AuditNet: A Conversational AI-based Security Assistant [DEMO]
This addresses the challenge of ensuring compliance with standards and regulations for professionals in fields like IoT network security, though it appears incremental as it applies existing retrieval-augmented generation methods to a new application area.
The paper tackles the problem of complex compliance checking across diverse domains by proposing AuditNet, a conversational AI assistant framework that automates review and retrieval of relevant information, reducing manual effort and enhancing accuracy and efficiency for professionals.
In the age of information overload, professionals across various fields face the challenge of navigating vast amounts of documentation and ever-evolving standards. Ensuring compliance with standards, regulations, and contractual obligations is a critical yet complex task across various professional fields. We propose a versatile conversational AI assistant framework designed to facilitate compliance checking on the go, in diverse domains, including but not limited to network infrastructure, legal contracts, educational standards, environmental regulations, and government policies. By leveraging retrieval-augmented generation using large language models, our framework automates the review, indexing, and retrieval of relevant, context-aware information, streamlining the process of verifying adherence to established guidelines and requirements. This AI assistant not only reduces the manual effort involved in compliance checks but also enhances accuracy and efficiency, supporting professionals in maintaining high standards of practice and ensuring regulatory compliance in their respective fields. We propose and demonstrate AuditNet, the first conversational AI security assistant designed to assist IoT network security experts by providing instant access to security standards, policies, and regulations.