Organizations are being urged by cybersecurity leaders to implement safeguards before deploying generative AI tools in the workplace to address significant security risks, including concerns about using Large Language Models (LLMs) in security operations [1].


One major risk is prompt injection attacks on LLMs [2], which can lead to malicious activities such as extracting private account details from customer service chatbots [2]. AI adoption is increasing in organizations [2], often without proper training and management of LLMs [2], which could result in biases and potential damage to the business [2]. Challenges of using LLMs out-of-box include the need to supply specific data and limitations on token limits [1]. Concerns about sending private data to LLMs include the risk of exposure and regulatory compliance [1]. Lessons learned from building an AI chatbot highlight vulnerabilities such as prompt injection and hallucination in LLM applications [1], emphasizing the importance of security best practices and output verification in AI tools [1]. Security leaders are observing AI creeping into various tools and services procured by businesses [2], raising concerns about the lack of awareness regarding AI usage [2]. Organizations need to be cautious in training internal models [2], ensuring broad data usage to prevent biases and prompt attacks [2]. Understanding and controlling the flow of data in AI tools throughout the enterprise is crucial to mitigate security risks [2]. Deployment of AI without proper data identity and classification tasks can lead to unintended exposure or breach of training data and corporate secrets [2], emphasizing the need for thorough processes before rolling out AI within organizations [2].


It is crucial for organizations to address security risks associated with the deployment of generative AI tools, particularly in relation to Large Language Models. By implementing safeguards [2], training internal models effectively [2], and controlling the flow of data [2], organizations can mitigate potential biases, prompt attacks [2], and breaches of sensitive information. Looking ahead, a proactive approach to AI security is essential to safeguard business operations and protect against emerging threats.