Which factor enables AI to improve incident response decision-making?

• A. Predictive analytics
• B. Manual-only processing
• C. Random guesswork
• D. Static rules

Answer: (A)

AI enhances incident response decision-making by using predictive analytics to forecast how an incident will unfold and which actions will be most effective. By analyzing historical incidents, current alerts, logs, and indicators of compromise, predictive analytics builds models that estimate the likelihood of different outcomes, such as propagation, impact, or time to containment. This allows incident responders to prioritize cases by risk, allocate resources where they’ll have the most impact, and recommend proactive steps rather than reacting after the fact. The approach continuously improves as more data is collected, so assessments become more accurate over time.

Other approaches are less effective because they rely on rigid rules or guesswork. Static rules don’t adapt to new attackers or evolving environments, and manual-only processing can be slow and limited by human workload. Random guesswork is unreliable and risky in high-stakes incident response. Predictive analytics, in contrast, leverages data-driven insights to guide decisions with quantified confidence, making response faster and more strategic.