Security in Generative AI Systems: A Comprehensive Literature Review

Resumen

Gen AI systems such as LLM’s, code assistants, image generators and personal assistants are growingly integrated with productivity and critical business applications across different domains of organizations such as security, testing, code review and automate day to day tasks. Their functionality depends on different training models, inputs and instructions and those models are integrated with various applications via API’s and connectors. In addition, there are many practical use cases where Generative AI LLM’s are already operationally embedded with many industrial equipment’s. This paper presents a detailed literature review of security, privacy concerns and compliance risks that are evolving day to day. This review refers to various published articles and subjects related to AI security from security perspective which addresses various security concerns specific to Gen AI and the need for evolution to adopt and comply with the evolving regulations. Also, the paper refers to various publications related to the role of AI in security, defense mechanisms to safeguard AI models and its applications, different approaches on risk management and so on. Based on these references, we also discussed the advantages and limitations of adopting NIST AI Risk management framework (AI-RMF) which is an adaptive governance guidance for Generative AI security at its lifecycle. The review finally highlights Assessment gaps and proposes a governance-tied adaptive security framework for Generative AI.