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Contextualized AI for Cyber Defense: An Automated Survey using LLMs
Contextualized AI for Cyber Defense: An Automated Survey using LLMs
Authors: Christoforus Yoga Haryanto, Anne Maria Elvira, Trung Duc Nguyen, Minh Hieu Vu, Yoshiano Hartanto, Emily Lomempow, Arathi Arakala
Abstract: This paper surveys the potential of contextualized AI in enhancing cyber defense capabilities, revealing significant research growth from 2015 to 2024. We identify a focus on robustness, reliability, and integration methods, while noting gaps in organizational trust and governance frameworks. Our study employs two LLM-assisted literature survey methodologies: (A) ChatGPT 4 for exploration, and (B) Gemma 2:9b for filtering with Claude 3.5 Sonnet for full-text analysis. We discuss the effectiveness and challenges of using LLMs in academic research, providing insights for future researchers.
How Contextualised AI Are Reshaping Personal Branding
How Contextualised AI Are Reshaping Personal Branding
Authors: Yulia Nurliana, Christoforus Yoga Haryanto, Jessica Helmi, Argho Bandyopadhyay
Abstract: As rapidly evolving digital landscape necessitates personalised brand interactions and further advances artificial intelligence (AI) use in marketing, this exploratory study explores contextualised AI integration in personal branding, addressing effectiveness and ethical considerations. A preliminary literature review synthesises insights from AI, computer science, and digital marketing literature. Findings indicate contextualised AI significantly improves AI-generated content relevance and engagement while raising unique ethical challenges. We propose interdisciplinary approaches, impact measurement frameworks, academia-industry collaboration, ethical AI research support, and enhanced AI-generated content accessibility. These strategies aim to optimise the use of contextualised AI in personal branding and address ethical concerns, providing a roadmap for future research and application.
LLAssist: Simple Tools for Automating Literature Review Using Large Language Models
LLAssist: Simple Tools for Automating Literature Review Using Large Language Models
Authors: Christoforus Yoga Haryanto
Abstract: This paper introduces LLAssist, an open-source tool designed to streamline literature reviews in academic research. In an era of exponential growth in scientific publications, researchers face mounting challenges in efficiently processing vast volumes of literature. LLAssist addresses this issue by leveraging Large Language Models (LLMs) and Natural Language Processing (NLP) techniques to automate key aspects of the review process. Specifically, it extracts important information from research articles and evaluates their relevance to user-defined research questions. The goal of LLAssist is to significantly reduce the time and effort required for comprehensive literature reviews, allowing researchers to focus more on analyzing and synthesizing information rather than on initial screening tasks. By automating parts of the literature review workflow, LLAssist aims to help researchers manage the growing volume of academic publications more efficiently.
SecGenAI: Enhancing Security of Cloud-based Generative AI Applications within Australian Critical Technologies of National Interest
SecGenAI: Enhancing Security of Cloud-based Generative AI Applications within Australian Critical Technologies of National Interest
Authors: Christoforus Yoga Haryanto, Minh Hieu Vu, Trung Duc Nguyen, Emily Lomempow, Yulia Nurliana, Sona Taheri
Abstract: The rapid advancement of Generative AI (GenAI) technologies offers transformative opportunities within Australia's critical technologies of national interest while introducing unique security challenges. This paper presents SecGenAI, a comprehensive security framework for cloud-based GenAI applications, with a focus on Retrieval-Augmented Generation (RAG) systems. SecGenAI addresses functional, infrastructure, and governance requirements, integrating end-to-end security analysis to generate specifications emphasizing data privacy, secure deployment, and shared responsibility models. Aligned with Australian Privacy Principles, AI Ethics Principles, and guidelines from the Australian Cyber Security Centre and Digital Transformation Agency, SecGenAI mitigates threats such as data leakage, adversarial attacks, and model inversion. The framework's novel approach combines advanced machine learning techniques with robust security measures, ensuring compliance with Australian regulations while enhancing the reliability and trustworthiness of GenAI systems. This research contributes to the field of intelligent systems by providing actionable strategies for secure GenAI implementation in industry, fostering innovation in AI applications, and safeguarding national interests.
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