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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Governance, Risk, and Compliance | 14% | - Compare and contrast various types of security controls - Explain privacy and sensitive data concepts in relation to security - Explain risk management processes and concepts - Summarize regulations, standards, and frameworks that impact organizations - Given a scenario, follow organizational security policies and procedures |
| Topic 2: Operations and Incident Response | 16% | - Given a scenario, use data sources to support an investigation - 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 - Given a scenario, use appropriate tool to assess organizational security |
| Topic 3: Attacks, Threats, and Vulnerabilities | 24% | - Given a scenario, analyze potential indicators to determine the type of attack - Compare and contrast types of social engineering attacks - Given a scenario, analyze potential indicators associated with application attacks - Explain vulnerability scanning concepts - Explain threat actor types and attributes - Given a scenario, analyze potential indicators associated with network attacks - Explain penetration testing concepts |
| Topic 4: Architecture and Design | 21% | - Summarize authentication and authorization design concepts - Summarize basics of cryptographic concepts - Explain the importance of security concepts in an enterprise environment - Explain secure application development, deployment, and automation concepts - Given a scenario, implement cybersecurity resilience - Explain the importance of physical security controls - Explain the security implications of embedded and specialized systems - Summarize virtualization and cloud security concepts |
| Topic 5: Implementation | 25% | - Given a scenario, implement secure host settings - Given a scenario, implement identity and account management controls - 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 secure network architecture concepts - Given a scenario, implement authentication and authorization solutions |
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NEW QUESTION # 125
A line of business wants to onboard an application that uses a custom AI model for employee assessments. The Chief Information Officer (CIO) agrees to allow the engagement to proceed but first wants a threat model. Which of the following is the most appropriate to use for an AI threat model?
Answer: C
Explanation:
ATLAS is specifically designed for creating AI threat models. It catalogs adversarial techniques, tactics, and use cases that target AI systems, making it the most appropriate framework for assessing risks to a custom AI model in employee assessments.
NEW QUESTION # 126
Which of the following are considered threat intelligence sources? (Choose two.)
Answer: A,C
Explanation:
ISACs and OSINT provide structured and open-source threat intelligence feeds.
NEW QUESTION # 127
Which of the following explains the reason a cybersecurity analyst prefers a machine learning (ML) model over a statistical model for attack classification?
Answer: C
Explanation:
Basic Concept: Cybersecurity threats evolve continuously, with new attack variants emerging regularly. The choice between traditional statistical models and ML models for attack classification depends on which better handles the complexity and dynamism of the threat landscape. CompTIA SecAI+ covers ML model advantages for cybersecurity under basic AI concepts.
Why A is Correct: ML models can learn arbitrarily complex, non-linear relationships from training data and adapt to new patterns when retrained with updated data. For attack classification, this means ML can recognize sophisticated, multi-feature attack patterns that exceed the capabilities of simple statistical models and can be updated to detect new attack variants as the threat landscape evolves. This adaptability to complex and changing problems is the primary reason analysts prefer ML over static statistical approaches.
Why B is Wrong: ML model development pipelines are generally more complex than statistical models, requiring data preparation, feature engineering, model selection, training, validation, and deployment steps.
Simplicity of development is not a characteristic advantage of ML over statistical models.
Why C is Wrong: ML models typically require large amounts of training data to perform well. Statistical models often perform better than ML with small datasets. Performance with small datasets is actually an advantage of statistical models over ML, not ML over statistical.
Why D is Wrong: Community support and expert availability are ecosystem considerations rather than technical reasons to prefer ML for cybersecurity classification tasks. These factors might influence tool selection but do not explain the fundamental technical preference for ML ' s superior handling of complex attack patterns.
NEW QUESTION # 128
Which of the following types of prompts best describes a developer's input that informs AI interactions?
Answer: B
Explanation:
Option A is correct because a system prompt contains the developer- or platform-defined instructions that establish how the AI should behave throughout an interaction. It can define the model's role, objectives, prohibited actions, response style, tool-use boundaries, and security constraints before the user submits a request. That description matches an input supplied by a developer to govern later AI interactions. Option B is the end user's prompt or request; it expresses what the user wants in a particular turn and normally has lower authority than system-level instructions. Option C, zero-shot prompting, describes asking the model to perform a task without providing an example. Option D, multi-shot prompting, supplies multiple examples to demonstrate the desired input or output pattern. Neither zero-shot nor multi-shot identifies the authority or source of the instruction. In a secure AI application, system instructions should be protected from unauthorized modification, tested for conflicts, and reinforced with technical controls rather than treated as the sole security boundary. The essential distinction is prompt hierarchy: system instructions configure persistent behavior, while user prompts request individual tasks.
NEW QUESTION # 129
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: A
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 # 130
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