CDPSE Certified Data Privacy Solutions Engineer Questions and Answers

The most effective way to incorporate privacy by design principles into applications is to include privacy requirements in software development practices, because this ensures that privacy is considered and integrated from the early stages of the design process and throughout the entire lifecycle of the application. Software development practices include activities such as defining the scope, objectives, and specifications of the application, identifying and analyzing the privacy risks and impacts, selecting and implementing the appropriate privacy-enhancing technologies and controls, testing and validating the privacy functionality and performance, and monitoring and reviewing the privacy compliance and effectiveness of the application. By including privacy requirements in software development practices, the organization can achieve a proactive, preventive, and embedded approach to privacy that aligns with the privacy by design principles.

References:

• CDPSE Review Manual, 2023 Edition, Domain 2: Privacy Architecture, Section 2.1.2: Privacy Requirements, p. 75
• CDPSE Review Manual, 2023 Edition, Domain 2: Privacy Architecture, Section 2.2.1: Privacy by Design Methodology, p. 79-80
• The 7 Principles of Privacy by Design | Blog | OneTrust1

A peer-to-peer (P2P) system architecture is a network model where each node (peer) can act as both a client and a server, and communicate directly with other peers without relying on a centralized authority or intermediary. A P2P system architecture best supports anonymity for data transmission, by providing the following advantages:

• It can hide the identity and location of the peers, by using encryption, pseudonyms, proxies, or onion routing techniques, such as Tor1 or I2P2. These techniques can prevent eavesdropping, tracking, or censorship by third parties, such as Internet service providers, governments, or hackers.
• It can distribute the data across multiple peers, by using hashing, replication, or fragmentation techniques, such as BitTorrent3 or IPFS4. These techniques can reduce the risk of data loss, corruption, or tampering by malicious peers, and increase the availability and resilience of the data.
• It can enable the peers to control their own data, by using consensus, validation, or incentive mechanisms, such as blockchain5 or smart contracts. These mechanisms can ensure the integrity and authenticity of the data transactions, and enforce the privacy policies and preferences of the data owners.

A privacy audit is a systematic and independent examination of an organization’s privacy policies, procedures, practices, and controls to assess their compliance with applicable laws, regulations, standards, and best practices. A privacy audit may result in various outputs, such as findings, recommendations, observations, or opinions. Among the options given, the output that is most likely to trigger remedial action is the identification of deficiencies in how personal data is shared with third parties. This is because such deficiencies may pose significant risks to the privacy and security of the data subjects, as well as to the reputation and legal liability of the organization. Remedial action may include implementing contractual safeguards, technical measures, or organizational changes to ensure that third parties respect and protect the personal data they receive from the organization.

References: CDPSE Review Manual, 2021, p. 181

Restricting access to authorized users is the best control to secure application programming interfaces (APIs) that may contain personal information, as it would prevent unauthorized access, modification or disclosure of the personal information by third parties or intermediaries. Restricting access to authorized users can be achieved by using various methods, such as authentication, authorization, encryption, tokens or certificates. The other options are not effective controls to secure APIs that may contain personal information. Encrypting APIs with the organization’s private key is not a feasible or desirable method, as it would make the APIs unreadable by anyone who does not have the corresponding public key, which would defeat the purpose of using APIs for interoperability and integration. Requiring nondisclosure agreements (NDAs) when sharing APIs is not a reliable or enforceable method, as it would depend on the compliance and cooperation of the parties who receive the APIs, and it would not prevent unauthorized access, modification or disclosure of the personal information by third parties or intermediaries who are not bound by the NDAs. Sharing only digitally signed APIs is not a sufficient method, as it would only ensure the authenticity and integrity of the APIs, but it would not prevent unauthorized access, modification or disclosure of the personal information by third parties or intermediaries who can read or intercept the APIs1, p. 90-91 References: 1: CDPSE Review Manual (Digital Version)

The data protection principle that is applied when an online business posts its customer data protection notice that includes a statement indicating information is collected on how products are used, the content viewed, and the time and duration of online activities is lawfulness and fairness. Lawfulness and fairness are two of the core principles of data protection under various laws and regulations, such as the GDPR or the CCPA. They state that personal data should be processed lawfully, fairly and in a transparent manner in relation to the data subject. By posting a customer data protection notice that informs customers about what information is collected and for what purpose, the online business demonstrates its compliance with these principles.

System use requirements, data integrity and confidentiality, or data use limitation are not the correct names of the data protection principles that are applied in this case. System use requirements are not a specific principle of data protection, but rather a general term that refers to the rules or policies that govern how users can access and use a system or service. Data integrity and confidentiality are two aspects of the security principle of data protection, which states that personal data should be processed in a manner that ensures appropriate security of the personal data. Data use limitation is not a specific principle of data protection either, but rather a concept that relates to the purpose limitation principle, which states that personal data should be collected for specified, explicit and legitimate purposes and not further processed in a manner that is incompatible with those purposes.

References: A guide to the data protection principles | ICO, Data Protection Principles: Core Principles of the GDPR - Cloudian, Data Protection Basics: The 7 data protection principles

The primary reason for an organization to use hash functions when hardening application systems involved in biometric data processing is to reduce the risk of sensitive data breaches, because hash functions are one-way mathematical functions that transform biometric data into a unique and irreversible representation that cannot be reconstructed or reversed. This means that even if an attacker gains access to the hashed biometric data, they cannot use it to identify or impersonate the individual. Hash functions also help preserve the privacy and confidentiality of biometric data by preventing unauthorized access, modification, or disclosure.

References:

• CDPSE Exam Content Outline, Domain 2 – Privacy Architecture (Privacy Architecture Implementation), Task 2: Implement privacy solutions1.
• CDPSE Review Manual, Chapter 2 – Privacy Architecture, Section 2.3 – Privacy Architecture Implementation2.
• CDPSE Certified Data Privacy Solutions Engineer All-in-One Exam Guide, Chapter 2 – Privacy Architecture, Section 2.4 – Remote Access3.

Data privacy standards are the set of rules, guidelines, and best practices that define the requirements and expectations for the collection, processing, storage, sharing, and disposal of personal data. Data privacy standards help to ensure that personal data is treated in a fair, lawful, transparent, and secure manner, as well as to comply with the applicable privacy laws and regulations. Data privacy standards also help to define the data retention time in a stream-fed data lake that includes personal data, as they specify the criteria and conditions for how long personal data can be kept in the data lake, based on factors such as the purpose, necessity, relevance, and quality of the data. Data retention time is an important aspect of data privacy, as it affects the risk of data breaches, unauthorized access, or misuse of personal data.

References: CDPSE Review Manual, 2021, p. 80

A data loss prevention (DLP) tool is a software solution that monitors, detects and prevents the unauthorized transmission or leakage of sensitive data, such as personal data, from an organization’s network or devices. A DLP tool can help to ensure the effectiveness of a policy requiring the encryption of personal data if transmitted through email, by applying the following controls:

• Scanning the content and attachments of outgoing emails for personal data, such as names, email addresses, biometric data, IP addresses, etc.
• Blocking or quarantining emails that contain unencrypted personal data, and alerting the sender and/or the administrator of the policy violation.
• Encrypting personal data automatically before sending them through email, using encryption standards and algorithms that are compliant with data protection laws and regulations, such as the General Data Protection Regulation (GDPR) or the California Consumer Privacy Act (CCPA).
• Generating audit logs and reports of email activities and incidents involving personal data, and providing visibility and accountability for policy compliance.

The other options are less effective or irrelevant to ensure the effectiveness of the policy. Providing periodic user awareness training on data encryption is a good practice, but it does not guarantee that users will follow the policy or know how to encrypt personal data properly. Conducting regular control self-assessments (CSAs) is a useful method to evaluate the design and operation of the policy, but it does not prevent or detect policy violations in real time. Enforcing annual attestation to policy compliance is a formal way to demonstrate user commitment to the policy, but it does not verify or measure the actual level of compliance.

References:

• The Complexity Conundrum: Simplifying Data Security - ISACA, section 3: “Data loss prevention (DLP) solutions can help prevent unauthorized access to sensitive information by monitoring network traffic for specific keywords or patterns.”
• Guide to Securing Personal Data in Electronic Medium, section 3.2: “Organisations should consider implementing DLP solutions to prevent unauthorised disclosure of personal data via email.”
• Encryption in the Hands of End Users - ISACA, section 2: “A key goal of encryption is to protect the file even when direct access is possible or the transfer is intercepted.”

Attribute-based access control (ABAC) is the best approach for limiting the access of regional HR team members to employee data only within their regional office, because it allows for fine-grained and dynamic access control based on attributes of the subject, object, environment, and action. Attributes are characteristics or properties that can be used to describe or identify entities, such as users, resources, locations, roles, or permissions. ABAC uses policies and rules that evaluate the attributes and grant or deny access accordingly. For example, an ABAC policy could state that a user can access an employee record if and only if the user’s role is HR and the user’s region matches the employee’s region. This way, the access control can be tailored to the specific needs and context of the organization, without relying on predefined or fixed access levels.

References:

• Attribute-Based Access Control (ABAC), NIST
• What is Attribute-Based Access Control (ABAC)?, Axiomatics
• Access Control Models – Westoahu Cybersecurity, Westoahu Cybersecurity

The first consideration when conducting a privacy impact assessment (PIA) is the applicable privacy legislation that governs the collection, processing, storage, transfer, and disposal of personal data within the scope of the assessment. The applicable privacy legislation may vary depending on the jurisdiction, sector, or purpose of the data processing activity. The PIA should identify and comply with the relevant legal requirements and obligations for data protection and privacy, such as obtaining consent, providing notice, ensuring data quality and security, respecting data subject rights, and reporting data breaches. The applicable privacy legislation also determines the criteria, methodology, and documentation for conducting the PIA.

References:

• ISACA, Performing an Information Security and Privacy Risk Assessment1
• ISACA, Best Practices for Privacy Audits2
• ISACA, GDPR Data Protection Impact Assessments3
• ISACA, GDPR Data Protection Impact Assessment Template4

The best way to validate that privacy practices align to the published enterprise privacy management program is to conduct an audit. An audit is an independent and objective examination of evidence to provide assurance that privacy practices are effective and compliant with the enterprise privacy management program. An audit can also identify any gaps or weaknesses in the privacy practices and provide recommendations for improvement. An audit can be conducted internally or externally, depending on the scope, objectives, and standards of the audit. References: : CDPSE Review Manual (Digital Version), page 83

The best practice to protect data privacy when disposing removable backup media is B. Data sanitization.

A comprehensive explanation is:

Data sanitization is the process of permanently and irreversibly erasing or destroying the data on a storage device or media, such as a hard drive, a USB drive, a CD/DVD, etc. Data sanitization ensures that the data cannot be recovered or reconstructed by any means, even by using specialized software or hardware tools. Data sanitization is also known as data wiping, data erasure, data destruction, or data disposal.

Data sanitization is the best practice to protect data privacy when disposing removable backup media because it prevents unauthorized access, disclosure, theft, or misuse of the sensitive or confidential data that may be stored on the media. Data sanitization also helps to comply with the legal and regulatory requirements and standards for data protection and privacy, such as the General Data Protection Regulation (GDPR), the Health Insurance Portability and Accountability Act (HIPAA), the Payment Card Industry Data Security Standard (PCI DSS), etc.

There are different methods and techniques for data sanitization, depending on the type and format of the storage device or media. Some of the common methods are:

• Overwriting: Overwriting replaces the existing data on the device or media with random or meaningless data, such as zeros, ones, or patterns. Overwriting can be done multiple times to increase the level of security and assurance. Overwriting is suitable for magnetic media, such as hard disk drives (HDDs) or tapes.
• Degaussing: Degaussing exposes the device or media to a strong magnetic field that disrupts and destroys the magnetic structure and alignment of the data. Degaussing renders the device or media unusable and unreadable. Degaussing is suitable for magnetic media, such as hard disk drives (HDDs) or tapes.
• Physical Destruction: Physical destruction involves applying physical force or damage to the device or media that breaks it into small pieces or shreds it. Physical destruction can be done by using mechanical tools, such as shredders, crushers, drills, hammers, etc., or by using thermal methods, such as incineration, melting, etc. Physical destruction is suitable for any type of media, such as hard disk drives (HDDs), solid state drives (SSDs), USB drives, CDs/DVDs, etc.

Data encryption (A) is not a good practice to protect data privacy when disposing removable backup media because it does not erase or destroy the data on the media. Data encryption only transforms the data into an unreadable format that can only be accessed with a key or a password. However, if the key or password is lost, stolen, compromised, or guessed by an attacker, the data can still be decrypted and exposed. Data encryption is more suitable for protecting data in transit or at rest, but not for disposing data.

Data scrambling © is not a good practice to protect data privacy when disposing removable backup media because it does not erase or destroy the data on the media. Data scrambling only rearranges the order of the bits or bytes of the data to make it appear random or meaningless. However, if the algorithm or pattern of scrambling is known or discovered by an attacker, the data can still be unscrambled and restored. Data scrambling is more suitable for obfuscating data for testing or debugging purposes, but not for disposing data.

Data masking (D) is not a good practice to protect data privacy when disposing removable backup media because it does not erase or destroy the data on the media. Data masking only replaces some parts of the data with fictitious or anonymized values to hide its true identity or meaning. However, if the original data is still stored somewhere else or if the masking technique is weak or reversible by an attacker, the data can still be unmasked and revealed. Data masking is more suitable for protecting data in use or in analysis, but not for disposing data.

References:

• What Is Data Sanitization?1
• How to securely erase hard drives (HDDs) and solid state drives (SSDs)2
• Secure Data Disposal & Destruction: 6 Methods to Follow3

Using hash values with stored personal data best enables an organization to detect changes to the data, because hash values are unique and fixed outputs that are generated from the data using a mathematical algorithm. If the data is altered in any way, even by a single bit, the hash value will change dramatically. Therefore, by comparing the current hash value of the data with the original or expected hash value, the organization can verify the integrity and authenticity of the data. If the hash values match, it means that the data has not been tampered with. If the hash values differ, it means that the data has been corrupted or modified.

References:

• Ensuring Data Integrity with Hash Codes, Microsoft Learn
• What is ‘hashing,’ and does it help avoid the obligations imposed by the new privacy regulations?, Data Privacy Dish

The most useful information for prioritizing database selection for encryption is the asset classification scheme. An asset classification scheme is a system of organizing and categorizing assets based on their value, sensitivity, criticality, or risk level. An asset classification scheme helps to determine the appropriate level of protection or handling for each asset. For example, an asset classification scheme may assign labels such as public, internal, confidential, or secret to different types of data based on their impact if compromised. Databases that contain higher-classified data should be prioritized for encryption to prevent unauthorized access, disclosure, or modification.

Database administration audit logs, historical security incidents, or penetration test results are also useful information for database security, but they are not the most useful for prioritizing database selection for encryption. Database administration audit logs are records of activities performed by database administrators or other privileged users on the database system. Database administration audit logs help to monitor and verify the actions and changes made by authorized users and detect any anomalies or violations. Historical security incidents are records of events that have compromised or threatened the security of the database system in the past. Historical security incidents help to identify and analyze the root causes, impacts, and lessons learned from previous breaches or attacks. Penetration test results are reports of simulated attacks performed by ethical hackers or security experts on the database system to evaluate its vulnerabilities and defenses. Penetration test results help to discover and exploit any weaknesses or gaps in the database security posture and recommend remediation actions.

References: Data Classification Policy - SANS Institute, Database Security Best Practices - Oracle, [Database Security: An Essential Guide | IBM]

The greatest benefit of adopting data minimization practices is that the associated threat surface is reduced. Data minimization is a privacy principle that states that personal data should be adequate, relevant, and limited to what is necessary for the purposes for which they are processed. Data minimization helps to protect data privacy by reducing the amount and type of personal data that are collected, stored, processed, or shared by an organization. This in turn reduces the exposure of personal data to potential threats, such as unauthorized access, use, disclosure, modification, or loss. References: : CDPSE Review Manual (Digital Version), page 29

Critical data elements are the data elements that are essential for the organization to achieve its business objectives, comply with legal and regulatory requirements, and protect the privacy and security of the data subjects. Critical data elements should be mapped to the data process flow, which is a graphical representation of how data is collected, processed, stored, shared, and disposed of within the organization. Mapping critical data elements to the data process flow helps to identify the sources, destinations, transformations, and dependencies of the data, as well as the potential risks and controls associated with each step of the data lifecycle.

References: CDPSE Review Manual, 2021, p. 83

The vulnerability that would have the greatest impact on the privacy of information is private key exposure, because it would compromise the encryption and decryption of the information, as well as the authentication and integrity of the communicating parties. A private key is a secret and unique value that is used to encrypt or decrypt data, or to sign or verify digital signatures. If an attacker gains access to the private key, they can read, modify, or impersonate the data or the sender, which would violate the confidentiality, integrity, and authenticity of the information12.

References:

• CDPSE Review Manual, Chapter 2 – Privacy Architecture, Section 2.3 – Privacy Architecture Implementation3.
• CDPSE Certified Data Privacy Solutions Engineer All-in-One Exam Guide, Chapter 2 – Privacy Architecture, Section 2.4 – Remote Access4.

Height, weight, and activities are the most legitimate information to collect for business reasons in this situation, as they are directly related to the purpose and functionality of a wellness smartwatch application that aims to monitor and improve the health and fitness of its users. Collecting height, weight, and activities would also comply with the data minimization principle that requires limiting the collection, storage and processing of personal data to what is necessary and relevant for the intended purposes. The other options are not legitimate information to collect for business reasons in this situation, as they are not related to the purpose and functionality of a wellness smartwatch application and may violate the privacy rights and preferences of its users. Collecting sleep schedule and calorie intake may be useful for some users who want to track their sleep quality and nutrition intake, but they are not essential for a wellness smartwatch application and may require additional consent or justification from the users. Collecting education and profession may be irrelevant for a wellness smartwatch application and may be used for other purposes, such as marketing or profiling, without the consent or knowledge of the users. Collecting race, age, and gender may be sensitive for some users who do not want to disclose their personal characteristics or identity, and may require additional safeguards or measures to protect their privacy1, p. 75-76 References: 1: CDPSE Review Manual (Digital Version)

Cloud vendor agreements are the best way to ensure an organization’s data retention requirements will be met in the public cloud environment because they define the roles, responsibilities and obligations of both parties regarding the collection, storage, processing and disposal of data in the cloud. They also specify the terms and conditions for data protection, security, privacy, compliance and auditability12. Data classification schemes, automated data deletion schedules and service level agreements (SLAs) are useful tools to manage and monitor data retention, but they do not guarantee that the cloud vendor will adhere to the organization’s data retention requirements or that they will be enforceable in case of disputes.

References: 1: CDPSE Review Manual (Digital Version), Domain 1: Privacy Governance, Task 1.7: Participate in the management and evaluation of contracts, service levels and practices of vendors and other external parties 2: CDPSE Certified Data Privacy Solutions Engineer All-in-One Exam Guide, Chapter 2: Privacy Governance, Section: Vendor Management

Anonymization is a technique that removes or modifies personal data in such a way that it can no longer be attributed to a specific data subject. Anonymization can be achieved by various methods, such as encryption, pseudonymization, aggregation, generalization, etc. When using anonymization techniques to prevent unauthorized access to personal data, the most important consideration to ensure the data is adequately protected is that the key must be kept separate and distinct from the data it protects. The key is a piece of information that is used to reverse the anonymization process and restore the original personal data. The key must be stored and managed in a secure location that is different from where the anonymized data is stored and processed. This way, even if the anonymized data is compromised, the key cannot be accessed or used to re-identify the data subjects. References: : CDPSE Review Manual (Digital Version), page 29

A privacy notice is a document that informs data subjects about how their personal data is collected, processed, stored, shared, and protected by an organization. The primary purpose of a privacy notice is to provide transparency to the data subject on the intended use of their personal data, as well as their rights and choices regarding their data. A privacy notice also helps the organization comply with legal and regulatory requirements, such as obtaining consent, demonstrating accountability, and fulfilling the principle of fairness and lawfulness.

References: CDPSE Review Manual, 2021, p. 36

The right not to be profiled is the right of data subjects to not be subject to a decision based solely on automated processing, including profiling, which produces legal or similarly significant effects on them. The online application that delivers a mortgage decision automatically based on criteria set by the lender is an example of such a decision, as it affects the data subject’s ability to obtain a loan.

References:

• What exactly is ‘profiling’ under the GDPR - DMA
• Can I be subject to automated individual decision-making, including profiling - European Commission

Public key infrastructure (PKI) is a system that enables the use of public key cryptography, which is a method of encrypting and authenticating data using a pair of keys: a public key and a private key. Public key cryptography can protect against man-in-the-middle (MITM) attacks, which are attacks where an attacker intercepts and modifies the communication between two parties. PKI makes public key cryptography feasible by providing a way to generate, distribute, verify, and revoke public keys. PKI also uses digital certificates, which are documents that bind a public key to an identity, and certificate authorities, which are trusted entities that issue and validate certificates. By using PKI, the parties can ensure that they are communicating with the intended recipient and that the data has not been tampered with by an attacker.

References:

• What is Public Key Infrastructure (PKI)? - Fortinet
• How is man-in-the-middle attack prevented in TLS? [duplicate]
• A brief look at Man-in-the-Middle Attacks and the Role of Public Key Infrastructure (PKI)

The best answer is D. Mutual certificate authentication.

A comprehensive explanation is:

Mutual certificate authentication is a method of mutual authentication that uses public key certificates to verify the identities of both parties in a two-way communication. A public key certificate is a digital document that contains information about the identity of the certificate holder, such as their name, organization, domain name, etc., as well as their public key, which is used for encryption and digital signature. A public key certificate is issued and signed by a trusted authority, called a certificate authority (CA), that vouches for the validity of the certificate.

Mutual certificate authentication works as follows:

• Both parties have a public key certificate issued by a CA that they trust.
• When they initiate a communication, they exchange their certificates with each other.
• They verify the signatures on the certificates using the CA’s public key, which they already have or can obtain from a trusted source.
• They check that the certificates are not expired, revoked, or tampered with.
• They extract the public keys from the certificates and use them to encrypt and decrypt messages or to generate and verify digital signatures.
• They confirm that the identities in the certificates match their expectations and intentions.

By using mutual certificate authentication, both parties can be confident that they are communicating with the intended and legitimate party, and that their communication is secure and confidential.

Mutual certificate authentication is often used in conjunction with Transport Layer Security (TLS), a protocol that provides encryption and authentication for network communications. TLS supports both one-way and two-way authentication. In one-way authentication, only the server presents a certificate to the client, and the client verifies it. In two-way authentication, also known as mutual TLS or mTLS, both the server and the client present certificates to each other, and they both verify them. Mutual TLS is commonly used for secure web services, such as APIs or webhooks, that require both parties to authenticate each other.

Virtual private network (VPN), Secure Shell (SSH), and Transport Layer Security (TLS) are all technologies that can help to ensure the identities of individuals in a two-way communication are verified, but they are not methods of mutual authentication by themselves. They can use mutual certificate authentication as one of their options, but they can also use other methods, such as username and password, pre-shared keys, or tokens. Therefore, they are not as specific or accurate as mutual certificate authentication.

References:

• What is mutual authentication? | Two-way authentication1
• How to prove and verify someone’s identity2
• Identity verification - Information Security & Policy3

Before using data from the organization’s customer database for a new marketing application, the first step should be to determine what data is required by the application and for what purpose. This will help to ensure that the data collection and processing are relevant, necessary, and proportionate to the intended use, and that the data minimization principle is followed. Data minimization means that only the minimum amount of personal data needed to achieve a specific purpose should be collected and processed, and that any excess or irrelevant data should be deleted or anonymized1. This will also help to comply with the data privacy laws and regulations that apply to the organization, such as the General Data Protection Regulation (GDPR) or the California Consumer Privacy Act (CCPA), which require organizations to inform data subjects about the types and purposes of data processing, and to obtain their consent if needed23.

References:

• ISACA, Data Privacy Audit/Assurance Program, Control Objective 2: Data Minimization, p. 61
• ISACA, GDPR Data Protection Impact Assessments, p. 4-52
• ISACA, CCPA vs. GDPR: Similarities and Differences, p. 1-23

The best time during the secure development life cycle to perform privacy threat modeling is early in the design phase, because this will help identify and mitigate the potential privacy risks and vulnerabilities of the system or application before they become costly or difficult to fix. Privacy threat modeling is a systematic process of analyzing the data flows, assets, actors, and scenarios of a system or application to identify and prioritize the privacy threats and countermeasures12. Performing privacy threat modeling early in the design phase will also help ensure that privacy is built into the system or application from the start, rather than as an afterthought.

References:

• CDPSE Exam Content Outline, Domain 2 – Privacy Architecture (Privacy Architecture Implementation), Task 2: Implement privacy solutions3.
• CDPSE Review Manual, Chapter 2 – Privacy Architecture, Section 2.3 – Privacy Architecture Implementation4.

A data inventory is a comprehensive list of the data that an organization collects, processes, stores, transfers, and disposes of. It includes information such as the type, source, location, owner, purpose, and retention period of the data. A data inventory is essential for understanding where personal data is coming from and how it is used within the organization, as well as for complying with data privacy laws and regulations. A data inventory also helps to identify and mitigate data privacy risks and gaps.

References:

• ISACA, CDPSE Review Manual 2021, Chapter 2: Privacy Governance, Section 2.2: Data Inventory and Data Mapping, p. 40-41.
• ISACA, Data Privacy Audit/Assurance Program, Control Objective 3: Data Inventory and Classification, p. 7-81

The primary consideration to protect against privacy violations when utilizing artificial intelligence (AI) driven business decisions is ensuring proper data sets are used to train the models. AI is a technology that enables machines or systems to perform tasks that normally require human intelligence, such as reasoning, learning, decision making, etc. AI relies on large amounts of data to train its models and algorithms to perform these tasks. However, if the data sets used to train the models are inaccurate, incomplete, biased, or outdated, they can result in privacy violations, such as discrimination, profiling, manipulation, or harm to the data subjects. Therefore, an IT privacy practitioner should ensure that the data sets used to train the models are proper, meaning that they are relevant, representative, reliable, and respectful of the data subjects’ rights and interests. References: : CDPSE Review Manual (Digital Version), page 141

Online behavioral tracking is a tracking technology associated with unsolicited targeted advertisements that presents the greatest privacy risk. Online behavioral tracking is a technique that collects and analyzes personal data about users’ online activities, preferences, interests, and behaviors across different websites or platforms. Online behavioral tracking is used to create user profiles and deliver personalized or targeted advertisements that match users’ needs or wants. Online behavioral tracking poses a privacy risk because it can invade users’ privacy by collecting sensitive or intimate personal data without their knowledge or consent, such as health conditions, political views, sexual orientation, etc. Online behavioral tracking can also expose users to unwanted or inappropriate advertisements that may influence their decisions or actions. References: : CDPSE Review Manual (Digital Version), page 139

From a privacy perspective, it is most important to ensure data backups are encrypted. Encryption is a process of transforming data into an unreadable form using a secret key or algorithm. Encryption can help protect the confidentiality, integrity, and availability of data backups by preventing unauthorized access, disclosure, or modification. Encryption can also help comply with legal and regulatory requirements for data protection, such as the General Data Protection Regulation (GDPR) or the Health Insurance Portability and Accountability Act (HIPAA). Encryption can be applied to data backups at different levels, such as file-level, disk-level, or network-level encryption.

Incremental backups, differential backups, or pseudonymization are also useful for data backup management, but they are not the most important from a privacy perspective. Incremental backups are backups that only copy the data that has changed since the last backup, whether it was a full, differential, or incremental backup. Incremental backups can help save storage space and time, but they do not directly protect the data from unauthorized access or disclosure. Differential backups are backups that only copy the data that has changed since the last full backup. Differential backups can also help save storage space and time, but they also do not directly protect the data from unauthorized access or disclosure. Pseudonymization is a process of replacing identifying information in data with artificial identifiers or pseudonyms. Pseudonymization can help enhance the privacy of data by reducing the linkability between data and data subjects, but it does not prevent re-identification or inference attacks.

References: Data backups 101: A complete guide for 2023 - Norton, Backup & Secure | U.S. Geological Survey - USGS.gov, The GDPR: How the right to be forgotten affects backups

Requiring independent audits of the providers’ data privacy controls is the best way to ensure third-party providers that process an organization’s personal data are addressed as part of the data privacy strategy. Independent audits can verify that the providers are complying with the applicable data privacy laws and regulations, as well as the organization’s own policies and standards. Independent audits can also identify any gaps or weaknesses in the providers’ data privacy controls and recommend corrective actions or improvements.

References:

• What Is Your Privacy and Data Protection Strategy? - ISACA
• Why data privacy and third-party risk teams need to work together - OneTrust

Data flow mapping is a technique to document how personal data flows within and outside an organization, including the sources, destinations, formats, purposes and legal bases of the data processing activities. Data flow mapping helps organizations to assess privacy risks, such as data breaches, unauthorized access, misuse or loss of data, and to implement appropriate controls to mitigate those risks. Data flow mapping may also help organizations to evaluate the effectiveness of data controls, determine data integration gaps and comply with regulations, but those are not the primary reasons for data flow mapping1, p. 69-70 References: 1: CDPSE Review Manual (Digital Version)

Awareness campaigns are initiatives that aim to educate and inform employees about the importance of privacy protection, the organization’s privacy policies and procedures, the applicable laws and regulations, and the best practices and behaviors to safeguard personal data. Awareness campaigns can support an organization’s efforts to create and maintain desired privacy protection practices among employees by raising their awareness, understanding and commitment to privacy, as well as by influencing their attitudes, values and culture. Awareness campaigns can use various methods and channels, such as posters, newsletters, videos, webinars, quizzes, games or events, to deliver consistent and engaging messages to the target audience. The other options are not the best ways to support an organization’s efforts to create and maintain desired privacy protection practices among employees. Skills training programs are focused on developing specific technical or functional skills related to privacy, but they may not address the broader aspects of privacy awareness or culture. Performance evaluations are focused on measuring and rewarding individual or team performance based on predefined criteria or objectives, but they may not reflect the actual level of privacy awareness or practice. Code of conduct principles are focused on establishing and enforcing ethical standards and rules of behavior for employees, but they may not be sufficient to create or maintain privacy awareness or practice without effective communication and education1, p. 103-104 References: 1: CDPSE Review Manual (Digital Version)

A privacy impact assessment (PIA) is a systematic process to identify and evaluate the potential privacy impacts of a system, project, program or initiative that involves the collection, use, disclosure or retention of personal data. A PIA should be done first to address privacy risk when migrating customer relationship management (CRM) data to a new system, as it would help to ensure that privacy risks are identified and mitigated before the migration is executed. A PIA would also help to ensure compliance with privacy principles, laws and regulations, and alignment with customer expectations and preferences. The other options are not as important as performing a PIA when addressing privacy risk when migrating CRM data to a new system. Developing a data migration plan is a process of defining and documenting the objectives, scope, approach, methods and steps for transferring data from one system to another, but it does not necessarily address privacy risk or impact. Conducting a legitimate interest analysis (LIA) is a process of assessing whether there is a legitimate interest for processing personal data that outweighs the rights and interests of the data subjects, but it is only applicable in certain jurisdictions and situations where legitimate interest is a valid legal basis for processing. Obtaining consent from data subjects is a process of obtaining their permission or agreement before collecting, using, disclosing or transferring their personal data for specific purposes, but it may not be required or sufficient for migrating CRM data to a new system, depending on the context and nature of the migration and the applicable laws and regulations1, p. 67 References: 1: CDPSE Review Manual (Digital Version)

The answer is B. Personal data could potentially be exfiltrated through the virtual workspace.

A comprehensive explanation is:

A virtualized workspace is a cloud-based service that provides remote access to a desktop environment, applications, and data. A virtualized workspace can enable software development teams to collaborate and work efficiently across different locations and devices. However, a virtualized workspace also poses significant privacy risks, especially when it is implemented by a third-party provider.

One of the greatest privacy concerns of using a third-party virtualized workspace is the potential for personal data to be exfiltrated through the virtual workspace. Personal data is any information that relates to an identified or identifiable individual, such as name, email, address, phone number, etc. Personal data can be collected, stored, processed, or transmitted by the software development organization or its clients, partners, or users. Personal data can also be generated or inferred by the software development activities or products.

Personal data can be exfiltrated through the virtual workspace by various means, such as:

• Data breaches: A data breach is an unauthorized or unlawful access to or disclosure of personal data. A data breach can occur due to weak security measures, misconfiguration errors, human errors, malicious attacks, or insider threats. A data breach can expose personal data to hackers, competitors, regulators, or other parties who may use it for harmful purposes.
• Data leakage: Data leakage is an unintentional or accidental transfer of personal data outside the intended boundaries of the organization or the virtual workspace. Data leakage can occur due to improper disposal of devices or media, insecure network connections, unencrypted data transfers, unauthorized file sharing, or careless user behavior. Data leakage can compromise personal data to third parties who may not have adequate privacy policies or practices.
• Data mining: Data mining is the analysis of large and complex data sets to discover patterns, trends, or insights. Data mining can be performed by the third-party provider of the virtual workspace or by other authorized or unauthorized parties who have access to the virtual workspace. Data mining can reveal personal data that was not explicitly provided or intended by the organization or the individuals.

The exfiltration of personal data through the virtual workspace can have serious consequences for the software development organization and its stakeholders. It can result in:

• Legal liability: The organization may face legal actions or penalties for violating the privacy laws, regulations, standards, or contracts that apply to the personal data in each jurisdiction where it operates or serves. For example, the General Data Protection Regulation (GDPR) in the European Union imposes strict obligations and sanctions for protecting personal data across borders.
• Reputational damage: The organization may lose trust and credibility among its clients, partners, users, employees, investors, or regulators for failing to safeguard personal data. This can affect its brand image, customer loyalty, market share, revenue, or growth potential.
• Competitive disadvantage: The organization may lose its competitive edge or intellectual property if its personal data is stolen or misused by its rivals or adversaries. This can affect its innovation capability, product quality, or market differentiation.

Therefore, it is essential for the software development organization to implement appropriate measures and controls to prevent or mitigate the exfiltration of personal data through the virtual workspace. Some of these measures and controls are:

• Data minimization: The organization should collect and process only the minimum amount and type of personal data that is necessary and relevant for its legitimate purposes. It should also delete or anonymize personal data when it is no longer needed or required.
• Data encryption: The organization should encrypt personal data at rest and in transit using strong and standardized algorithms and keys. It should also ensure that only authorized parties have access to the keys and that they are stored securely.
• Data segmentation: The organization should segregate personal data into different categories based on their sensitivity and risk level. It should also apply different levels of protection and access control to each category of personal data.
• Data governance: The organization should establish a clear and comprehensive policy and framework for managing personal data throughout its lifecycle. It should also assign roles and responsibilities for implementing and enforcing the policy and framework.
• Data audit: The organization should monitor and review the activities and events related to personal data on a regular basis. It should also conduct periodic assessments and tests to evaluate the effectiveness and compliance of its privacy measures and controls.
• Data awareness: The organization should educate and train its staff and users on the importance and best practices of protecting personal data. It should also communicate and inform its clients, partners, and regulators about its privacy policies and practices.

The other options are not as great of a concern as option B.

The third-party workspace being hosted in a highly regulated jurisdiction (A) may pose some challenges for complying with different privacy laws and regulations across borders. However it may also offer some benefits such as higher standards of privacy protection and enforcement.

The organization’s products being classified as intellectual property © may increase the value and attractiveness of the personal data related to the products, but it does not necessarily increase the risk of exfiltration of the personal data through the virtual workspace.

The lack of privacy awareness and training among remote personnel (D) may increase the likelihood of human errors or negligence that could lead to exfiltration of personal data through the virtual workspace. However it is not a direct cause or source of exfiltration, and it can be addressed by providing adequate education and training.

References:

• 8 Risks of Virtualization: Virtualization Security Issues1
• Security & Privacy Risks of the Hybrid Work Environment2
• The Risk of Virtualization - Concerns and Controls3
• What is Virtualized Security?4

Sectoral is a privacy protection reference model that refers to a system of laws and regulations that apply to specific sectors or industries within a jurisdiction, such as health, finance, education or telecommunications. Sectoral privacy protection is typically characterized by having different rules and standards for different types of personal data or data processing activities, depending on the sensitivity and value of the data or the impact and risk of the processing. When a government’s health division established the complete privacy regulation for only the health market, it is using a sectoral privacy protection reference model, as it is addressing the specific needs and challenges of the health sector in terms of privacy protection. The other options are not applicable in this scenario. Co-regulatory is a privacy protection reference model that refers to a system of laws and regulations that are supplemented by self-regulation mechanisms, such as codes of conduct, standards or certification schemes, developed by industry associations or professional bodies with oversight from government agencies or regulators. Comprehensive is a privacy protection reference model that refers to a system of laws and regulations that apply to all sectors and industries within a jurisdiction, regardless of the type or nature of personal data or data processing activities. Self-regulatory is a privacy protection reference model that refers to a system of laws and regulations that rely on voluntary compliance by organizations with their own policies and procedures, without any external oversight or enforcement from government agencies or regulators1, p. 63-64 References: 1: CDPSE Review Manual (Digital Version)

The value proposition of a PIA is not understood by management is the greatest obstacle to conducting a PIA, as it may result in lack of support, funding, resources or commitment for the PIA process and outcomes. Management may not appreciate or recognize the benefits of a PIA, such as enhancing privacy protection, reducing privacy risks and costs, increasing customer trust and satisfaction, and complying with privacy laws and regulations. Management may also perceive a PIA as a burden, a delay or a hindrance to the system or project development and delivery. The other options are not as significant as the value proposition of a PIA is not understood by management as obstacles to conducting a PIA. Conducting a PIA requires significant funding and resources is an obstacle to conducting a PIA, but it may be overcome by demonstrating the return on investment or the cost-benefit analysis of a PIA. PIAs need to be performed many times in a year is an obstacle to conducting a PIA, but it may be mitigated by adopting a scalable or modular approach to PIAs that can be tailored to different types or levels of systems or projects. The organization lacks knowledge of PIA methodology is an obstacle to conducting a PIA, but it may be resolved by acquiring or developing the necessary skills, tools or guidance for performing PIAs1, p. 67-68 References: 1: CDPSE Review Manual (Digital Version)

The best way to address privacy concerns when an organization captures personal data from a third party through an open application programming interface (API) is to obtain consent from the data subjects. Consent is a freely given, specific, informed, and unambiguous indication of the data subject’s wishes by which they agree to the processing of their personal data by the organization for a defined purpose. Consent is one of the legal bases for processing personal data under various privacy laws and regulations such as the General Data Protection Regulation (GDPR) or the California Consumer Privacy Act (CCPA). Obtaining consent from the data subjects can help ensure that they are aware of and agree to the collection and use of their personal data by the organization through the open API. Obtaining consent can also help respect the data subject’s rights and preferences regarding their personal data.

Developing a service level agreement (SLA) with the third party, implementing encryption for the data transmission, or reviewing the specification document of the open API are also good practices for addressing privacy concerns when using an open API to capture personal data from a third party, but they are not the best way. Developing an SLA with the third party can help define the roles, responsibilities, expectations, and obligations of both parties regarding the provision and use of the open API and the personal data involved. Implementing encryption for the data transmission can help protect the confidentiality, integrity, and availability of the personal data transferred between the third party and the organization through the open API. Reviewing the specification document of the open API can help understand the functionality, features, parameters, or requirements of the open API and how it handles personal data.

References: Open APIs and Security Risks | Govenda Board Portal Software, The top API security risks and how to mitigate them - Appinventiv, Critical API security risks: 10 best practices | TechBeacon

The best way to ensure data confidentiality across databases is to use data anonymization, which is a process of removing or modifying personal or sensitive data from a dataset so that it cannot be linked or attributed to a specific individual or entity. Data anonymization helps protect the privacy and security of the data subjects, as well as comply with the applicable data protection laws and regulations. Data anonymization can be achieved by using various techniques, such as masking, encryption, aggregation, generalization, perturbation, or synthetic data generation12.

References:

• CDPSE Review Manual, Chapter 3 – Data Lifecycle, Section 3.3 – Data Anonymization3.
• CDPSE Certified Data Privacy Solutions Engineer All-in-One Exam Guide, Chapter 3 – Data Lifecycle, Section 3.4 – Data Anonymization4.

The best way to ensure an effective data privacy policy is implemented is to align regulatory requirements with business needs, because this will help achieve compliance while also supporting the organization’s objectives, values, and strategies. A data privacy policy should reflect the legal obligations and expectations of the organization, as well as the needs and preferences of its stakeholders, such as customers, employees, partners, and regulators. A data privacy policy should also be flexible and adaptable to changing circumstances and environments12.

References:

• CDPSE Exam Content Outline, Domain 1 – Privacy Governance (Governance, Management & Risk Management), Task 3: Participate in the evaluation of privacy policies, programs and policies for their alignment with legal requirements, regulatory requirements and/or industry best practices3.
• CDPSE Review Manual, Chapter 1 – Privacy Governance, Section 1.2 – Privacy Policy4.

In GDPR parlance, organizations that use third-party service providers are often, but not always, considered data controllers, which are entities that determine the purposes and means of the processing of personal data, which can include directing third parties to process personal data on their behalf. The third parties that process data for data controllers are known as data processors.

The best way to protect personal data in the custody of a third party is to include requirements to comply with the organization’s privacy policies in the contract. This means that the organization should specify the terms and conditions of data processing, such as the purpose, scope, duration, and security measures, and ensure that they are consistent with the organization’s privacy policies and applicable privacy regulations. The contract should also define the roles and responsibilities of both parties, such as data controller and data processor, and establish mechanisms for monitoring, reporting, auditing, and resolving any issues or incidents related to data privacy. References: : CDPSE Review Manual (Digital Version), page 41

The most important consideration when writing an organization’s privacy policy is to align the statements to the organizational practices, because this will help ensure that the policy is accurate, consistent, and transparent. A privacy policy is a document that explains how the organization collects, uses, discloses, and protects personal data from its customers, employees, partners, and other stakeholders. A privacy policy should reflect the actual data processing activities and privacy measures of the organization, as well as comply with the applicable laws and regulations. A privacy policy that is not aligned with the organizational practices may lead to confusion, mistrust, or legal liability12.

References:

• CDPSE Review Manual, Chapter 1 – Privacy Governance, Section 1.2 – Privacy Policy3.
• CDPSE Certified Data Privacy Solutions Engineer All-in-One Exam Guide, Chapter 1 – Privacy Governance, Section 1.2 – Data Privacy Laws and Regulations4.

Prioritizing privacy-related risk scenarios as part of ERM processes is the best way to ensure that the risk responses meet the organizational objectives, because it helps to align the privacy risk management with the overall strategic goals, values, and culture of the organization. ERM is a holistic approach to identify, assess, and manage risks across the organization, taking into account the interdependencies and trade-offs among different types of risks. By integrating privacy-related risk scenarios into the ERM processes, the organization can evaluate the potential impact and likelihood of privacy risks on its mission, vision, and performance, and prioritize the most significant ones for mitigation or acceptance. This can also help to allocate appropriate resources, assign clear roles and responsibilities, and monitor and report on the effectiveness of the risk responses.

References:

• Privacy Risk Management, ISACA Journal
• Enterprise Risk Assessment, Deloitte

One of the foundational goals of data privacy laws is to give people rights over the collection of personal data, such as the right to access, correct, delete, or object to the processing of their data. Privacy laws also aim to protect people’s dignity, autonomy, and self-determination in relation to their personal data. The other options are not accurate or complete descriptions of the purpose of data privacy laws.

References:

• CDPSE Review Manual, Chapter 1 – Privacy Governance, Section 1.1 – Privacy Principles1.
• CDPSE Certified Data Privacy Solutions Engineer All-in-One Exam Guide, Chapter 1 – Privacy Governance, Section 1.2 – Data Privacy Laws and Regulations2.

Remote employee monitoring software is a solution that collects, analyzes and reports data on the activities and behaviors of employees who work remotely or from home. It can help organizations to measure and improve employee productivity, performance, engagement and security. However, it also poses significant privacy risks and challenges, as it may involve the collection and processing of personal data, such as names, email addresses, biometric data, IP addresses, keystrokes, screenshots, web browsing history, app usage, communication content and frequency, etc.

Data access should be restricted based on roles, meaning that only authorized and legitimate parties should be able to access and use the data collected by the remote employee monitoring software, based on their roles and responsibilities within the organization. This is a key privacy principle and practice that helps to protect the privacy rights and interests of the employees, and to prevent unauthorized or excessive access, use, disclosure or modification of their personal data by the organization or third parties. Data access restriction based on roles also helps to comply with data protection laws and regulations, such as the General Data Protection Regulation (GDPR) or the California Consumer Privacy Act (CCPA), which require data controllers and processors to implement appropriate technical and organizational measures to safeguard personal data.

References:

• Mobile Workforce Security Considerations and Privacy - ISACA, section 3: “The principle of least privilege should be applied to ensure that only authorized personnel have access to the data.”
• Why Employee Privacy Matters More Than Ever - ISACA, section 3: “Privacy-first monitoring should include granular privacy controls, including: Auto-redacting personal information; Restricting access to sensitive information based on role; Masking sensitive information from view.”

The first thing that an IT privacy practitioner should do before an organization migrates personal data from an on-premise solution to a cloud-hosted solution is to perform a privacy impact assessment (PIA). A PIA is a systematic process of identifying and evaluating the potential privacy risks and impacts of a data processing activity or system. A PIA helps to ensure that privacy is considered and integrated into the design and development of data processing activities or systems, and that privacy risks are mitigated or eliminated. A PIA also helps to determine the appropriate measures to protect personal data in a cloud-hosted solution, such as encryption, pseudonymization, anonymization, access control, audit trail, breach notification, etc. A PIA also helps to comply with the applicable privacy regulations and standards that govern data processing activities in a cloud-hosted solution. References: : CDPSE Review Manual (Digital Version), page 99

A mobile application implementation should meet the organization’s data security standards by ensuring that the application does not contain any vulnerabilities, errors or malicious code that could compromise the confidentiality, integrity or availability of the data. An automatic dynamic code scan is a technique that analyzes the application code while it is running to detect and report any security issues or defects. An automatic dynamic code scan can help to identify and fix any potential data security risks before the application is deployed. The other options are not sufficient to ensure data security standards. UAT is a process of verifying that the application meets the user requirements and expectations, but it does not necessarily test for data security. Data classification is a process of categorizing data according to its sensitivity and value, but it does not ensure that the data is protected by the application. A PIA is a process of identifying and evaluating the privacy impacts of a system or project that involves personal data, but it does not ensure that the system or project meets data security standards. , p. 89-90 References: : CDPSE Review Manual (Digital Version)

A raw zone is a zone within a data lake that contains unprocessed or unstructured data that is ingested from various sources without any transformation or validation. A raw zone may contain sensitive data that has not been identified or classified yet, such as personal data. Therefore, sensitive data in a raw zone should be encrypted or tokenized to protect its confidentiality and integrity. Encryption is a process of transforming data into an unreadable form using a secret key or algorithm. Tokenization is a process of replacing sensitive data with non-sensitive substitutes called tokens. Both encryption and tokenization help to prevent unauthorized or unlawful access, use, disclosure, or transfer of sensitive data in a raw zone. References: : CDPSE Review Manual (Digital Version), page 169

The practice that best indicates an organization follows the data minimization principle is that data is regularly reviewed for its relevance. The data minimization principle is one of the core principles of data protection under various laws and regulations, such as the GDPR or the CCPA. It states that personal data should be adequate, relevant, and limited to what is necessary in relation to the purposes for which they are processed. By regularly reviewing the data they hold, organizations can ensure that they do not collect or retain excessive or unnecessary data that may pose privacy risks or violate data subject rights.

Data is pseudonymized when being backed up, data is encrypted before storage, or data is only accessible on a need-to-know basis are also good practices for data protection, but they do not directly indicate that the organization follows the data minimization principle. Pseudonymization is a process of replacing identifying information in data with artificial identifiers or pseudonyms. Pseudonymization can help enhance the privacy of data by reducing the linkability between data and data subjects, but it does not prevent re-identification or inference attacks. Encryption is a process of transforming data into an unreadable form using a secret key or algorithm. Encryption can help protect the confidentiality, integrity, and availability of data by preventing unauthorized access, disclosure, or modification. Access control is a process of restricting who can access, modify, or delete data based on their roles, permissions, or credentials. Access control can help prevent unauthorized or inappropriate use of data by limiting the scope of access.

References: Data Minimization | Washington Technology Solutions, What Is Data Minimization? The Principles According to GDPR | 2BAdvice, Data Protection Principles: Core Principles of the GDPR - Cloudian

Ensuring appropriate data classification should be done next after a migration of personal data involving a data source with outdated documentation has been approved by senior management, as it helps to identify the types, locations, and owners of the data, and to apply the appropriate privacy controls and measures based on the data classification level. Data classification also facilitates the data discovery, data minimization, data retention, and data disposal processes15. References: 1 Domain 3, Task 2; 5 Page 9

System hardening is a process of applying security measures and configurations to a system to reduce its attack surface and enhance its resistance to threats. System hardening can include disabling unnecessary services, removing default accounts, applying patches and updates, enforcing strong passwords and encryption, and implementing firewalls and antivirus software. The primary benefit of system hardening is that it increases system resiliency, which is the ability of a system to withstand or recover from adverse events that could affect its functionality or performance. The other options are not the primary benefits of system hardening, although they may be secondary benefits or outcomes. System hardening does not necessarily reduce external threats to data, as threats can originate from various sources and vectors. System hardening may reduce exposure of data, but only if the data is stored or processed by the system. System hardening does not eliminate attack motivation for data, as attackers may have different motives and incentives for targeting data. , p. 91-92 References: : CDPSE Review Manual (Digital Version)

The first consideration when selecting a data sanitization method is the type of storage device that holds the data to be sanitized. Different types of storage devices have different characteristics and limitations that affect the effectiveness and feasibility of data sanitization methods. For example, magnetic media, such as hard disk drives (HDDs), can be sanitized by data degaussing, which is wiping data permanently by weakening the magnetic field1. However, data degaussing is not applicable to devices that use solid state drive (SSD) technology, since SSDs do not store data magnetically2. Therefore, the storage type determines which data sanitization methods are suitable and available for the data disposal process.

References:

• ISACA, Why (and How to) Dispose of Digital Data, Data Degaussing1
• ISACA, Best Practices for Data Hygiene, Data Hygiene Practices3
• TechReset, Data Sanitization and Methods, Cryptographic Erasure2
• Imperva, What is Data Sanitization?4