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Five strategies for mitigating LLM risks in cybersecurity apps – SC Media


Today’s columnist, Aqsa Taylor of Gutsy, offers five strategies for mitigating LLM risks. (Adobe Stock)
While most CISOs and CIOs have created AI policies, it’s become clear that more extensive due diligence, oversight, and governance are required for the use of AI in a cybersecurity context. According to Deloitte’s annual cyberthreat report, 66% of organizations suffered ransomware attacks. There was also a 400% increase in IoT malware attacks. And in 2023 91% of organizations had to remediate a supply chain attack affecting their code or systems they used.
That’s because the long-standing cybersecurity practices that worked in the past, haven’t caught up to the capabilities and threats presented by large language models (LLMs). These LLMs trained on vast quantities of data can make both security operations teams, and the threats they’re trying to mitigate, smarter. Because LLMs are different from other security tools, we need to adopt a different set of approaches to mitigate their risks. Some involve new security technologies. Others are tried-and-true tactics modified for LLMs. These include:

LLMs have introduced a greater risk of the unexpected, and so, we should closely monitor their integration, usage and maintenance protocols. Once the CISO has been satisfied that an LLM is safe enough and effective, they can proceed with a gradual and methodical deployment. For best result, deploy the LLM initially for less critical and complex tasks and slowly introduce it into the most cognitively challenging workflows and processes that call for good judgment by humans.
Aqsa Taylor, director of product management, Gutsy

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