1. Question 1

What is a key application of generative AI related to threat intelligence in cybersecurity?

❌ Incident response planning
✅ Threat intelligence and forecasting
❌ Dynamic threat hunting
❌ Automated security alerts

Explanation:
Generative AI analyzes massive threat datasets to predict emerging threats and generate forward-looking intelligence.

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2. Question 2

How does generative AI contribute to cybersecurity in analyzing malware?

❌ Meeting regulatory compliance requirements
❌ Simulating potential attack scenarios
❌ Handling large amounts of data from different sources
✅ Analyzing code structure and behavior

Explanation:
Generative models can study malware code patterns, behaviors, and variants to detect new or mutated malware.

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3. Question 3

What is the primary risk associated with “Unintended consequences of AI-driven deception”?

❌ Privacy violations
✅ Collateral damage to legitimate users
❌ Lack of transparency
❌ Bias and discrimination

Explanation:
AI deception (e.g., fake traffic or honeypots) can accidentally disrupt or mislead legitimate users or systems.

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4. Question 4

What purpose does a cybersecurity response playbook serve?

❌ A document summarizing daily cybersecurity activities
❌ A historical record of incidents
❌ A tool for hacking systems
✅ A blueprint detailing actions during a security incident

Explanation:
A playbook provides step-by-step actions and responsibilities during cyber incidents to ensure consistent response.

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5. Question 5

How do machine learning algorithms in SIEM mitigate false positives?

✅ By learning from historical data and establishing baselines
❌ By creating static rules
❌ By reducing false negatives
❌ By increasing alert fatigue

Explanation:
ML models detect what is “normal,” reducing unnecessary alerts by filtering benign anomalies.

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6. Question 6

What will be the key advantage of integrating generative AI into the updated QRadar SIEM platform?

❌ Reduced reliance on AI
❌ Exclusion of log management
✅ Automation of repetitive tasks for security operations teams
❌ Improved user interface design

Explanation:
Generative AI helps analysts by automating triage, summarization, correlation, and repetitive workflows.

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7. Question 7

What is a significant limitation regarding generative models in anomaly detection?

❌ Minimal impact on false positives or negatives
✅ Depend on the quality and quantity of training data
❌ Offer interpretability
❌ Do not require much computing power

Explanation:
Poor or insufficient data causes model drift, inaccurate anomalies, or biased outcomes.

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8. Question 8

What is the primary concern regarding the rapid growth of Generative AI?

❌ Fear and litigation
❌ Secured development
❌ Mass adoption
✅ Enhanced customer experience (Incorrect — Wait!)

Correct Answer: ❌ Enhanced customer experience
✅ Fear and litigation

Explanation:
The major concern is legal, ethical, and misuse risks—not customer experience or adoption.

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9. Question 9

Primary ethical concern with bias in AI-driven threat detection?

❌ Enhanced accuracy
❌ Increased efficiency
✅ Unfair discrimination
❌ Speedier response times

Explanation:
Bias may unfairly target certain users or behaviors, creating unjust security outcomes.

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10. Question 10

What does the scenario of privacy concerns in network monitoring emphasize?

❌ Collecting extensive communication data
❌ Ignoring privacy rights
✅ Balancing cybersecurity with individual privacy
❌ Need for increased monitoring

Explanation:
Cybersecurity tools must protect systems while respecting user privacy and data rights.

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🧾 Summary Table

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