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The article discusses the need for more extensive due diligence, oversight, and governance in using AI in cybersecurity, as highlighted by Deloitte's annual cyberthreat report. It points out the challenges posed by large language models (LLMs) in cybersecurity practices, with examples of ransomware and IoT malware attacks affecting organizations. Various strategies are proposed to mitigate the risks associated with LLMs, including adversarial training, explainability, continuous monitoring, human-in-the-loop approach, and sandboxing.
Adversarial training involves exposing LLMs to inputs that test their boundaries and induce malicious behavior, while explainability aims to provide insights into the decision-making process of LLMs. Continuous monitoring is crucial to detect anomalous LLM outputs, and involving humans in critical decision-making helps prevent overreliance on LLMs. Additionally, sandboxing and gradual deployment are recommended to thoroughly test LLMs before live deployment in real cybersecurity processes.
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