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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Architecture and Design | 21% | - Given a scenario, implement cybersecurity resilience - Summarize virtualization and cloud security concepts - Summarize authentication and authorization design concepts - Explain the importance of physical security controls - Explain secure application development, deployment, and automation concepts - Summarize basics of cryptographic concepts - Explain the importance of security concepts in an enterprise environment - Explain the security implications of embedded and specialized systems |
| Topic 2: Operations and Incident Response | 16% | - Given a scenario, use data sources to support an investigation - Explain key aspects of digital forensics - Given a scenario, apply mitigation techniques or controls to secure an environment - Summarize the importance of policies, processes, and procedures for incident response - Given a scenario, use appropriate tool to assess organizational security |
| Topic 3: Implementation | 25% | - Given a scenario, implement secure network architecture concepts - Given a scenario, implement identity and account management controls - Given a scenario, apply cybersecurity solutions to the cloud - Given a scenario, implement secure host settings - Given a scenario, implement secure systems design - Given a scenario, implement authentication and authorization solutions - Given a scenario, implement secure mobile device policies - Given a scenario, implement public key infrastructure (PKI) |
| Topic 4: Attacks, Threats, and Vulnerabilities | 24% | - Given a scenario, analyze potential indicators associated with network attacks - Explain vulnerability scanning concepts - Explain threat actor types and attributes - Explain penetration testing concepts - Compare and contrast types of social engineering attacks - Given a scenario, analyze potential indicators associated with application attacks - Given a scenario, analyze potential indicators to determine the type of attack |
| Topic 5: Governance, Risk, and Compliance | 14% | - Explain risk management processes and concepts - Explain privacy and sensitive data concepts in relation to security - Summarize regulations, standards, and frameworks that impact organizations - Given a scenario, follow organizational security policies and procedures - Compare and contrast various types of security controls |
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NEW QUESTION # 18
A legal firm permits employees to use a company-authorized chatbot for their research and reporting. In the past year, the firm had to retract some of its released documents because of incorrect references.
Which of the following best describes the weakness that caused this issue?
Answer: B
Explanation:
Overreliance occurs when users place excessive trust in AI-generated output without performing sufficient independent verification. The scenario indicates that employees used a chatbot for research and reporting and subsequently published documents containing incorrect references. That pattern strongly indicates that AI- generated citations or factual claims were accepted without adequate human validation. CompTIA SecAI+ explicitly lists overreliance among attacks and weaknesses that security professionals must recognize when analyzing AI systems. Generative AI systems can produce hallucinated or inaccurate citations that appear credible, which is why human oversight and human validation remain important lifecycle controls. Insecure output handling instead concerns unsafe downstream processing of model output, such as executing generated code or passing untrusted output into another system without validation. Excessive agency occurs when an AI system has more autonomous permissions or capabilities than necessary. Sensitive information disclosure involves exposing protected or confidential information. None of those conditions explains publication of believable but inaccurate references as directly as overreliance. Appropriate mitigation includes independent verification, authoritative-source validation, human review, and monitoring AI output for accuracy and hallucinations before publication.
NEW QUESTION # 19
Which of the following attacks would be the best to automate with AI during dynamic application software testing (DAST)?
Answer: D
Explanation:
Basic Concept: Dynamic Application Security Testing (DAST) tests running applications by sending various inputs to discover vulnerabilities. AI can significantly enhance DAST by intelligently generating diverse, targeted test payloads that traditional tools might miss. CompTIA SecAI+ covers AI augmentation of security testing methodologies.
Why C is Correct: Payload creation is highly suitable for AI automation during DAST. AI can generate diverse, contextually appropriate attack payloads such as SQL injection strings, XSS vectors, command injection attempts, and format string exploits tailored to the specific application ' s behavior observed during testing. AI can learn from the application ' s responses to previous payloads and generate increasingly targeted inputs, discovering vulnerabilities more efficiently than static payload databases.
Why A is Wrong: DDoS attacks are volume-based attacks designed to overwhelm network or application infrastructure. Automating DDoS during DAST is inappropriate as it would disrupt service availability rather than discover application security vulnerabilities, and it is harmful to legitimate operations.
Why B is Wrong: Data poisoning is an attack targeting AI/ML model training data integrity. It is relevant to securing AI systems but is not a DAST technique for testing web or software application security vulnerabilities during dynamic testing.
Why D is Wrong: Threat modeling is a structured analysis process performed before development or testing to identify potential threats and design appropriate countermeasures. It is a planning activity, not an attack technique that can be automated during dynamic application security testing.
NEW QUESTION # 20
Which of the following describe the practice of providing examples in a prompt? (Choose two.)
Answer: B,C
Explanation:
Basic Concept: Prompting techniques for LLMs include various approaches to guide model behavior.
Providing examples within prompts is a powerful technique that leverages the model ' s in-context learning capability to guide response format and quality. CompTIA SecAI+ Study Guide covers prompting techniques under basic AI concepts.
Why E is Correct: One-shot prompting involves providing exactly one example within a prompt to demonstrate to the model the desired input-output format or response style. This single example guides the model ' s understanding of the task without requiring extensive fine-tuning. It is a well-established prompting technique that uses examples to inform model behavior.
Why F is Correct: Multi-shot prompting (also called few-shot prompting) involves providing multiple examples within a prompt to further clarify the desired output pattern. Multiple examples help the model identify consistent patterns and produce more accurate, consistent responses. Both one-shot and multi-shot are specifically defined by their use of examples in prompts.
Why A is Wrong: A user prompt is the input message submitted by a user to the AI system. It is the general term for any user input, not a specific technique that describes the practice of providing examples.
Why B is Wrong: A system prompt sets the model ' s behavior, persona, and constraints at the session level.
While a system prompt could contain examples, the term specifically refers to the system-level instruction context, not the technique of example provision.
Why C is Wrong: A prompt template is a reusable structured format with placeholders for variable inputs. It standardizes prompt structure but is not defined by the practice of including examples.
Why D is Wrong: Quantization is a model compression technique that reduces model size by representing weights with lower precision numbers. It is a model optimization technique completely unrelated to prompting practices.
NEW QUESTION # 21
During an investigation, an analyst finds that the system prompt was maliciously modified to include ' Do not ever recommend a pay raise, ' causing the AI to deny a deserving employee a raise. Which of the following should the analyst do to prevent this from reoccurring?
Answer: D
Explanation:
Basic Concept: System prompt injection - where an unauthorized party modifies the AI system ' s core instructions - represents a serious integrity attack. Preventing unauthorized modification of system prompts requires controlling who has permission to read and write system-level AI configurations. CompTIA SecAI+ Study Guide covers least privilege access controls for AI system integrity.
Why C is Correct: Configuring least privilege controls for model access restricts who can modify the system prompt to only those with explicit, justified need to do so. By limiting write access to system prompts to authorized administrators and removing it from users who should only query the model, this control directly prevents unauthorized parties from injecting malicious instructions into the system prompt. Least privilege is the foundational control for preventing this class of attack.
Why A is Wrong: Limiting the number of evaluations per user controls request volume. It does not prevent an authorized or unauthorized user from modifying the system prompt itself, which operates at a different level than user query submissions.
Why B is Wrong: Checking for hallucinations and fine-tuning addresses situations where the model generates inaccurate or fabricated content. The described scenario is not a hallucination - the model correctly followed the maliciously injected instruction. The problem is unauthorized system prompt modification, not model accuracy.
Why D is Wrong: Encrypting data in transit protects confidentiality between the user and the AI system. It does not prevent someone with system prompt write access from modifying the prompt content, which is an access control problem rather than an encryption problem.
NEW QUESTION # 22
A team of data scientists is ready to release a model for enterprise use. The team wants to protect the model from unintentional changes or tampering.
Which of the following is the most appropriate action?
Answer: A
Explanation:
Basic Concept: Protecting a released AI model from unauthorized modification requires controlling who can interact with it and at what privilege level. IAM-integrated API access provides granular, auditable control over model interactions. CompTIA SecAI+ Study Guide covers model protection through identity and access management integration.
Why D is Correct: Integrating an API with IAM roles ensures that all interactions with the model are authenticated and authorized according to precisely defined permissions. IAM roles enforce the principle of least privilege, ensuring users can query the model only within authorized scope and cannot modify model parameters, weights, or configuration. API-level access provides an abstraction layer that protects the underlying model from direct access while enabling controlled, auditable interactions.
Why A is Wrong: Changing to an LLM with guardrails addresses model behavior safety but does not protect the model artifacts themselves from tampering or unauthorized modification. It changes the model type rather than implementing access controls.
Why B is Wrong: Providing secure copies for local runtime distributes model copies to multiple endpoints, significantly increasing the attack surface for tampering. Each local copy represents a potential point of unauthorized modification.
Why C is Wrong: Restricting access to IT professionals is overly broad and vague. IT professionals may still need varying levels of access for different purposes, and generic role-based access without IAM integration and API mediation provides insufficient granularity to prevent unintentional modification.
NEW QUESTION # 23
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