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| Section | Weight | Objectives |
|---|---|---|
| Architecture and Design | 21% | - Explain the security implications of embedded and specialized systems - Explain the importance of physical security controls - Explain secure application development, deployment, and automation concepts - Summarize authentication and authorization design concepts - Summarize virtualization and cloud security concepts - Given a scenario, implement cybersecurity resilience - Explain the importance of security concepts in an enterprise environment - Summarize basics of cryptographic concepts |
| Governance, Risk, and Compliance | 14% | - Explain privacy and sensitive data concepts in relation to security - Given a scenario, follow organizational security policies and procedures - Compare and contrast various types of security controls - Explain risk management processes and concepts - Summarize regulations, standards, and frameworks that impact organizations |
| Implementation | 25% | - 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 secure network architecture concepts - Given a scenario, implement identity and account management controls - Given a scenario, implement secure mobile device policies - Given a scenario, implement public key infrastructure (PKI) - Given a scenario, implement authentication and authorization solutions |
| Attacks, Threats, and Vulnerabilities | 24% | - Given a scenario, analyze potential indicators associated with application attacks - Explain threat actor types and attributes - Given a scenario, analyze potential indicators associated with network attacks - Given a scenario, analyze potential indicators to determine the type of attack - Explain vulnerability scanning concepts - Compare and contrast types of social engineering attacks - Explain penetration testing concepts |
| Operations and Incident Response | 16% | - Given a scenario, use data sources to support an investigation - Explain key aspects of digital forensics - Summarize the importance of policies, processes, and procedures for incident response - Given a scenario, apply mitigation techniques or controls to secure an environment - Given a scenario, use appropriate tool to assess organizational security |
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NEW QUESTION # 30
A security analyst who is reviewing logs notices the following:
* Suspicious inputs are present and contain character look-alikes.
* Strange character substitutions
* Odd spacing
* Inputs that appear to be broken sentence structures
Which of the following best explains what the user was utilizing?
Answer: B
Explanation:
Obfuscation is the correct answer because the observed inputs show deliberate attempts to make content difficult for automated security controls or AI systems to interpret while preserving enough meaning for the intended recipient. Character look-alikes, unusual Unicode substitutions, abnormal spacing, and deliberately fragmented sentence structures are common techniques for disguising malicious or restricted content.
CompTIA SecAI+ explicitly identifies obfuscation within the AI-assisted security domain as a way AI can enable or enhance attack vectors.
Disinformation involves intentionally spreading false information to deceive an audience, while misinformation refers to inaccurate information that may be distributed without deliberate malicious intent.
Neither term describes manipulating the representation of text to evade detection. Anonymization removes or obscures identifying information to protect identity or privacy; it does not explain character substitution or malformed sentence construction. In this scenario, the attacker is manipulating how the input appears so filtering, pattern-matching, or other inspection mechanisms may have difficulty recognizing the underlying content. Therefore, obfuscation most precisely describes the activity.
NEW QUESTION # 31
A management team is concerned about an unexpected cost increase for a public-facing AI chatbot.
Which of the following should a security administrator examine first to determine the root cause?
Answer: A
Explanation:
Basic Concept: AI chatbot operational costs are primarily driven by token consumption - the number of tokens processed in requests and generated in responses. Unexpected cost increases in LLM-based chatbots almost always trace back to abnormal token usage patterns. CompTIA SecAI+ Study Guide covers AI cost monitoring and token-based billing under securing AI systems.
Why D is Correct: Model token usage logs directly show how many tokens are being consumed per request, by which users or endpoints, and whether usage has increased abnormally. Examining token usage data is the most direct path to identifying the root cause of unexpected cost increases - whether from a denial-of-wallet attack, user abuse, a new feature generating verbose responses, or legitimate organic growth in usage. This is the first and most relevant examination point for LLM cost analysis.
Why A is Wrong: Firewall logs capture network-level traffic information. While they can reveal unusual access patterns or volumes, they do not contain token consumption data that directly explains LLM billing increases.
Why B is Wrong: WAF rules define filtering policies for web traffic. Reviewing rule configurations does not reveal whether token usage has increased or why costs have risen; it shows security policy settings rather than consumption metrics.
Why C is Wrong: Vector database IOPS performance measures how quickly the database processes read and write operations. While relevant to RAG system performance, IOPS metrics do not directly explain LLM API cost increases driven by token consumption.
NEW QUESTION # 32
A customer using a travel chatbot reports that the chatbot responds with the following:
def choose_location(location):
if location == " United States of America " :
print( " You have selected an authorized travel destination " )
elif location == " Europe " :
print( " You have selected an unauthorized travel destination " )
Which of the following remediates this issue?
Answer: B
Explanation:
Deploying data loss prevention (DLP) is the best answer because the chatbot has exposed internal application logic rather than returning only the information intended for the customer. This represents a form of sensitive information disclosure , which CompTIA SecAI+ specifically identifies as an AI-system attack or failure condition. The objectives also emphasize data security controls such as classification, redaction, masking, and minimization to prevent inappropriate disclosure.
A DLP control can inspect generated responses for source code, confidential identifiers, credentials, internal business logic, or other restricted information and prevent that content from being returned externally.
Retraining the model to eliminate bias is unrelated because no discriminatory or statistically skewed behavior is shown. Secure output handling is important when model-generated output is consumed by downstream applications and could lead to injection or execution vulnerabilities. Here, however, the immediate issue is disclosure of internal information directly to a user. Cookies also do not address the problem; they are associated with maintaining web sessions and are not a general mitigation for server-side request forgery.
Therefore, DLP most directly addresses the exposed information.
NEW QUESTION # 33
A web server shows signs of SQL injection. Which control BEST prevents this?
Answer: B
Explanation:
Validating and sanitizing user input is the core SQLi defense.
NEW QUESTION # 34
Instructions: Use the drop-down menus to define two appropriate security controls for each component of the AI system. Each control may be used only once.
An engineer is deploying a new AI system and wants to integrate it into the core system through an API.
Answer:
Explanation:
Explanation:
Basic Concept: This is a Performance-Based Question (PBQ) - a HOTSPOT/simulation item requiring interactive selection in the actual exam. It tests the candidate ' s ability to map appropriate security controls to AI system components such as API gateway, model endpoint, data layer, and authentication layer.
Key Concept - Appropriate Controls by Component: For an API gateway connecting an AI system, typical controls include API key authentication, rate limiting, TLS encryption, and input validation. For the model endpoint, controls include IAM role-based access, audit logging, and guardrails. For data access components, encryption at rest and data masking are appropriate. For the authentication layer, MFA and expiring session tokens are relevant.
Why This Matters: The CompTIA SecAI+ Study Guide emphasizes defense-in-depth for AI system integration, ensuring each architectural layer has dedicated, appropriate security controls. The principle of least privilege should guide access control assignments at each component, while availability controls such as rate limiting protect against abuse.
Reference: CompTIA SecAI+ Exam Objectives Domain 2 (Securing AI Systems) covers AI system component security controls. Candidates should study the mapping of controls to infrastructure components including API gateways, model serving endpoints, data stores, and identity management layers. In the live exam, select the most specific and directly relevant control for each component based on the component ' s function and risk profile.
NEW QUESTION # 35
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