An audit identified PII being utilized in the development environment of a critical application. The Chief Privacy Officer (CPO) is adamant that this data must be removed; however, the developers are concerned that without real data they cannot perform functionality tests and search for specific data. Which of the following should a security professional implement to BEST satisfy both the CPO's and the development team's requirements?
To address the Chief Privacy Officer's requirement to remove personally identifiable information (PII) while ensuring the development team can perform realistic functionality tests and search for specific data, implementing data masking is the best choice. Data masking obscures sensitive data by substituting it with fictional yet structurally and contextually similar data, thus preserving the usability of the data for testing purposes without exposing actual PII. This approach effectively balances the need for data privacy with the necessity for realistic testing environments.
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.
I agree this is the right answer in this case because Data Masking would not allow them to search for specific data results. BUT as a protector of privacy you should be careful when implementing this solution as it takes a certain amount of data to truly make it to where a data analyst cannot figure out individuals. (Source: My partner is a data analyst and she has to approve the use of anonymized data before it can be used for testing such as this)
That makes sense to an extent, but the answer would be more helpful and complete if it could be known how the data analyst handles requests for data that is masked.
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 involves replacing sensitive data with fictional or scrambled data. While this could address the CPO's concerns, the development team's need for real data to perform functionality tests might not be met. Data anonymization strikes a balance between privacy and functionality, making it the most suitable option in this scenario.
I think you could also argue that any real data, even if it is ananoymized, still left in the application, would be contrary to what the CPO is requesting and therefore would not be the most suitable. I don't think they would need actual customer data to perform their tests
I think it satisfies both. For instance, some of the customers' card number could be masked leaving only the last 4 digits.
I'm going to piggyback off this comment to explain in more detail WHY data masking is truly the answer! Let's compare data masking vs data anonymization! Data anonymization: - Generalization of data (reducing the level of detail in the data) - Aggregation (combining the data into groups to prevent identification [total sales per region]) - Randomization (adding noise to individual records to make them indistinguishable) - Suppression (removing certain columns or data points that could lead to identification) Data Masking: - Substitution (replacing original data with fake but structurally similar [henry ford -> john doe] - Shuffling (reordering the data in a column to break any connections between original values) - Encryption & decryption - Tokenization Data masking is combining the ability to hide the data, anonymize it, encrypt & decrypt and also the ability to tokenize it! Open to discussion!
Anonymization (in terms of databases) is simply replacing real data with dummy data. Data masking is solely referring to substituting a generic character for a real one, done x amount of times as in ***-***-1234 for a phone number OR ***** as a zip code. You cannot develop an algorithm for determining if a phone# is valid or not or if a zip code is valid or not. You cannot do lookups, test regular expressions, or really do anything useful as a developer with masked data. I've never heard of Data Masking as replacing original data with fake data, but unfortunately, this is the only correct answer for this exam as Data Anonymization as an answer is missing on this same question (CompTIA is tricky and often you will find a slightly different set of answers that correspond to the same question). In another's exam dump, "Data purge" is substituted for "Data Anonymization", so whatever you do, don't pick A.
We require maintaining the format and context of the data for realistic testing and searches. Data masking partially obscures personal identifiers (PII), ensuring they remain readable enough to fulfill functionality tests and search requirements. Data encryption completely transforms the data, rendering it unreadable unless decrypted. However, decrypted data would contain PII, which developers must not access. Data anonymization irreversibly alters data, rendering it unsuitable for our needs. Data tokenization converts data into unreadable tokens using a token generator. Tokens are mapped to the original data using a decryption method to retrieve their original, unaltered form. Once again, we aim to avoid exposing PII to developers.
Data masking "partially" masks or replaces data to an extent where it remains relevant and suitable for our testing and searching purposes.
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.
Data masking allows developers to use data that looks and behaves like real data without exposing actual sensitive information. This satisfies the Chief Privacy Officer's requirement to protect PII while enabling developers to perform realistic tests and searches with data that mimics the characteristics of the original data.
I'm going with data anonymization, CPO wants PII data removed and developers do not. Data anonymization solves both problems by substituting the real data with similar fake data. The developers dont have to actually get rid of any real data. But the real data is technically removed. The developers and CPO both get what they want.
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 <a href="/cdn-cgi/l/email-protection" class="__cf_email__" data-cfemail="9febf7fafeb1ecf2f6ebf7adafdff0eaebf3f0f0f4b1fcf0f2">[email protected]</a>
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).
Ok - I change it back to Data Masking. I didn't realize that it included dummy data and Anonymization, nowadays, refers to primarily aggregate data that does not include PII.
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.