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| Section | Weight | Objectives |
|---|---|---|
| Implementation | 25% | - Given a scenario, implement secure network architecture concepts - Given a scenario, implement identity and account management controls - Given a scenario, implement secure host settings - Given a scenario, apply cybersecurity solutions to the cloud - Given a scenario, implement secure systems design - Given a scenario, implement public key infrastructure (PKI) - Given a scenario, implement secure mobile device policies - Given a scenario, implement authentication and authorization solutions |
| Operations and Incident Response | 16% | - Explain key aspects of digital forensics - Summarize the importance of policies, processes, and procedures for incident response - Given a scenario, use appropriate tool to assess organizational security - Given a scenario, use data sources to support an investigation - Given a scenario, apply mitigation techniques or controls to secure an environment |
| Governance, Risk, and Compliance | 14% | - Summarize regulations, standards, and frameworks that impact organizations - Given a scenario, follow organizational security policies and procedures - Explain risk management processes and concepts - Explain privacy and sensitive data concepts in relation to security - Compare and contrast various types of security controls |
| Architecture and Design | 21% | - Explain the importance of physical security controls - Explain the importance of security concepts in an enterprise environment - Explain the security implications of embedded and specialized systems - Given a scenario, implement cybersecurity resilience - Explain secure application development, deployment, and automation concepts - Summarize basics of cryptographic concepts - Summarize virtualization and cloud security concepts - Summarize authentication and authorization design concepts |
| Attacks, Threats, and Vulnerabilities | 24% | - Given a scenario, analyze potential indicators associated with network attacks - Explain threat actor types and attributes - 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 - Given a scenario, analyze potential indicators associated with application attacks |
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NEW QUESTION # 116
Which of the following roles best supports the implementation of AI governance, risk, and compliance (GRC)? (Choose two.)
Answer: C,E
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 # 117
Which of the following certifies an organization in the use of responsible AI?
Answer: A
Explanation:
ISO/IEC 42001 is the correct answer because it is an Artificial Intelligence Management System (AIMS) standard that organizations can implement to demonstrate structured and responsible governance of AI.
CompTIA SecAI+ explicitly includes ISO AI standards within the governance, risk, and compliance domain and expects candidates to distinguish standards from regulatory frameworks. ISO/IEC 42001 specifies requirements for establishing, implementing, maintaining, and continually improving an AI management system. Its scope includes responsible AI use, governance, risk management, accountability, transparency, and organizational controls. ISO describes the standard as applicable to organizations that develop, provide, or use AI systems and specifically identifies responsible AI and AI governance among its benefits. GDPR is legally binding privacy legislation rather than an AI management-system certification. The EU AI Act is also legislation and establishes regulatory requirements based on AI risk. NIST AI RMF provides voluntary risk- management guidance but is not itself an organizational certification standard. Therefore, ISO/IEC 42001 is the option specifically designed to support certifiable organizational AI-management practices.
NEW QUESTION # 118
SIMULATION
Instructions
Part1
Use drop-down menu to select the most appropriate protocol or cipher for each system component.
Part2
Use the drop-down menu to select the most appropriate technique to apply to the modified data.
It at any time you would like to bring back the initial state of the simulation, please click the Reset All button.
An engineer is analyzing findings from a penetration test that indicate insufficient data encryption.
The engineer must implement data security.
Answer:
Explanation:
Explanation:
Part 1 (Protocols/Ciphers):
API Gateway: TLS 1.2
* Database: AES-512
* AI Model: gRPC
Part 2 (Techniques):
1. {pin:"999-99-9999", name:"john doe"} → {pin:"999-99-9999", name:"john doe", sensitivity:"SECRET"} → Classification
2. {ip_addr:"1.2.3.4", cookie:"aK3idkd==", name:"John Doe" uid="1111"} → {cookie:"aK3idkd==", uid="1111"} → De-identification
3. {pin:"999-99-9999", name:"john doe"} → {pin:"999-99-XXXX", name:"john doe"} → Masking
4. {card_number:"1111222233334444", name:"john doe", user_id:"1"} →
{card_number:"0x019238829", name:"john doe", user_id:"1"} → Tokenization
5. {name:"john doe", patient_id:"10000", dob:"1980-Jan-05"} → {patient_id:"10000", dob:"1980- Jan-05"} → Anonymization Part 1:
* TLS 1.2 secures client-to-gateway communications.
* AES-512 provides strong encryption for data at rest in the database.
* gRPC ensures efficient, secure communication between services (AI model).
Part 2:
* Classification tags sensitive data for handling.
* De-identification strips direct identifiers.
* Masking obscures part of sensitive values while keeping format.
* Tokenization replaces sensitive data with a reversible placeholder.
* Anonymization removes identifying attributes, making re-identification impossible.
NEW QUESTION # 119
A company develops an AI model to diagnose patients. Hospitals access the model through an integrated application programming interface (API). The security team performs a denial-of-service (DoS) attack via brute force on the model.
Which of the following controls would have prevented this issue?
Answer: D
Explanation:
Basic Concept: API-based AI systems are susceptible to DoS attacks where excessive requests overwhelm the system ' s ability to respond to legitimate users. Rate limiting is the standard control for preventing both intentional and unintentional API abuse. CompTIA SecAI+ Study Guide covers rate limiting as a key availability control for AI APIs.
Why C is Correct: Rate limiting restricts the number of requests a client can make to an API within a defined time window. In this scenario, a brute-force DoS attack works by sending a massive volume of requests to exhaust the model ' s resources. Rate limiting would have automatically throttled or blocked the excessive request volume, preventing the attack from succeeding and preserving service availability for legitimate hospital users.
Why A is Wrong: Tokenization replaces sensitive data values with non-sensitive placeholders. It is a data security control for protecting sensitive information such as patient identifiers, not a control for managing API request volumes or preventing DoS attacks.
Why B is Wrong: Model guardrails filter and constrain model inputs and outputs for safety and policy compliance. They inspect content quality, not request volume, and cannot prevent a volume-based DoS attack.
Why D is Wrong: A prompt firewall inspects the content of prompts for malicious patterns or policy violations. Like guardrails, it analyzes content rather than controlling request frequency and cannot prevent resource exhaustion from a high-volume brute-force attack.
NEW QUESTION # 120
An airline corporation wants to implement a chatbot application using a large language model (LLM) so its customers can ask questions and receive answers about flight details and have the option to upload files.
Which of the following security controls should the airline use to protect against malicious input and unauthorized use beyond the service-level agreement? (Choose two.)
Answer: A,B
Explanation:
Basic Concept: LLM-based chatbots accepting user-uploaded files face two critical risk categories: malicious input injection and resource or cost abuse. CompTIA SecAI+ Study Guide highlights prompt security controls and resource management as key defensive layers for public-facing LLM applications.
Why A is Correct: Prompt guardrails intercept and filter user inputs and model outputs, blocking malicious prompts, prompt injection attempts, and harmful file content before affecting model behavior. Since users can upload files, guardrails are essential for sanitizing and validating that content before processing.
Why D is Correct: Model token quotas directly limit how much of the LLM ' s processing capacity a user can consume. This prevents abuse beyond the SLA, including denial-of-wallet attacks or resource exhaustion through excessively large inputs or repeated requests.
Why B is Wrong: Role-based access controls manage who can access what resources. While useful for internal systems, they do not address malicious input content or enforce LLM resource consumption limits for a public-facing chatbot.
Why C is Wrong: Firewall rules operate at the network layer and can block unauthorized IPs or ports but cannot inspect or filter the semantic content of prompts or control token-level LLM usage.
NEW QUESTION # 121
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