Integrating Generative AI (GenAI) and LLMs into high-interaction Honeypots allows security teams to create dynamic, realistic environments that can…

The primary defensive benefit of integrating Generative AI into high-interaction honeypots is enhancing their realism to deceive attackers. GenAI can dynamically adjust persona-based responses, creating believable, non-repetitive interactions that mimic legitimate users or systems, making the trap harder to identify. It also excels at generating large volumes of realistic synthetic data (e.g., files, logs, network traffic) to populate the honeypot environment. This increases the attacker's dwell time, as they are more convinced of the system's legitimacy, allowing defenders to gather more extensive intelligence on their tactics, techniques, and procedures (TTPs).

A. Automatic application of Model Watermarking to all outbound traeic: Watermarking is for tracing AI-generated content origin, not a primary defensive benefit of a honeypot's interaction with an attacker.

D. Reduction of Vector Database latency for faster model retraining: This is a backend performance optimization. It does not directly contribute to the honeypot's defensive capability of deceiving an attacker.

1. Weber, S., et al. (2023). HoneyGen: A generative-AI-based honeypot framework. 2023 IEEE International Conference on Cyber Security and Resilience (CSR), pp. 465-470. DOI: 10.1109/CSR57506.2023.10224999. (Describes using GenAI to create dynamic and believable honeypot content to increase attacker engagement).

2. Fang, Y., et al. (2023). LLM-based high-interaction honeypot for industrial control systems. Computers & Security, 135, 103491. DOI: 10.1016/j.cose.2023.103491. (Highlights the use of LLMs to generate realistic responses and simulate complex interactions, thereby increasing attacker dwell time).

3. Brown, T., et al. (2020). Language Models are Few-Shot Learners. Advances in Neural Information Processing Systems 33. (The underlying capabilities of LLMs described in this foundational paper enable the generation of realistic, context-aware text and data, which is the basis for options B and C).