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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Attacks, Threats, and Vulnerabilities | 24% | - Explain threat actor types and attributes - Explain penetration testing concepts - Explain vulnerability scanning concepts - Given a scenario, analyze potential indicators associated with network attacks - Given a scenario, analyze potential indicators associated with application attacks - Given a scenario, analyze potential indicators to determine the type of attack - Compare and contrast types of social engineering attacks |
| Topic 2: Architecture and Design | 21% | - Summarize basics of cryptographic concepts - Given a scenario, implement cybersecurity resilience - Explain the importance of physical security controls - Explain secure application development, deployment, and automation concepts - Explain the importance of security concepts in an enterprise environment - Explain the security implications of embedded and specialized systems - Summarize authentication and authorization design concepts - Summarize virtualization and cloud security concepts |
| Topic 3: Governance, Risk, and Compliance | 14% | - Explain privacy and sensitive data concepts in relation to security - Compare and contrast various types of security controls - Given a scenario, follow organizational security policies and procedures - Summarize regulations, standards, and frameworks that impact organizations - Explain risk management processes and concepts |
| Topic 4: Implementation | 25% | - Given a scenario, implement secure systems design - Given a scenario, implement identity and account management controls - Given a scenario, implement secure host settings - Given a scenario, implement authentication and authorization solutions - Given a scenario, apply cybersecurity solutions to the cloud - Given a scenario, implement secure mobile device policies - Given a scenario, implement secure network architecture concepts - Given a scenario, implement public key infrastructure (PKI) |
| Topic 5: Operations and Incident Response | 16% | - Given a scenario, use data sources to support an investigation - Given a scenario, use appropriate tool to assess organizational security - Given a scenario, apply mitigation techniques or controls to secure an environment - Summarize the importance of policies, processes, and procedures for incident response - Explain key aspects of digital forensics |
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NEW QUESTION # 23
A security alert triggers an agentic system. An analyst notices the following payload in the logs. The alert includes multiple shell commands that are not typically run as part of any hardening:
Which of the following is the most effective control to implement?
Answer: C
Explanation:
Basic Concept: Agentic AI systems that execute shell commands based on model-generated output are vulnerable to prompt injection attacks where malicious actors craft inputs that cause the agent to run unauthorized commands. Input validation using allowlists is a critical defense mechanism. CompTIA SecAI+ Study Guide covers agentic AI security controls.
Why A is Correct: Adding logic that validates shell commands against an approved allowlist before execution is the most direct and effective defense. This ensures only pre-approved, safe commands can be executed regardless of what the agentic system ' s model generates, preventing malicious command injection from reaching the operating system. This principle of allowlist-based input validation is a foundational secure agentic AI control.
Why B is Wrong: Deprecating and retraining the model is a lengthy process that addresses root cause training issues but does not provide immediate protection against ongoing injection attacks in the current deployed system.
Why C is Wrong: Modifying the application to ignore a specific tag merely removes one attack surface while leaving the system vulnerable to other injection vectors. It is not a comprehensive defense.
Why D is Wrong: Using only approved libraries controls which code libraries the agentic system can call, but does not validate or restrict the shell commands generated by the model at runtime based on arbitrary user input.
NEW QUESTION # 24
A recent release of an AI software update exposes confidential customer information due to storage misconfiguration.
Which of the following data security controls will help maintain confidentiality despite the data leak?
Answer: C
Explanation:
Basic Concept: When a storage misconfiguration leads to data exposure, the question is which encryption type would have protected the confidentiality of data stored in that misconfigured storage. The three states of data
- at rest, in transit, and in use - each require different encryption mechanisms. CompTIA SecAI+ Study Guide covers encryption states and their applicability to AI data protection.
Why D is Correct: Encryption at rest protects data stored in databases, file systems, and storage media by encrypting it so that even if unauthorized parties gain access to the storage through a misconfiguration, the data remains unreadable without the decryption key. Since the exposure resulted from a storage misconfiguration that allowed access to stored data, encryption at rest would have maintained confidentiality of the customer information despite the misconfiguration granting storage access.
Why A is Wrong: Model encryption specifically protects AI model weights and parameters from unauthorized access. It does not protect customer data stored in databases or data stores associated with the AI system.
Why B is Wrong: Encryption in transit protects data moving between components over networks. It does not protect data stored at rest in misconfigured storage that is accessed directly rather than over a network connection.
Why C is Wrong: Encryption in use (homomorphic encryption or confidential computing) protects data while it is being actively processed in memory. It addresses runtime processing security, not the confidentiality of data stored in misconfigured storage that is not currently being processed.
NEW QUESTION # 25
Which of the following requires developers to harden infrastructure to protect AI systems?
Answer: B
Explanation:
Basic Concept: Infrastructure hardening for AI systems involves applying security baseline settings and eliminating unnecessary attack surfaces. CompTIA SecAI+ Exam Objectives identify configuration standards as the specific governance instrument that mandates infrastructure hardening requirements for AI deployments.
Why D is Correct: Configuration standards are formal, technical documents specifying exact security settings, baseline configurations, and hardening requirements that developers and administrators must implement to protect systems including AI infrastructure. They establish enforceable rules such as disabling unnecessary services, applying least-privilege access, and enforcing secure communication protocols specifically for AI systems.
Why A is Wrong: Intake processes govern how new projects, systems, or requests are evaluated and onboarded into an organization. They are procedural checkpoints for initial assessment, not technical hardening directives for developers.
Why B is Wrong: Acceptable use policies define appropriate ways employees and users may use organizational systems and AI tools. They are behavioral guidelines aimed at end users, not technical requirements instructing developers to secure infrastructure.
Why C is Wrong: Development guidelines provide best practices and recommendations for software development and may include security considerations. However, they are advisory in nature and broader in scope than the specific mandatory infrastructure-hardening requirements found in configuration standards.
NEW QUESTION # 26
Which of the following provides guidance on AI-specific compliance?
Answer: D
Explanation:
Basic Concept: Different regulatory and standards bodies address different aspects of technology governance.
For AI-specific compliance guidance that addresses the unique characteristics of AI systems including transparency, fairness, accountability, and societal impact, a framework specifically designed for AI is required. CompTIA SecAI+ Study Guide identifies OECD as a key source of AI-specific compliance guidance.
Why A is Correct: The OECD AI Principles and Recommendation on AI provide internationally recognized, AI-specific guidance on compliance with responsible AI values including transparency, accountability, robustness, security, safety, and human-centric values. The OECD has developed a dedicated framework specifically addressing the compliance considerations unique to AI systems across sectors and national boundaries, making it the most AI-specific compliance guidance option listed.
Why B is Wrong: ISO 27001 is a general information security management standard addressing broad organizational security controls. It is not AI-specific and does not address the unique compliance considerations of AI transparency, fairness, or algorithmic accountability.
Why C is Wrong: PCI DSS is a payment card industry security standard focused on protecting payment card data. It has no AI-specific compliance provisions and is limited to financial transaction security requirements.
Why D is Wrong: GDPR is a European data protection regulation focused on personal data privacy rights and obligations. While relevant to AI systems that process personal data, GDPR is a privacy regulation rather than AI-specific compliance guidance addressing the full spectrum of AI governance considerations.
NEW QUESTION # 27
A financial services company is being sued for using AI in the loan underwriting process. The lawsuit reveals that the AI model used biased sources to disproportionately deny loans to residents in certain postal codes.
Which of the following employees is the most qualified to remove these biased sources from the model?
Answer: D
Explanation:
The data scientist is the most appropriate role because correcting biased source data requires examining the datasets used to develop, train, validate, or refine the model and then modifying the data preparation or model- development process. Data scientists work directly with training data, feature selection, statistical distributions, model development, and evaluation, which places them in the strongest position to identify biased inputs and retrain or adjust the model using more representative information. CompTIA SecAI+ includes the data scientist as a formal AI governance role and separately identifies fairness, introduction of bias, accuracy, and model performance as key responsible-AI risks. A platform engineer primarily manages the infrastructure supporting AI workloads. A cybersecurity analyst focuses on security threats, monitoring, and defensive controls rather than correcting discriminatory training data. An AI architect designs the broader architecture and integration of AI solutions but normally does not perform the detailed dataset preparation and model-training work required here. Because the problem originates in biased data sources, the data scientist is the role closest to the underlying cause.
NEW QUESTION # 28
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