Generative AI significantly enhances cybersecurity incident response by automating complex cognitive and procedural tasks. It can analyze incident data, threat intelligence, and organizational context in real-time to dynamically generate tailored response playbooks. This moves beyond static, pre-defined plans. Furthermore, GenAI integrates with Security Orchestration, Automation, and Response (SOAR) platforms to orchestrate the execution of these playbooks, automating actions like isolating affected systems, blocking malicious IPs, and coordinating communication between security teams. This automation accelerates the entire incident lifecycle, from detection and analysis to containment and recovery, minimizing the impact of security breaches.

A. The primary goal of using AI in incident response is to accelerate analysis and reduce response times, not delay them.

C. This option describes a manual process and is the antithesis of leveraging GenAI for automation and assistance.

D. While GenAI can assist in generating post-incident reports, its contribution is far broader, with its most significant impact being in real-time analysis and response automation.

1. National Institute of Standards and Technology (NIST). (2023). AI Risk Management Framework (AI RMF 1.0).

Reference: Section 3.3

"Govern

" discusses mapping

measuring

and managing risks in AI systems. The framework's principles support using AI to automate and improve the consistency and speed of complex processes like incident response orchestration

which is a form of risk management.

2. Annas

G.

& Stähle

M. (2023). Generative AI in Cybersecurity: A Review of Threats

and Opportunities. In Proceedings of the 18th International Conference on Availability

Reliability and Security (ARES 2023).

Reference: Section 4

"Opportunities of Generative AI in Cybersecurity

" explicitly details the use of GenAI for "Automated Incident Response

" including the generation of response plans and the automation of mitigation steps

which directly supports the concept of automated playbook generation and orchestration.

DOI: https://doi.org/10.1145/3600160.3605262

3. MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). (2024). AI for Cybersecurity Initiative.

Reference: Research publications and project descriptions from this initiative frequently cover the application of AI to automate security operations (SecOps). The core concept involves using AI models to synthesize data from multiple sources and recommend or automate response actions

aligning with the answer. (e.g.

Research on the AI-Cybersecurity nexus).