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Cited by 12Information and Cyber SecurityPrivacy, Security, and Data ProtectionDigitalization, Law, and Regulation

The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.

Data Security for Connected Governments and Organisations: Managing Automation and Artificial Intelligence

Susanto, Heru, Yie, Leu Fang, Rosiyadi, Didi, Basuki, Akbari Indra, Setiana, Desi·2021·Advances in Electronic Government, Digital Divide, and Regional Development
DOI 10.4018/978-1-7998-4570-6.ch011
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Abstract

Digital ecosystems have grown rapidly over the years, and governments are investing in digital provision for their processes and services. Despite the advantages of distributed technologies, there are many security issues as well that result in breaches of data privacy with serious impact including legal and reputational implications. To deal with such threats, government agencies need to thoughtfully improve their security defences to protect data and systems by using automation and artificial intelligence (AI), as well as easing the data security measures including early warning of threats and detection. This study provides a comprehensive view of AI and automaton to highlight challenges and issues concerning data security and suggests steps to combat the issues. The authors demonstrate the role of AI-driven security tools and automation to mitigate the impact of data breaches to also propose recommendations for government agencies to enhance their data security protection.

Overview

Integrity signals

Registry

Crossref · 10.4018/978-1-7998-4570-6.ch011

Cites

8 of 8 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Fine-tuning has two primary approaches: end-to-end fine-tuning, which adjusts all weights of encoder and classifier, and feature-extraction fine-tuning, where encoder weights are frozen to extract features for a downstream classifier.

    “Its two primary approaches are end-to-end fine-tuning, which adjusts all weights of the encoder and classifier [260], [261], and feature-extraction fine-tuning, where the encoder weights are frozen to extract features for a downstream classifier [262], [263], [264].”

    From From Google Gemini to OpenAI Q* (Q-Star): A Survey on Reshaping the Generative Artificial Intelligence (AI) Research Landscape (2025) · cites this paper as [260]

Citations

Cites

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Research works printed but not linked: 1

  1. Stavros, J. & Hinrichs, G. (2009). The thin book of SOAR: Building strengths-based strategy. Academic Press.

Cited by

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Related

Metadata

Authors
Susanto, H., Yie, L. F., Rosiyadi, D., Basuki, A. I., & Setiana, D.
Affiliations
  • Indonesian Institute of Sciences (ID)
  • Tunghai University (TW)
  • Universiti Brunei Darussalam (BN)
ORCID
Venue
Advances in Electronic Government, Digital Divide, and Regional Development
Publisher
IGI Global
Published
2021
Pages
229-251
ISSN
  • 2326-9103 (ISSN-L)
  • 2326-9111
DOI
10.4018/978-1-7998-4570-6.ch011
OpenAlex
W3046812357
Cited by
12
References
8
Citations by year
2026: 1 · 2025: 2 · 2024: 1 · 2022: 5 · 2021: 3
FWCI
5.34
Type
book chapter
Language
en
Open access
closed
Locations
Advances in electronic government, digital divide, and regional development book series (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Information Systems › Information and Cyber Security
  • Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
  • Social Sciences › Law › Digitalization, Law, and Regulation
Keywords
data security, government agencies, data breaches, data privacy, artificial intelligence for security, automation, threat detection, early warning, digital ecosystems, e-government

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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