Data anonymization is the alteration process of personally identifiable information (PII) in a dataset, to protect individual identification. This way the data can be used and still be protected.

Data masking would best satisfy both the CPO's and the development team's requirements. Data masking is a technique for obscuring sensitive data in a database or other data store, while still preserving the structure and format of the data. Data masking can be used to protect personally identifiable information (PII) or other sensitive data from being accessed or exposed in the development environment. In this case, the CPO is concerned about PII being utilized in the development environment, and is adamant that it must be removed. At the same time, the development team needs real data in order to perform functionality tests and search for specific data. Data masking would allow the CPO's requirement to be satisfied, while still providing the development team with real data to work with.

Data masking might be considered a better option in this scenario for a few reasons: Realistic Data Representation: Data masking typically involves replacing sensitive data with realistic but fictional data. This means the development team can still work with data that accurately represents the production environment without compromising individuals' privacy. Preservation of Data Structure: Data masking techniques usually maintain the structure and format of the original data, ensuring that the testing environment closely resembles the production environment. This is important for accurately assessing the functionality and performance of the application. Ease of Implementation: Implementing data masking techniques is often straightforward and can be integrated into existing development and testing processes without significant disruption. It may require less overhead compared to other methods like data anonymization, which might involve more complex transformations.

Explanation: Data masking involves replacing sensitive information with fictitious or scrambled data while retaining the original data format and structure. This allows the development team to work with realistic data for testing purposes without exposing sensitive PII. By implementing data masking, the security professional can address the CPO's concerns about PII exposure while enabling the development team to perform functionality tests and search for specific data within the development environment.

You can't reverse data anonymization

A is the correct one in my opinion. Contact for full questions [email protected]

Data masking, first than anything, will probably cost money, and second, may disrupt the patterns the developers need to check for testing. Data anonymization, or "dirtying the db", is a better solution.

Masking, still have the access to information but some of it hidden from people who dont need to see it

Coming back to correct my last answer after reviewing more of this dump and the new 701 study guide from Dion Training. It is C - Data Masking Qs 546 is identical and 637 is almost the same with a few different words. 701 guide says Data Masking maintains data authenticity and usability. It is used in test environments especially for software developers. It also reduces risk of data breach.

A variant of this question (different answers) does not have Data anonymization as an answer (although it IS the best thing to do), so A is out. This leaves Data masking and Data tokenization. The problem with masking is that you cannot perform certain functions (like validating a credit card checksum or checking a date, name lookups and so forth - not much you can do with a bunch of asterisks!) The problem with tokenization is that you would be replacing the data with tokens (long strings) and it's not really applicable to databases. Again you have the same problem with masking - you can't do anything with a bunch of random strings. There is only one correct answer: Anonymization. You can scramble names, use fake card data, birth dates, SSNs, addresses, etc and work happily developing for such (I'm a 25year veteran developer and we've use Anonymization for decades).

Can someone explain why this isn’t Tokenization? From my understanding, Masking is replacing data with characters like asterisks (like showing a credit card as 1234************). I thought tokenization would be changing the value of something, so like changing a name from Richard to Jeremy, keeping the data, but giving fake names / PII. For Anonymization, I thought the data would be lost, so that the analysts wouldn’t be able to analyze the dataset like they would want. I googled the difference between tokenization and masking, and from everything I see, masking would make it harder for analysts to analyze.

In this case the developers need the PII to do their work so anonymizing the data is not an option (as this would remove the PII entirely), the best option is masking, where the full data is obscured to keep the information private, but retains the identity of the data.

Data masking is the process of hiding sensitive, classified, or personal data from a dataset, then replacing it with equivalent random characters, dummy information, or fake data. This essentially creates an inauthentic version of data, while preserving the structural characteristics of the dataset itself. Data masking tools allow data to be used for purposes like user training and software testing – protecting the actual sensitive data while offering a functional substitute for critical organization usage. https://www.k2view.com/blog/data-anonymization-vs-data-masking/#:~:text=Data%20anonymization%20removes%20classified%2C%20personal,confidential%20data%20with%20altered%20values.

A - key words "PII" and "real data". Data masking is not real data it is adding x in place of real data and no developer can test and validate. Data anonymization is the process of removing personally identifiable information from a data set so that the people whom the data describe remain anonymous (per DION Training). People remain private and there is real data to test.

As per the question the Data must be removed and accroding to the requirement the Option A data anonymization removes classified, personal or sensitive information from datasets while using for development purpose.

Exact same question as 546. Data Anonymization is not a choice in 546. The correct answer is Data Masking.

A. Data Anonymization From my Mike Meyers’ CompTIA Sec+ SY0-601 Certification Guide: Data masking is a process of hiding original data with modified content, such as using asterisks to hide all but the last four digits of a credit card number. Data Anonymization: There’s a lot of useful information that comes from PII/PHI that doesn’t require actually identifying the people whose information is part of the data set. How many people in Houston, Texas are morbidly obese? What’s the average age for women in the US to have their first child? How many complaints came in last month about our new smart phone app? These types of statistics are very helpful, but it’s up to the data set provider to ensure that no PII/PHI is retrieved by implementing anonymization of the data. Anonymization removes all PII/PHI.