SECURING GENERATIVE AI DEPLOYMENTS: IDENTITY AND ACCESS MANAGEMENT STRATEGIES FOR SCALABLE LLM INTEGRATION

Paper ID: EIJTEM_2024_11_4_79-93

Author's Name: Ravi Kumar

Volume: 11

Issue: 4

Year: 2024

Page No: 79-93

Abstract:

As large language models increasingly embed themselves into aspects of daily life, an even stronger IAM strategy will become necessary in securing and scaling those systems. This paper builds on securing generative AI deployments, keeping the focus on integration challenges with scalable LLMs, and its IAM solutions. We explore key areas such as hardware security, intrusion detection, programming safeguards, and model identification while placing particular emphasis on managing access control and authentication processes within LLM environments. The paper offers an in-depth analysis of 42 different architectures of LLMs, which include GPT-4o, GPT-4, GPT-3.5, Mixtral8x7B, BERT, Falcon2, Gemma, Phi-2, Phi-3, and LLaMA, with the objective of evaluation of their capabilities in the secure user identity management and safe interactions among diverse AI-driven applications. We address the vulnerabilities of such models, which include prompt injection attacks, unauthorized output generation, leakage of information, susceptibility to attacks such as DDoS and malicious command execution, etc. Finally, we critically review the IAM solutions specifically for the integration of LLM, pointing out the necessity of data access policies, authentication protocols, and preventing mechanisms of privilege escalation. We focus specifically on more advanced IAM techniques, such as Zero Trust Architecture (ZTA), multi-factor authentication (MFA), and fine-grained access control models. Innovative approaches, such as Half-Quadratic Quantization (HQQ), reinforced learning with Human Feedback (RLHF), and Retrieval-Augmented Generation (RAG), are also discussed to strengthen LLM security while ensuring scalability. With its strategic insights and examination of how IAM is applied in the secure and efficient deployment of AI models in future AI-driven environments to address the challenges of ever-changing cybersecurity, this research offers grounds for laying the foundation to facilitate the future AI-driven deployment.

Keywords: Generative AI, Large Language Models (LLMs), Identity and Access Management, Scalability and Zero Trust Architecture.

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