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Related Experiment Video

Updated: Sep 19, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Published on: December 6, 2024

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Generative AI cybersecurity and resilience.

Petar Radanliev1,2, Omar Santos3, Uchenna Daniel Ani4

  • 1Department of Computer Sciences, University of Oxford, Oxford, United Kingdom.

Frontiers in Artificial Intelligence
|June 18, 2025
PubMed
Summary
This summary is machine-generated.

Generative Artificial Intelligence (AI) offers powerful content synthesis but poses significant ethical and security risks. This study highlights the gap between rapid AI adoption and governance, urging adaptive, sector-specific strategies for responsible AI deployment.

Keywords:
cybersecuritydata ethicsgenerative artificial intelligencepolicy developmentresponsible AI deploymentshadow AI

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Published on: December 6, 2024

693

Area of Science:

  • Computer Science and Engineering
  • Information Science
  • Cybersecurity and Ethics

Background:

  • Generative Artificial Intelligence (AI) systems enable autonomous content creation across diverse domains, representing a significant advancement in machine learning.
  • The rapid evolution and deployment of generative AI present substantial ethical, security, and privacy challenges that current governance frameworks struggle to address.
  • Existing socio-technical and governance models require adaptation to effectively manage the implications of advanced AI technologies.

Purpose of the Study:

  • To systematically investigate the ethical, security, and privacy challenges posed by generative AI deployment.
  • To develop an integrated theoretical framework for evaluating and guiding the responsible application of generative AI across its lifecycle.
  • To identify the disconnect between generative AI adoption and institutional safeguard maturity, and propose adaptive governance solutions.

Main Methods:

  • A PRISMA-guided systematic literature review was conducted to gather relevant research on generative AI challenges.
  • Thematic and quantitative analyses were employed to interrogate the socio-technical implications of generative AI.
  • An integrated theoretical framework was developed, drawing on technology adoption, cybersecurity resilience, and normative governance models.

Main Results:

  • A significant gap exists between the rapid adoption of generative AI systems and the development of mature institutional safeguards.
  • New risks, including those associated with 'shadow Artificial Intelligence,' emerge due to inadequate governance.
  • The study identifies a need for adaptive, sector-specific governance strategies to address the unique challenges of generative AI.

Conclusions:

  • Responsible deployment of generative AI requires a proactive and adaptive governance approach.
  • The proposed five-stage lifecycle framework (design, implementation, monitoring, compliance, feedback) provides a schema for ethical and secure AI application.
  • Implementing sector-specific, adaptive governance is crucial for mitigating risks and ensuring the secure application of AI in critical infrastructure.