1. Question 1

What is one of the advantages of generative AI over conventional AI in cybersecurity?

❌ Well-defined task execution
❌ Structured data analysis
❌ Diagnostic analytics
✅ Adaptability to novel and evolving threats

Explanation:
Generative AI can generalize from incomplete patterns and adapt to new threats that were not previously seen, making it more flexible than conventional rule-based or supervised models.

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2. Question 2

What does behavioral analytics in cybersecurity focus on?

❌ Analyzing network traffic patterns
✅ Analyzing user and entity behavior within a network
❌ Scrutinizing endpoint behavior
❌ Detecting intrusions and malware activity

Explanation:
Behavioral analytics tracks how users and systems normally behave to detect deviations that may indicate an attack.

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3. Question 3

How does generative AI contribute to anomaly detection in cybersecurity?

✅ By learning standard user behavior and network patterns
❌ By ignoring deviations from normal behavior
❌ By encrypting all user data
❌ By blocking all network traffic

Explanation:
Generative models learn what “normal” looks like, so anything abnormal can be flagged as a potential threat.

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4. Question 4

Why is incomplete training data a threat to generative AI models?

✅ It may lead to inaccurate or insecure model outputs.
❌ It enhances model training efficiency.
❌ It doesn’t impact the model’s ability to generalize.
❌ It ensures accurate model generalization.

Explanation:
Missing or biased training data reduces model reliability, causing wrong predictions or exploitable weaknesses.

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5. Question 5

How does generative AI automate incident triage in cybersecurity?

❌ By ignoring incoming data
❌ By encrypting incident data
✅ By rapidly analyzing incoming data and determining severity and relevance
❌ By focusing only on historical incidents

Explanation:
Generative AI can quickly classify incidents, prioritize risks, and support analysts for faster response.

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6. Question 6

How does generative AI ensure the effectiveness of playbooks against the latest cybersecurity threats?

❌ By automating incident triage only
❌ By ignoring evolving threats
❌ By focusing solely on historical incident data
✅ By continuously learning from new data and evolving threats

Explanation:
Generative AI updates its understanding based on new threat intelligence, keeping playbooks relevant.

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7. Question 7

How does generative AI contribute to automated summarization in cybersecurity?

❌ By encrypting lengthy reports
❌ Ignoring natural language processing
✅ By automating the condensation of intricate information from reports
❌ Focusing solely on critical details

Explanation:
Generative AI uses NLP to summarize long logs, alerts, and reports into digestible insights.

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8. Question 8

What is the impact of generative AI on incident response in cybersecurity?

❌ Slowing down incident response
❌ Ignoring customization and scalability
❌ Focusing solely on routine events
✅ Accelerating the analysis of cybersecurity reports

Explanation:
AI speeds up review and interpretation of complex data, improving response time significantly.

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9. Question 9

How does generative AI contribute to the real-time detection of potential security breaches?

❌ Compliance reporting
❌ Network performance enhancement
✅ Continuous monitoring and analysis of user behavior
❌ Code optimization

Explanation:
Generative AI continuously evaluates behavioral patterns to detect anomalies instantly.

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10. Question 10

Which type of analytics involves investigating why events occurred and pinpointing vulnerabilities and weaknesses?

❌ Predictive analytics
✅ Diagnostic analytics
❌ Prescriptive analytics
❌ Descriptive analytics

Explanation:
Diagnostic analytics focuses on root-cause analysis and understanding why an incident happened.

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🧾 Summary Table

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