Which challenge is commonly associated with AI-powered incident response?

• A. Accuracy and trust issues, human oversight, evolving threat landscape, and AI biases.
• B. Guaranteed zero incidents.
• C. No training data required for models.
• D. AI can operate fully without any human input.

Answer: (A)

AI-powered incident response is only as good as the reliability of its decisions in real-world operations. The main idea tested is that practical uses of AI in incident response face real challenges around accuracy and trust, require ongoing human oversight, must adapt to a changing threat landscape, and can be affected by biases in the models. Models can misclassify alerts or miss threats, which undermines trust unless security teams can validate why a decision was made and see the reasoning behind it. Human oversight is essential to verify actions, handle edge cases, and intervene when risk is high. The threat landscape doesn’t stand still, so models need continuous updating and retraining to remain effective. Biases from training data or model behavior can skew prioritization or remediation efforts, leading to unfair or suboptimal outcomes. Together, these factors show why AI is best used to augment human analysts rather than replace them. The other options describe unattainable or incorrect ideas in this context: zero incidents cannot be guaranteed, no training data is required to build effective models, and AI cannot operate fully without human input in security operations.