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Other literature type . 2025
License: CC BY
Data sources: ZENODO
ZENODO
Data Paper . 2025
License: CC BY
Data sources: Datacite
ZENODO
Data Paper . 2025
License: CC BY
Data sources: Datacite
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Employment Tax and Cybersecurity Risks: Threats of a Tax Season

Authors: Olubukola Sanni;

Employment Tax and Cybersecurity Risks: Threats of a Tax Season

Abstract

The convergence of employment tax systems and cybersecurity vulnerabilities poses significant challenges during the annual tax season, when digital transactions and sensitive data exchanges reach their peak. This paper examines the growing threats of cyberattacks targeting employment tax processes, with a focus on phishing schemes, ransomware, identity theft, and payroll data breaches. As governments and organisations increasingly adopt e-filing and cloud-based payroll systems, cybercriminals exploit weak authentication protocols and human error to gain unauthorised access to taxpayer information. The study explores how the complexity of employment tax compliance amplifies risk exposure, particularly in small and medium-sized enterprises with limited cybersecurity infrastructure. Using recent data and case studies, the research highlights how fraudulent tax filings and employer impersonation have become prevalent attack vectors. Moreover, it evaluates the role of artificial intelligence and machine learning in detecting anomalies within digital tax filings and enhancing preventive controls. The findings underscore the need for a multi-layered security strategy, combining employee awareness, regulatory compliance, and advanced encryption, to mitigate cyber threats during tax season. The paper concludes by emphasising the importance of continuous monitoring, cross-sector collaboration, and integrating cybersecurity frameworks into tax administration systems to ensure data integrity, taxpayer confidence, and a sustainable digital transformation.

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    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green