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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Operations and Incident Response | 16% | - Given a scenario, use data sources to support an investigation - Summarize the importance of policies, processes, and procedures for incident response - Explain key aspects of digital forensics - Given a scenario, apply mitigation techniques or controls to secure an environment - Given a scenario, use appropriate tool to assess organizational security |
| Topic 2: Governance, Risk, and Compliance | 14% | - Summarize regulations, standards, and frameworks that impact organizations - 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 |
| Topic 3: Architecture and Design | 21% | - Explain the importance of security concepts in an enterprise environment - Summarize basics of cryptographic concepts - Summarize virtualization and cloud security concepts - 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 - Given a scenario, implement cybersecurity resilience |
| Topic 4: Attacks, Threats, and Vulnerabilities | 24% | - 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 - Given a scenario, analyze potential indicators associated with application attacks - Explain vulnerability scanning concepts - Explain penetration testing concepts - Compare and contrast types of social engineering attacks |
| Topic 5: Implementation | 25% | - Given a scenario, implement authentication and authorization solutions - Given a scenario, apply cybersecurity solutions to the cloud - Given a scenario, implement secure systems design - Given a scenario, implement secure mobile device policies - Given a scenario, implement public key infrastructure (PKI) - Given a scenario, implement identity and account management controls - Given a scenario, implement secure host settings - Given a scenario, implement secure network architecture concepts |
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NEW QUESTION # 41
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 # 42
An organization deploys an application programming interface (API) to allow external customers to perform tasks supported by internally developed AI models. Some customers require limited use of sensitive data.
After the API is deployed, customers report that the API returns sensitive data to all customers. Which of the following is the best action to take with the API?
Answer: A
Explanation:
Option D is correct because the failure is an authorization problem: every external customer can receive sensitive information even though only some customers are permitted to use it. Role-based access control assigns permissions to defined customer roles and requires the API to evaluate the caller's role before returning protected fields or invoking sensitive model functions. Properly implemented, RBAC supports least privilege and separates ordinary customers from approved sensitive-data users. Option A may route customers to different models, but model selection does not itself enforce who is authorized to receive data. Option B changes model behavior and training data, yet retraining cannot replace an access-control decision at the API boundary. Option C may improve network isolation, but placing the model in a VPC does not stop an authenticated external customer from receiving information that the API exposes. The organization should also apply field-level filtering, deny-by-default policies, logging, and authorization testing. NIST's RBAC model assigns users to roles and associates each role with permitted privileges, directly supporting this control.
NEW QUESTION # 43
Which of the following describes the process of adjusting the distribution of underrepresented classes in an AI model data set to improve model performance?
Answer: B
Explanation:
Data balancing is the process specifically intended to correct disproportionate class distributions within a machine learning dataset. CompTIA SecAI+ lists data balancing as a distinct data-processing concept alongside data cleansing, verification, lineage, provenance, and augmentation. When one class substantially outnumbers another, the model may learn to favor the dominant class and perform poorly on underrepresented examples. Balancing techniques can modify this distribution through approaches such as oversampling minority classes, undersampling majority classes, assigning class weights, or generating suitable additional minority examples. The objective is to give the model adequate exposure to each relevant class and improve generalization across the complete problem space. Data augmentation creates additional or modified training examples and can sometimes contribute to balancing, but augmentation is broader and does not specifically mean adjusting class distribution. Data lineage records where data originated and how it changed throughout its lifecycle. Data cleansing removes or corrects inaccurate, duplicate, malformed, or otherwise unsuitable records. Because the question specifically asks about changing the representation of underrepresented classes, data balancing is the precise CompTIA concept being tested.
NEW QUESTION # 44
A social media company with more than a million lines of code wants to reduce the mean time to fix bugs and issues. Which of the following is the most balanced AI strategy to automate the vulnerability management flow?
Answer: C
Explanation:
This approach balances automation and human oversight. AI accelerates vulnerability management by triaging issues and generating tickets, while software engineers retain responsibility for merging code changes, ensuring quality and reducing the risk of insecure or unstable code being deployed.
NEW QUESTION # 45
A security analyst is aware of an active penetration test in the environment. The analyst examines SIEM log data and notices the following AI system output:
Which of the following is the vulnerability that has occurred and the control the analyst should implement?
Answer: B
Explanation:
Basic Concept: AI systems can inadvertently reveal sensitive information such as PII, credentials, or internal data in their outputs when not properly controlled. Sensitive information disclosure is a critical OWASP LLM Top 10 risk. CompTIA SecAI+ Study Guide covers both vulnerability identification and appropriate data protection controls for AI outputs.
Why D is Correct: The scenario describes the AI system outputting sensitive information in its responses, which is a sensitive information disclosure vulnerability. The appropriate control is masking, which replaces sensitive data values such as credit card numbers, SSNs, or API keys with redacted or tokenized equivalents in the model ' s outputs before they are returned to users. This prevents the AI from disclosing sensitive data while still providing useful responses.
Why A is Wrong: Prompt injection involves crafting inputs to override model instructions. If the penetration test revealed sensitive information, the primary vulnerability is the disclosure of that sensitive data, not the injection mechanism itself. EDR monitors endpoint behavior, not AI output content.
Why B is Wrong: Model hallucinations produce fabricated information rather than disclosing real sensitive data. The described scenario involves actual sensitive information being revealed, not fictitious content generation.
Why C is Wrong: Jailbreaking circumvents safety restrictions but the primary harm demonstrated is sensitive data exposure. RBAC manages access permissions but does not prevent the model from including sensitive data in responses once access is granted.
Why E is Wrong: Role impersonation involves the AI pretending to be a different entity. This may be a secondary technique used by the penetration tester but the primary vulnerability described is the disclosure of actual sensitive information in the output.
NEW QUESTION # 46
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