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Shared Assessments Certified Third-Party Risk Professional (CTPRP) Sample Questions (Q93-Q98):

NEW QUESTION # 93
The BEST time in the SDLC process for an application service provider to perform Threat Modeling analysis is:

A. After the application vulnerability or penetration test is completed
B. After testing and before the deployment of the final code into production
C. Before the application design and development activities begin
D. Prior to the execution of a contract with each client

Answer: C

Explanation:
Threat modeling is a core element of the Microsoft Security Development Lifecycle (SDL) and a structured approach to identify, quantify, and address the security risks associated with an application12. Threat modeling helps to shape the application's design, meet the security objectives, and reduce risk1. The best time to perform threat modeling analysis is before the application design and development activities begin, as this allows the application service provider to:
* Communicate about the security design of their systems1.
* Analyze the design for potential security issues using a proven methodology1.
* Suggest and manage mitigations for security issues1.
* Incorporate security requirements into the design2.
* Avoid costly rework or redesign later in the SDLC2.
* Identify the most critical and relevant threats to focus on2. References: 1: Microsoft Security Development Lifecycle Threat Modelling1 2: Threat Modeling Process | OWASP Foundation2

NEW QUESTION # 94
Which factor is the LEAST important attribute when classifying personal data?

A. The data subject category that identifies the data owner
B. The assignment of a confidentiality level that differentiates public or non-public information
C. The sensitivity level of specific data elements that could identify an individual
D. The volume of data records processed or retained

Answer: D

Explanation:
According to the GDPR, personal data is any information relating to an identified or identifiable natural person (data subject). The GDPR does not consider the volume of data records as a relevant factor for classifying personal data, but rather the nature and context of the data. The GDPR requires data controllers and processors to apply appropriate technical and organizational measures to ensure a level of security appropriate to the risk of processing personal data, taking into account factors such as the state of the art, the costs of implementation, the nature, scope, context and purposes of processing, and the risks of varying likelihood and severity for the rights and freedoms of natural persons. Therefore, the volume of data records is not a decisive attribute for classifying personal data, but rather an indicator of the potential impact of a data breach or misuse.
The other factors listed in the question are more important attributes for classifying personal data, as they relate to the identification, protection, and rights of the data subjects. The data subject category that identifies the data owner refers to the type of natural person whose personal data is processed, such as customers, employees, patients, students, etc. This factor is important for determining the purpose and legal basis of processing, as well as the data subject's rights and expectations1. The sensitivity level of specific data elements that could identify an individual refers to the degree of harm or discrimination that could result from the disclosure or misuse of such data, such as racial or ethnic origin, political opinions, religious or philosophical beliefs, trade union membership, genetic or biometric data, health data, sex life or sexual orientation, or criminal convictions or offenses2. The GDPR imposes stricter rules and obligations for the processing of such special categories of personal data, as they pose a higher risk to the data subject's fundamental rights and freedoms. The assignment of a confidentiality level that differentiates public or non-public information refers to the degree of access and disclosure that is permitted or required for the personal data, depending on the data subject's consent, the legitimate interests of the data controller or processor, or the applicable laws and regulations1. The GDPR requires data controllers and processors to implement data protection by design and by default, meaning that they should only process the personal data that is necessary for the specific purpose and limit the access to those who need to know.
References:
* 4: 5 Types of Data Classification (With Examples) | Indeed.com
* 7: Special Categories of Personal Data - GDPR EU
* [8]: Data Classification for GDPR Explained [Full Breakdown] - DataGrail

NEW QUESTION # 95
When conducting an assessment of a third party's physical security controls, which of the following represents the innermost layer in a 'Defense in Depth' model?

A. Private internal
B. Restricted entry
C. Public external
D. Public internal

Answer: A

Explanation:
In the 'Defense in Depth' security model, the innermost layer typically focuses on protecting the most sensitive and critical assets, which are often categorized as 'Private internal'. This layer includes security controls and measures that are designed to safeguard the core, confidential aspects of an organization's infrastructure and data. It encompasses controls such as access controls, encryption, and monitoring of sensitive systems and data to prevent unauthorized access and ensure data integrity and confidentiality. The
'Private internal' layer is crucial for maintaining the security of critical information and systems that are essential to the organization's operations and could have the most significant impact if compromised.
Implementing robust security measures at this layer is vital for mitigating risks associated with physical access to critical infrastructure and sensitive information.
References:
* Security frameworks and standards, including NIST SP 800-53 (Security and Privacy Controls for Federal Information Systems and Organizations) and the SANS Institute's guidelines on implementing
'Defense in Depth', provide detailed recommendations on securing the innermost layers of an organization's information systems.
* Publications such as "Physical Security Principles" by ASIS International offer insights into best practices for securing the private internal layer, including access control systems, surveillance, and intrusion detection mechanisms.

NEW QUESTION # 96
Which of the following is a component of evaluating a third party's use of Remote Access within their information security policy?

A. Providing guidelines to configuring ports on a router
B. Identifying the use of multifactor authentication
C. Reviewing the testing and deployment procedures to networking components
D. Maintaining blocked IP address ranges

Answer: B

Explanation:
Remote access is any connection made to an organization's internal network and systems from an external source by a device or host. Remote access can enable greater worker flexibility and productivity, but it also poses significant security risks, such as unauthorized access, data leakage, malware infection, or network compromise. Therefore, it is important to evaluate a third party's use of remote access within their information security policy, which should define the roles, responsibilities, standards, and procedures for remote access.
One of the key components of evaluating a third party's use of remote access within their information security policy is identifying the use of multifactor authentication. Multifactor authentication is a method of verifying the identity of a remote user by requiring two or more factors, such as something the user knows (e.g., password, PIN), something the user has (e.g., token, smart card), or something the user is (e.g., fingerprint, face). Multifactor authentication enhances the security of remote access by making it harder for attackers to impersonate or compromise legitimate users. According to the NIST Guide to Enterprise Telework, Remote Access, and Bring Your Own Device (BYOD) Security1, multifactor authentication should be used for all remote access, especially for high-risk situations, such as accessing sensitive data or privileged accounts.
The other options are not components of evaluating a third party's use of remote access within their information security policy. Maintaining blocked IP address ranges, reviewing the testing and deployment procedures to networking components, and providing guidelines to configuring ports on a router are all examples of network security controls, but they are not specific to remote access. They may be part of the overall information security policy, but they are not sufficient to assess the security of remote access.
References:
* NIST Guide to Enterprise Telework, Remote Access, and Bring Your Own Device (BYOD) Security
* How to Implement an Effective Remote Access Policy
* Why Managing Third-Party Access Requires A Better Approach

NEW QUESTION # 97
Which of the following data types would be classified as low risk data?

A. Non personally identifiable, but sensitive to an organizations significant process
B. Government-issued number, credit card number or bank account information
C. Personally identifiable data but stored in a test environment cloud container
D. Sanitized customer data used for aggregated profiling

Answer: D

Explanation:
Data classification is the process of categorizing data according to its type, sensitivity, and value to the organization if altered, stolen, or destroyed1. Data classification helps an organization understand the risk level of its data and implement appropriate controls to protect it. Data can be classified into three risk levels: low, moderate, and high23. Low risk data are data that are intended for public disclosure or have no adverse impact on the organization's mission, safety, finances, or reputation if compromised23. Sanitized customer data used for aggregated profiling are an example of low risk data, as they do not contain any personally identifiable or sensitive information that could be exploited for criminal or other wrongful purposes. Sanitized data are data that have been modified to remove or obscure any confidential or identifying information, such as names, addresses, phone numbers, etc. Aggregated data are data that have been combined or summarized from multiple sources to provide statistical or analytical insights, such as trends, patterns, averages, etc. Sanitized and aggregated data are often used for research, marketing, or business intelligence purposes, and do not pose a significant threat to the organization or the customers if exposed. References:
* 1: What is Data Classification? | Best Practices & Data Types | Imperva
* 2: Data Classification Guideline (1604 GD.01) - Yale University
* 3: Risk Classifications | University IT
* : Data Classification Policy - Shared Assessments
* : What is Data Sanitization? | Definition and Examples | Imperva
* : What is Data Aggregation? | Definition and Examples | Imperva

NEW QUESTION # 98
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