mlsanityData to 7 Oct 2026

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

arXiv 2407.01110 ↗First posted 1 Jul 2024cs.CR cs.AI cs.CYcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 27 of its 91 references that are in the map. First posted on arXiv 1 Jul 2024.

Builds on

27 of 91 references.

References · 27

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  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
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  7. 2016Deep Learning with Differential Privacycited by 2,682
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  12. 2024Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexitycited by 437
15 more
  1. 2023A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Uglycited by 380
  2. 2019The Secret Revealer: Generative Model-Inversion Attacks Against Deep Neural Networkscited by 241
  3. 2020Artificial Fingerprinting for Generative Models: Rooting Deepfake Attribution in Training Datacited by 200
  4. 2020Information Leakage in Embedding Modelscited by 199
  5. 2022Do Users Write More Insecure Code with AI Assistants?cited by 186
  6. 2024The Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG)cited by 159
  7. 2022Internet-augmented language models through few-shot prompting for open-domain question answeringcited by 131
  8. 2020Adversarial Machine Learning -- Industry Perspectivescited by 121
  9. 2023Sentence Embedding Leaks More Information than You Expect: Generative Embedding Inversion Attack to Recover the Whole Sentencecited by 81
  10. 2023GenAI Against Humanity: Nefarious Applications of Generative Artificial Intelligence and Large Language Modelscited by 40
  11. 2019CT-GAN: Malicious Tampering of 3D Medical Imagery using Deep Learningcited by 31
  12. 2023A Study on the Implementation of Generative AI Services Using an Enterprise Data-Based LLM Application Architecturecited by 28
  13. 2024Generative AI for Unmanned Vehicle Swarms: Challenges, Applications and Opportunitiescited by 14
  14. 2021AI Ethics Principles in Practice: Perspectives of Designers and Developerscited by 12
  15. 2024Generative AI Security: Challenges and Countermeasurescited by 4

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Cited by year

Citing papers per year: 2024: 1; 2025: 1.

2 papers.

  1. 2025Securing RAG: A Risk Assessment and Mitigation Frameworkcited by 3
  2. 2024Contextualized AI for Cyber Defense: An Automated Survey using LLMscited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2024Enhancing Security Control Production With Generative AI
  2. 2026From Alignment to Access Control: A Framework for GenAI Policy Enforcement
  3. 2025Securing Generative AI Agentic Workflows: Risks, Mitigation, and a Proposed Firewall Architecture
  4. 2023Identifying and Mitigating the Security Risks of Generative AI
  5. 2026Privilege and confidentiality in generative AI workflows
  6. 2025Rethinking Data Protection in the (Generative) Artificial Intelligence Era
  7. 2026Towards Secure Retrieval-Augmented Generation: A Comprehensive Review of Threats, Defenses and Benchmarks
  8. 2025Provably Secure Retrieval-Augmented Generation