Data Masking

What is Data Masking?
Data masking is the process of hiding or obscuring sensitive data in a database by replacing it with randomized characters, numbers, or other values. It is typically used to ensure that confidential information is not exposed to unauthorized users. Data masking can also be used to protect data from being altered or tampered with in any way.

Why Data Masking is Important?
Data masking is an important security measure for customs house brokerage companies, as it helps protect sensitive data from unauthorized access. Data masking helps ensure that any data stored by the company is secure, and that only authorized personnel have access to it. Data masking also reduces the risk of identity theft and fraud by making it difficult for hackers to gain access to confidential information. It also reduces the risk of data breaches, which can be costly for companies in terms of financial losses. Finally, data masking helps ensure compliance with regulations such as GDPR and other privacy regulations.

What are the risk associated with not implementing Data Masking?

1. Data Security Breach: If data masking is not implemented, sensitive data can be easily accessed by unauthorized personnel, leading to potential data security breaches.
2. Privacy Violations: Without data masking, companies may be unable to protect the privacy of customers’ personal information, such as Social Security numbers and credit card numbers, which could lead to legal action against the company.
3. Regulatory Non-Compliance: Companies in certain industries may be subject to regulatory requirements that include data masking. Without it, those companies may be subject to hefty fines or other sanctions for non-compliance.
4. Damage to Reputation: Data breaches can cause irreparable damage to a company’s reputation and trustworthiness in the eyes of its customers and partners

Steps to implement Data Masking

1. Identify the data that needs to be masked: It is important to first identify the data that needs to be masked. Identify the type of data and the format of the data. This will help determine which masking techniques should be used and how much masking is necessary.
2. Decide which masking techniques to use: Data masking can be done in a variety of ways. Choose a method that will provide the level of protection needed while still allowing users to access the data they need. Examples include tokenization, encryption, redaction, obfuscation and pseudonymization.
3. Implement the chosen masking techniques: Once you have identified which masking technique you want to use, it is time to begin implementing it into your system or database
4. Test the masked data: Test the data to ensure that it is properly protected and that users are able to access the data they need without compromising security.
5. Monitor and review the masked data: Regularly review and monitor the masked data to ensure that it is functioning as intended. This is especially important if there are any changes made to the system or database.
6. Document the data masking process: Document all steps taken to implement data masking, including any changes made and the results of any tests conducted. This will help ensure that the data remains properly protected and can be quickly accessed if needed.

Data Masking Techniques

1. Tokenization: This technique replaces sensitive information with randomized characters or tokens that represent the same data type, such as credit card numbers.
2. Substitution: This technique replaces sensitive data with other values that are not real but appear to be valid.
3. Perturbation: This technique slightly changes the value of the data so that it is still realistic, but not accurate or true.
4. Redaction: This technique completely removes or blocks out sensitive information from view, typically using black boxes or asterisks to hide the data.
5. Encryption: This technique encodes data so that it can only be decoded by authorized users with a valid key or password.

List of Opensource tools available to do Data Masking

1. Anonymizer: An open source data masking tool that can be used to anonymize sensitive data for testing and development purposes.
2. Privacy-preserving Data Mining (PPDM): An open source data masking and de-identification tool for protecting sensitive information in data mining applications.
3. Apache DataSketches: An open source library for approximate query processing with privacy preserving capabilities.
4. Distributed Data Masking System (DDMS): An open source distributed system for data masking and de-identification of sensitive information in large datasets.
5. FAIR: A free, open source anonymization framework that enables organizations to protect their sensitive data while still being able to use it for analytics purposes.
6. DataMasker: An open source data masking and obfuscation tool for transforming data into realistic but non-identifiable values.
7. Masking Agent: An open source tool for masking sensitive information in databases, files and streams.
8. Maskerade: A free and open source data masking library written in Java.
9. Obfuscator-LLVM: An open source LLVM-based obfuscator for protecting sensitive data from reverse engineering or code tampering attacks.
10. Paragon Initiatives Data Masker: A free, open source data masking tool for transforming production databases into sanitized versions for testing or development purposes.