Evidence map›Paper›PMID 41654524›Full record

ArticleScientific reports2026

A generative AI-driven cybersecurity framework for small and medium enterprises software development: an ANN-ISM approach.

Mujtaba Awan, Abu Alam, Rafiq Ahmad Khan, Hathal Salamah Alwageed, Sarra Ayouni, Alaa Omran Almagrabi

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Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Mujtaba AwanSchool of Computing and Engineering, University of Gloucestershire, Cheltenham, UK.
Abu AlamSchool of Computing and Engineering, University of Gloucestershire, Cheltenham, UK.
Rafiq Ahmad KhanSoftware Engineering Research Group, Department of Computer Science and IT, University of Malakand, Chakdara, Pakistan. rafiqahmadk@gmail.com.
Hathal Salamah AlwageedCollege of Computer and Information Sciences, Jouf University, Sakaka, Saudi Arabia.
Sarra AyouniDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia. saayouni@pnu.edu.sa.
Alaa Omran AlmagrabiDepartment of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper presents an AI-based generative model to address the cybersecurity threats in software development for Small and Medium Enterprises (SMEs). The model aims to address the unique challenges SMEs face in implementing effective cybersecurity practices by leveraging generative AI to enhance threat detection, prevention, and response. Initially, we conducted a multivocal literature review (MLR) and an empirical survey to identify and validate cybersecurity threats and the generative AI practices used in secure software development for SMEs. An expert panel review was then assigned for the process of artificial neural network (ANN) and interpretive structural model (ISM). The ANN model can predict potential cybersecurity threats by learning from historical data and software development patterns. ISM is used to (1) structure and visualize (2) relations between identified threats and mitigation approaches and (3) offer a combined, multi-layered risk management methodology. A case study was conducted to evaluate the effectiveness of the proposed model. The evaluation has shown that the model significantly enhances SME online security and enables rapid adoption of sophisticated AI-based practices for detecting and responding to primary and advanced cyber threats. Phishing and ransomware received high assessments (Advanced), whereas some advanced techniques, e.g., AI-guided evasion and zero-day attacks, were at early stages of development (Understanding and Development). The general results indicated that generative AI can help organizations enhance SME cybersecurity, and some efforts are underway to develop use cases for advanced threats further. The AI-based generative model is a viable and scalable approach to the cybersecurity of SME software development. Such AI-based practices will enable SMEs to effectively protect themselves against various cyber threats systematically. Future studies should focus on developing contemporary threat strategies and on the impediments to global implementation, particularly in less resource-rich settings.

Indexed as

ANN-ISMCase studyCybersecurity threatsEmpirical surveyGenerative AI practicesMultivocal literature reviewSmall and medium enterprises (SMEs)Software development

Identifiers

PMID41654524
PMCPMC13018218

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.