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| Section | Weight | Objectives |
|---|---|---|
| Governance, Risk, and Compliance | 14% | - Summarize regulations, standards, and frameworks that impact organizations - Explain risk management processes and concepts - Given a scenario, follow organizational security policies and procedures - Compare and contrast various types of security controls - Explain privacy and sensitive data concepts in relation to security |
| Attacks, Threats, and Vulnerabilities | 24% | - Given a scenario, analyze potential indicators associated with application attacks - Given a scenario, analyze potential indicators to determine the type of attack - Explain vulnerability scanning concepts - Explain penetration testing concepts - Given a scenario, analyze potential indicators associated with network attacks - Compare and contrast types of social engineering attacks - Explain threat actor types and attributes |
| Architecture and Design | 21% | - Explain the importance of security concepts in an enterprise environment - Summarize virtualization and cloud security concepts - Explain the importance of physical security controls - Explain the security implications of embedded and specialized systems - Explain secure application development, deployment, and automation concepts - Summarize authentication and authorization design concepts - Given a scenario, implement cybersecurity resilience - Summarize basics of cryptographic concepts |
| Implementation | 25% | - Given a scenario, implement secure mobile device policies - Given a scenario, implement secure network architecture concepts - Given a scenario, implement secure host settings - Given a scenario, implement secure systems design - Given a scenario, implement public key infrastructure (PKI) - Given a scenario, implement identity and account management controls - Given a scenario, apply cybersecurity solutions to the cloud - Given a scenario, implement authentication and authorization solutions |
| Operations and Incident Response | 16% | - Explain key aspects of digital forensics - Given a scenario, apply mitigation techniques or controls to secure an environment - Given a scenario, use data sources to support an investigation - Given a scenario, use appropriate tool to assess organizational security - Summarize the importance of policies, processes, and procedures for incident response |
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NEW QUESTION # 158
Which of the following roles best supports the implementation of AI governance, risk, and compliance (GRC)? (Choose two.)
Answer: C,D
Explanation:
Basic Concept: AI GRC implementation requires roles that combine understanding of AI technical capabilities and limitations with security risk assessment, control design, and compliance framework expertise. Identifying which roles naturally contribute to AI GRC is essential for team design. CompTIA SecAI+ Study Guide covers AI governance role responsibilities under Domain 4.
Why B is Correct: Data Scientists possess deep understanding of AI model capabilities, limitations, data requirements, and failure modes. For GRC implementation, their technical expertise is essential for identifying AI-specific risks such as bias, model drift, and data quality issues, assessing compliance implications of model design choices, and evaluating whether AI systems meet governance requirements.
Why D is Correct: Security Architects design comprehensive security frameworks and risk management strategies. For AI GRC, they translate governance requirements into technical controls, design AI security architectures that satisfy compliance obligations, assess the risk posture of AI deployments, and ensure security principles including least privilege, defense-in-depth, and audit logging are built into AI system designs.
Why A is Wrong: Desktop specialists manage user workstation hardware and software. Their role focuses on endpoint management and user support, not on the strategic risk assessment, compliance evaluation, or technical AI governance activities required for AI GRC implementation.
Why C is Wrong: Software developers write application code. While they implement security controls when directed, they typically lack the broad risk management, compliance framework expertise, and security architecture perspective needed to lead AI GRC implementation.
Why E is Wrong: SOC analysts focus on monitoring, detecting, and responding to security incidents in operational environments. Their expertise is in reactive security operations rather than the proactive governance framework design and compliance management that AI GRC requires.
Why F is Wrong: Network engineers design and maintain network infrastructure. Their expertise is in network connectivity and protocols, not in AI system governance, risk assessment frameworks, or compliance requirements.
NEW QUESTION # 159
An employee wants a consulting company to procure a data set that contains age, ethnicity, and diabetes status. During development, the employer wants to ensure the integrity of the data.
Which of the following is the best strategy to accomplish this task?
Answer: C
Explanation:
Basic Concept: Data integrity ensures that data has not been tampered with, corrupted, or modified during storage or transmission. For AI training data that is procured from external sources, cryptographic integrity verification is essential to confirm the data arrived unmodified. CompTIA SecAI+ Study Guide covers data integrity controls for AI data pipelines.
Why A is Correct: Implementing checksums provides cryptographic verification of data integrity. A checksum or hash value such as SHA-256 is computed from the dataset at the source. The receiver computes the same hash and compares it to the provided value. Any modification to the data during transit or storage will produce a different hash, immediately detecting tampering or corruption. This is the most reliable, automated, and scalable strategy for ensuring the integrity of procured training data.
Why B is Wrong: Human evaluation can verify data quality and relevance but is impractical for verifying integrity across large datasets of medical records. Human reviewers cannot detect subtle bit-level corruption or intentional small modifications, and the process is not scalable.
Why C is Wrong: Querying the model tests model performance rather than verifying the integrity of the underlying training data. The model cannot tell you whether its training data was modified after collection or during procurement.
Why D is Wrong: Log monitoring tracks system activities and events over time. While useful for auditing access to data, it cannot retroactively confirm that data content has not been modified and does not provide cryptographic integrity guarantees.
NEW QUESTION # 160
An organization develops a chatbot that does not provide harmful or explicit responses, must use clean and professional language, and ensures that responses are accurate.
Which of the following should the organization conduct after the chatbot is fully developed but before a customer-facing deployment?
Answer: C
Explanation:
Basic Concept: Before deploying an AI chatbot that has specific behavioral requirements - no harmful content, professional language, and accurate responses - organizations must verify that the controls designed to enforce these requirements actually work as intended. This pre-deployment verification is essential for customer-facing systems. CompTIA SecAI+ Study Guide covers guardrail testing as a required pre- deployment activity.
Why C is Correct: Guardrail testing and validation specifically verifies that the content filtering, safety controls, and behavioral constraints implemented in the chatbot function correctly before customer exposure.
This involves systematically testing with edge cases, adversarial prompts, and boundary conditions to confirm that harmful content is blocked, language remains professional, and responses are accurate. This directly validates the three requirements stated in the question.
Why A is Wrong: Data labeling and classification is a data preparation activity performed during model training and development. By the time the chatbot is fully developed, this work should already be complete.
Why B is Wrong: Model auditing and evaluation assesses overall model performance, accuracy, and compliance at a broader level. While important, it does not specifically verify that the guardrails enforcing the three behavioral requirements work correctly for the specific failure modes customers might trigger.
Why D is Wrong: Regression modeling and minimization refers to statistical techniques for continuous outcome prediction. This is not a relevant pre-deployment activity for a conversational chatbot requiring behavioral safety validation.
NEW QUESTION # 161
An automobile manufacturer implements a chatbot to assist with configuration options for customer automobiles. Given a customer ' s prompt, the chatbot gives offensive responses.
Which of the following describes this behavior?
Answer: C
Explanation:
Basic Concept: AI chatbots are designed with safety guidelines and content policies that prevent them from generating harmful, offensive, or inappropriate content. When users find ways to bypass these restrictions through crafted prompts, they have " jailbroken " the model. CompTIA SecAI+ Study Guide covers jailbreaking as a key AI vulnerability category.
Why C is Correct: Jailbreaking is the process of using cleverly crafted prompts to bypass an AI model ' s built- in safety restrictions, content policies, and behavioral guardrails, causing it to produce outputs it was designed to refuse. The scenario describes a chatbot that was designed for automobile configuration assistance but is producing offensive responses following customer prompts, indicating that customers have successfully prompted the model to bypass its safety constraints and generate prohibited content.
Why A is Wrong: Model skewing refers to attacks or biases that cause a model to favor certain outputs or perspectives systematically over time, often through data manipulation. It describes a gradual distortion of model behavior, not a direct user-prompted bypass of safety restrictions in a single interaction.
Why B is Wrong: Model theft involves extracting or replicating a proprietary model ' s functionality or architecture through repeated queries. It is an intellectual property attack aimed at stealing the model ' s knowledge, not an attack that causes the model to produce offensive content.
Why D is Wrong: Insecure output handling occurs when an application fails to properly validate or sanitize AI-generated outputs before using them in ways that could cause harm such as passing AI output directly to a system command or database query. It describes a developer implementation vulnerability, not the act of a user prompting a model to bypass its safety constraints.
NEW QUESTION # 162
A security analyst notices that regardless of user-submitted prompts, an AI model always returns unsanitized responses. These responses are then passed to multiple plug-ins. The analyst is concerned with the potential security implications.
Which of the following Open Worldwide Application Security Project (OWASP) categories addresses this vulnerability?
Answer: C
Explanation:
Basic Concept: OWASP has published the Top 10 vulnerabilities for Large Language Model Applications, each addressing a distinct category of LLM security risk. Understanding which OWASP category maps to specific LLM vulnerability scenarios is a key competency in the CompTIA SecAI+ Study Guide under securing AI systems.
Why D is Correct: Improper output handling (OWASP LLM02) occurs when an application passes LLM- generated outputs to downstream systems such as plug-ins, web browsers, or databases without proper validation, sanitization, or encoding. This can enable XSS, SQL injection, remote code execution, or other injection attacks against plug-ins and downstream systems. The scenario exactly matches this: unsanitized AI responses are automatically passed to multiple plug-ins, which could execute malicious content in the model ' s output.
Why A is Wrong: Misinformation refers to the AI generating false or misleading content that users might believe. It is a content accuracy concern related to hallucinations and false information propagation, not a vulnerability describing how model outputs are handled by downstream systems.
Why B is Wrong: Prompt injection involves crafting inputs to manipulate model behavior and override instructions. While it can be a contributing cause of unsafe outputs, the vulnerability described - passing unsanitized outputs to plug-ins - is specifically the output handling failure, not the injection mechanism itself.
Why C is Wrong: Unbounded consumption (OWASP LLM10) refers to resource exhaustion attacks including denial-of-wallet and denial-of-service through excessive token consumption. It addresses resource management vulnerabilities, not the security implications of passing model outputs to downstream systems.
NEW QUESTION # 163
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