SecGenAI: Enhancing Security of Cloud-based Generative AI Applications within Australian Critical Technologies of National Interest

• URL: http://arxiv.org/abs/2407.01110v1
• Date: Mon, 1 Jul 2024 09:19:50 GMT
• Title: 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 summary: SecGenAI is a comprehensive security framework for cloud-based GenAI applications. Aligned with Australian Privacy Principles, AI Ethics Principles, and guidelines from the Australian Cyber Security Centre and Digital Transformation Agency.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Securing the Future of GenAI: Policy and Technology [50.586585729683776]
  Governments globally are grappling with the challenge of regulating GenAI, balancing innovation against safety. A workshop co-organized by Google, University of Wisconsin, Madison, and Stanford University aimed to bridge this gap between GenAI policy and technology. This paper summarizes the discussions during the workshop which addressed questions, such as: How regulation can be designed without hindering technological progress?
  arXiv  Detail & Related papers  (2024-05-21T20:30:01Z)
• Generative AI for Secure and Privacy-Preserving Mobile Crowdsensing [74.58071278710896]
  generative AI has attracted much attention from both academic and industrial fields. Secure and privacy-preserving mobile crowdsensing (SPPMCS) has been widely applied in data collection/ acquirement.
  arXiv  Detail & Related papers  (2024-05-17T04:00:58Z)
• Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems [88.80306881112313]
  We will introduce and define a family of approaches to AI safety, which we will refer to as guaranteed safe (GS) AI. The core feature of these approaches is that they aim to produce AI systems which are equipped with high-assurance quantitative safety guarantees. We outline a number of approaches for creating each of these three core components, describe the main technical challenges, and suggest a number of potential solutions to them.
  arXiv  Detail & Related papers  (2024-05-10T17:38:32Z)
• Artificial Intelligence as the New Hacker: Developing Agents for Offensive Security [0.0]
  This paper explores the integration of Artificial Intelligence (AI) into offensive cybersecurity. It develops an autonomous AI agent, ReaperAI, designed to simulate and execute cyberattacks. ReaperAI demonstrates the potential to identify, exploit, and analyze security vulnerabilities autonomously.
  arXiv  Detail & Related papers  (2024-05-09T18:15:12Z)
• Generative AI in Cybersecurity [0.0]
  Generative Artificial Intelligence (GAI) has been pivotal in reshaping the field of data analysis, pattern recognition, and decision-making processes. As GAI rapidly progresses, it outstrips the current pace of cybersecurity protocols and regulatory frameworks. The study highlights the critical need for organizations to proactively identify and develop more complex defensive strategies to counter the sophisticated employment of GAI in malware creation.
  arXiv  Detail & Related papers  (2024-05-02T19:03:11Z)
• Security Risks Concerns of Generative AI in the IoT [9.35121449708677]
  In an era where the Internet of Things (IoT) intersects increasingly with generative Artificial Intelligence (AI), this article scrutinizes the emergent security risks inherent in this integration. We explore how generative AI drives innovation in IoT and we analyze the potential for data breaches when using generative AI and the misuse of generative AI technologies in IoT ecosystems.
  arXiv  Detail & Related papers  (2024-03-29T20:28:30Z)
• Review of Generative AI Methods in Cybersecurity [0.6990493129893112]
  This paper provides a comprehensive overview of the current state-of-the-art deployments of Generative AI (GenAI) It covers assaults, jailbreaking, and applications of prompt injection and reverse psychology. It also provides the various applications of GenAI in cybercrimes, such as automated hacking, phishing emails, social engineering, reverse cryptography, creating attack payloads, and creating malware.
  arXiv  Detail & Related papers  (2024-03-13T17:05:05Z)
• A Zero Trust Framework for Realization and Defense Against Generative AI Attacks in Power Grid [62.91192307098067]
  This paper proposes a novel zero trust framework for a power grid supply chain (PGSC) It facilitates early detection of potential GenAI-driven attack vectors, assessment of tail risk-based stability measures, and mitigation of such threats. Experimental results show that the proposed zero trust framework achieves an accuracy of 95.7% on attack vector generation, a risk measure of 9.61% for a 95% stable PGSC, and a 99% confidence in defense against GenAI-driven attack.
  arXiv  Detail & Related papers  (2024-03-11T02:47:21Z)
• Generative AI for Secure Physical Layer Communications: A Survey [80.0638227807621]
  Generative Artificial Intelligence (GAI) stands at the forefront of AI innovation, demonstrating rapid advancement and unparalleled proficiency in generating diverse content. In this paper, we offer an extensive survey on the various applications of GAI in enhancing security within the physical layer of communication networks. We delve into the roles of GAI in addressing challenges of physical layer security, focusing on communication confidentiality, authentication, availability, resilience, and integrity.
  arXiv  Detail & Related papers  (2024-02-21T06:22:41Z)
• Deepfakes, Misinformation, and Disinformation in the Era of Frontier AI, Generative AI, and Large AI Models [7.835719708227145]
  Deepfakes and the spread of m/disinformation have emerged as formidable threats to the integrity of information ecosystems worldwide. We highlight the mechanisms through which generative AI based on large models (LM-based GenAI) craft seemingly convincing yet fabricated contents. We introduce an integrated framework that combines advanced detection algorithms, cross-platform collaboration, and policy-driven initiatives.
  arXiv  Detail & Related papers  (2023-11-29T06:47:58Z)
• Managing extreme AI risks amid rapid progress [171.05448842016125]
  We describe risks that include large-scale social harms, malicious uses, and irreversible loss of human control over autonomous AI systems. There is a lack of consensus about how exactly such risks arise, and how to manage them. Present governance initiatives lack the mechanisms and institutions to prevent misuse and recklessness, and barely address autonomous systems.
  arXiv  Detail & Related papers  (2023-10-26T17:59:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.