Generative AI in security contexts can be used for which of the following?

• A. Creating new content for detection rules and also for polymorphic malware or deepfakes.
• B. Replacing all password authentication with static tokens.
• C. Performing only data encryption and decryption.
• D. Managing hardware asset inventory.

Answer: (A)

Generative AI thrives on creating new content by learning patterns from data, which is especially useful in security where threats constantly evolve. In defense, it can help draft and refine detection rules, signatures, and playbooks, enabling security teams to respond more quickly to emerging techniques. It can also be misused to generate attacker-like content, such as polymorphic malware variants or realistic deepfakes, which security teams must anticipate and defend against. This dual capability—assisting defenders with fresh detection content while illustrating potential attacker methods—is why this option best captures how generative AI can be applied in security contexts.

The other options aren’t aligned with what generative AI is typically used for: replacing authentication with static tokens is a flawed approach to identity security and not a function of generative models; encryption and decryption are cryptographic tasks rather than generation-based tasks; and asset inventory management is more about asset tracking and inventory systems than about generating new security content.