t mean harm can compromise sensitive data by accident.

Implement logging and monitoring mechanisms to track access to sensitive data within Salesforce.

Regularly review audit logs to detect any unauthorized access attempts and take appropriate action.

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5. Provide Ample Training

Having a thorough understanding of data masking within Salesforce and how to interact with sensitive information greatly reduces accidents that threaten your data.

Provide training to developers, administrators, and other personnel involved in Salesforce DevOps on data security best practices and the importance of protecting sensitive data.

Fostering a culture of security awareness within your organization keeps everyone on the same page.

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6. Implement Data Masking

Use a reliable release management tool to protect your sensitive data, so even if an unauthorized individual accesses your data, they can’t do anything with it.

Data masking techniques should be used to obfuscate sensitive data in non-production environments.

This can include techniques like pseudonymization, tokenization, or anonymization to replace sensitive data with realistic but fictional values.

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7. Leverage a Comprehensive Data Security Platform

7 Best Practices for Secure Salesforce DevOps with Data Masking_AutoRABIT

Data masking is a critical aspect of protecting your sensitive Salesforce data, but it shouldn’t be the only one.

Leverage a data security platform that protects your sensitive data from all angles and offers tools like static code analysis, security posture management, backup and recovery, and more.

Protecting your sensitive information needs to be a top priority. Data masking goes a long way toward assisting in this effort. Continually monitor and revisit your security approach to ensure your strategy remains contemporary and effective.

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Next Step…

Data masking is an essential tool for protecting sensitive data and adhering to data security regulations. Combining this with other security tools offers comprehensive coverage that you need to keep this data safe.

Check out our infographic, 7 Tips for Using Code Scan Tools to Improve Data Security, to learn how you can utilize code scanning tools to support your Salesforce data security strategy.

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FAQs

What are some common data masking techniques?

There are a variety of ways to approach data masking, such as substitution, where sensitive data is replaced with fictional but structurally similar data. An example of this would be replacing names with random names or credit card numbers with fake numbers. Shuffling is another technique where the order of sensitive data is rearranged, maintaining statistical characteristics while making individual data points unidentifiable. Another method is tokenization, where sensitive data is replaced with a randomly generated token. The original data is stored in a secure location and only accessible with an authorized decryption.

How does data masking differ from encryption?

Data masking and encryption are both common ways to protect sensitive data, but there are some differences in both their approach and purpose. Generally, while data masking aims to obscure sensitive information while retaining usability, encryption focuses on rendering data unreadable to anyone without proper authorization, emphasizing data confidentiality and security. Data masking focuses on hiding or obfuscating sensitive data by replacing it with fictional or modified data, preserving the data’s format and structure while rendering it meaningless to unauthorized users. This allows organizations to use the data for certain purposes such as testing or analytics while safeguarding the sensitive information. Encryption provides a higher level of security against unauthorized access or interception. It transforms data into an unreadable format using cryptographic algorithms and keys, making it accessible only to authorized parties with the corresponding decryption key. Encryption ensures data confidentiality and integrity during storage, transmission, and processing.

Is data masking suitable for all types of data?

Data masking is generally used for data sets that contain sensitive information. It’s commonly seen in databases, applications, and other systems where sensitive data is stored or processed. However, its suitability can vary depending on the specific requirements and context. Data masking is particularly effective for structured data like names, addresses, or credit card numbers, where it’s relatively straightforward to apply techniques such as substitution, shuffling, or tokenization without compromising data integrity or usability. However, for unstructured data or highly sensitive information, such as medical records or financial transactions, additional security measures like encryption or access controls may be necessary to ensure comprehensive protection. While data masking is a valuable tool for many scenarios, organizations should evaluate its appropriateness based on the nature of the data and the level of security required.

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