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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Architecture and Design | 21% | - Explain the security implications of embedded and specialized systems - Given a scenario, implement cybersecurity resilience - Explain secure application development, deployment, and automation concepts - Explain the importance of security concepts in an enterprise environment - Summarize basics of cryptographic concepts - Explain the importance of physical security controls - Summarize virtualization and cloud security concepts - Summarize authentication and authorization design concepts |
| Topic 2: Attacks, Threats, and Vulnerabilities | 24% | - Explain vulnerability scanning concepts - Given a scenario, analyze potential indicators associated with network attacks - Compare and contrast types of social engineering attacks - Explain penetration testing concepts - Explain threat actor types and attributes - Given a scenario, analyze potential indicators to determine the type of attack - Given a scenario, analyze potential indicators associated with application attacks |
| Topic 3: Governance, Risk, and Compliance | 14% | - Compare and contrast various types of security controls - Explain risk management processes and concepts - Summarize regulations, standards, and frameworks that impact organizations - Given a scenario, follow organizational security policies and procedures - Explain privacy and sensitive data concepts in relation to security |
| Topic 4: Operations and Incident Response | 16% | - Given a scenario, use appropriate tool to assess organizational security - Given a scenario, apply mitigation techniques or controls to secure an environment - Explain key aspects of digital forensics - Summarize the importance of policies, processes, and procedures for incident response - Given a scenario, use data sources to support an investigation |
| Topic 5: Implementation | 25% | - Given a scenario, implement secure host settings - Given a scenario, implement secure mobile device policies - Given a scenario, implement identity and account management controls - Given a scenario, implement secure network architecture concepts - Given a scenario, implement public key infrastructure (PKI) - Given a scenario, apply cybersecurity solutions to the cloud - Given a scenario, implement secure systems design - Given a scenario, implement authentication and authorization solutions |
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NEW QUESTION # 58
Which of the following improves the observability and auditing of an AI system?
Answer: B
Explanation:
Basic Concept: Observability in AI systems refers to the ability to monitor, log, trace, and audit the behavior of AI models in production. MLOps is the operational discipline that establishes the processes, tooling, and practices for managing AI systems throughout their lifecycle. CompTIA SecAI+ Study Guide covers MLOps as a key mechanism for AI system transparency and auditability.
Why C is Correct: MLOps implements comprehensive monitoring, logging, versioning, and audit pipelines for AI systems. It provides observability through model performance tracking, data drift detection, prediction logging, lineage tracking, and audit trails. MLOps platforms enable organizations to understand what their AI models are doing, why they are making certain decisions, and how their behavior changes over time, directly improving observability and auditing.
Why A is Wrong: Redeploying a model is an operational action taken to restore a previous version or apply updates. It does not improve monitoring infrastructure, logging capabilities, or auditing frameworks for ongoing observability.
Why B is Wrong: Manual detection relies on human observation to identify issues. It is labor-intensive, inconsistent, and not scalable for AI systems processing high volumes of data. It does not provide systematic observability or comprehensive audit trails.
Why D is Wrong: Anomaly detection identifies unusual patterns in data or behavior. While useful as a monitoring component within an observability strategy, it is a single technique and does not encompass the full observability and auditing capabilities provided by a comprehensive MLOps implementation.
NEW QUESTION # 59
A security administrator must provide access controls for AI systems to list tables.
Which of the following should the administrator implement?
Answer: A
Explanation:
Basic Concept: AI systems interact with different resource layers including models, data stores, and infrastructure. Controlling what data an AI system can access requires implementing access controls at the data layer. CompTIA SecAI+ Study Guide differentiates between model access, data access, and network access controls for AI systems.
Why D is Correct: Data access controls govern what data resources an AI system can interact with, including which databases, tables, and records it can read or modify. To control an AI system ' s ability to list database tables, the administrator must implement data access controls that define precisely which tables the AI can enumerate and query, following the principle of least privilege for data interactions.
Why A is Wrong: Agentic AI access refers to permissions granted to autonomous AI agents to perform actions and use tools. It is a broader concept about what an AI agent can do operationally rather than a specific data-layer access control mechanism.
Why B is Wrong: A Network Access Control List controls network traffic at the IP and port level, determining which hosts can communicate with which network resources. It operates at the network layer and cannot enforce fine-grained control over which database tables an AI system is allowed to list.
Why C is Wrong: Model access controls govern who and what can interact with the AI model itself - who can query it, update it, or access its parameters. This is distinct from data access, which controls what the model can read from data stores during operation.
NEW QUESTION # 60
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: B
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 # 61
An organization is developing and implementing AI features into a customer service application.
Which of the following practices should the organization put the place before releasing the application for customer trials?
Answer: B
Explanation:
Before releasing AI features for customer trials, it is critical to protect sensitive information that may be used during testing. Data masking and sanitization ensure customer or corporate data is anonymized or obfuscated, reducing the risk of data exposure while still allowing realistic evaluation of the AI system.
NEW QUESTION # 62
A detection engineering team wants to use AI to automatically prevent vulnerable code from reaching production.
Which of the following is the most effective way to accomplish this task?
Answer: B
Explanation:
Basic Concept: Preventing vulnerable code from reaching production requires an automated, mandatory gate in the software delivery pipeline. The CI/CD pipeline is the enforcement point where all code must pass before deployment. CompTIA SecAI+ Study Guide covers AI integration in secure development pipelines under AI-assisted security.
Why C is Correct: Implementing an LLM in the CI/CD runner creates a mandatory automated security gate that every code change must pass. The LLM can analyze code for vulnerabilities, insecure patterns, and policy violations, then automatically fail the build if issues are found. This prevents vulnerable code from progressing toward production without human bypass capability, making it the most effective enforcement mechanism.
Why A is Wrong: IDE plug-ins provide warnings to developers during coding, but developers can choose to ignore them and proceed with compilation and commits. Warnings are advisory, not preventive, and cannot guarantee vulnerable code is blocked from the pipeline.
Why B is Wrong: SOAR platforms with ML models are excellent for incident response and security operations automation. However, they are not positioned in the code delivery pipeline and do not gate code from progressing to production.
Why D is Wrong: An agentic penetration testing tool validates vulnerabilities reactively after code is written or deployed. This approach does not intercept code before production deployment and is typically used for post-deployment assessment rather than prevention.
NEW QUESTION # 63
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