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CompTIA CY0-001 Exam Syllabus Topics:

SectionWeightObjectives
Basic AI Concepts Related to Cybersecurity17%- Core AI principles and terminology
  • 1. Generative AI concepts and capabilities
  • 2. Machine learning, deep learning, NLP, automation
- AI-driven threats and risks
  • 1. Malicious use of generative AI
  • 2. Adversarial machine learning attacks
  • 3. Automated phishing, polymorphic malware
- AI applications in security
  • 1. Security automation and decision support
  • 2. Threat detection and anomaly analysis
Securing AI Systems40%- Secure AI development and operations
  • 1. DevSecOps integration for AI
  • 2. Secure MLOps and AI pipeline design
- Security controls for AI systems
  • 1. Data protection: integrity, confidentiality, privacy
  • 2. Model security: access, integrity, anti-tampering
  • 3. Deployment environment security
- Defending against AI-specific attacks
  • 1. Adversarial example defense
  • 2. Threat modeling for AI lifecycles
  • 3. Prompt injection, data poisoning, model inversion
AI-assisted Security24%- AI in security strategy and operations
  • 1. Threat modeling and risk assessment
  • 2. Compliance monitoring and auditing
- Security automation and orchestration
  • 1. Vulnerability management and assessment
  • 2. Workflow automation and response playbooks
- AI for threat detection and response
  • 1. Automated incident triage and correlation
  • 2. Accelerated threat hunting
  • 3. Anomaly detection and behavioral analysis
AI Governance, Risk and Compliance19%- Compliance and legal requirements
  • 1. Data protection and privacy laws
  • 2. Transparency, accountability and auditability
- Governance frameworks and policies
  • 1. Organizational AI governance structures
  • 2. Responsible AI principles and ethics
  • 3. Global standards: NIST AI RMF, EU AI Act
- Risk management for AI
  • 1. Risk mitigation and control strategies
  • 2. AI risk identification and assessment

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CompTIA SecAI+ Certification Exam Sample Questions (Q11-Q16):

NEW QUESTION # 11
Which methods identify vulnerabilities BEFORE deployment? (Choose two.)

Answer: B,E

Explanation:
Pre-deployment assessments rely on design evaluation and code inspection.


NEW QUESTION # 12
A customer-facing, AI-powered chatbot has been jailbroken through prompt injections. As a result, the AI model is offering a 99% discount on the purchase of a new vehicle. Which of the following should be implemented to enhance the model's robustness against such attacks?

Answer: C

Explanation:
Guardrails enforce strict rules on what the AI model can and cannot do, preventing malicious prompt injections from overriding intended behavior. In this case, guardrails would stop the chatbot from generating unauthorized offers such as extreme discounts.


NEW QUESTION # 13
A security team is using an AI-based tool to try to bypass organizational boundaries. The team uses AI to look at the current state and suggest different attack vectors based on the outcome of the previous ones.
Which of the following techniques is the team most likely using?

Answer: C

Explanation:
Basic Concept: Modern penetration testing increasingly leverages AI to automate the reconnaissance, exploitation, and pivoting process. AI-assisted automated penetration testing can adapt its strategy based on previous results, simulating intelligent adversary behavior more realistically than static scripts. CompTIA SecAI+ covers AI-assisted offensive security techniques.
Why D is Correct: Automated penetration testing uses AI to systematically discover and attempt to exploit vulnerabilities while adapting tactics based on the results of previous attempts. The described behavior - looking at the current state, suggesting attack vectors, and adjusting based on outcomes - precisely describes an adaptive AI-driven penetration testing tool that iteratively explores the attack surface, mimicking how an advanced persistent threat would operate.
Why A is Wrong: Manual signature matching compares network traffic or files against a database of known threat signatures. It is a passive detection technique used by defensive tools like IDS/IPS, not an adaptive offensive technique for bypassing organizational boundaries.
Why B is Wrong: Code quality testing analyzes source code for bugs, vulnerabilities, and adherence to coding standards. It is a development quality assurance activity, not an offensive security technique for testing organizational security boundaries.
Why C is Wrong: Fraud detection uses ML to identify suspicious patterns in transactions or user behavior for defensive purposes. It is a preventive security measure, not an offensive technique for penetration testing.


NEW QUESTION # 14
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:
Basic Concept: Balancing automation with human oversight in vulnerability management requires understanding where AI adds efficiency and where human judgment is irreplaceable. CompTIA SecAI+ Study Guide emphasizes human-in-the-loop principles for high-stakes security decisions, particularly code changes in production systems.
Why A is Correct: Having AI handle triage and ticket creation leverages its ability to rapidly process and categorize large volumes of vulnerability findings, while requiring a software engineer to review and merge code changes maintains essential human oversight for production deployments. This balance maximizes automation benefits (faster triage at scale) while ensuring that actual code modifications to a million-line codebase receive appropriate human review before deployment.
Why B is Wrong: Having humans triage but AI merge code reverses the appropriate division. Manual triage of millions of lines worth of vulnerabilities is where the bottleneck exists. Allowing AI to autonomously merge code changes without human code review oversight creates unacceptable risk of introducing defects or vulnerabilities.
Why C is Wrong: Full manual triage and manual merging eliminates AI automation entirely, failing to address the speed requirement for reducing mean time to fix in a large codebase.
Why D is Wrong: Full AI automation including merging code changes removes essential human oversight from production code deployment. In a million-line codebase, autonomous AI code merging without human review could introduce critical errors or security vulnerabilities.


NEW QUESTION # 15
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: D

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 # 16
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