Factors Affecting MFA in Cloud Environments : A Case Study on Azure and AWS

Sammanfattning

This study examines Multi-Factor Authentication (MFA) implementation and effectiveness determinants in cloud computing, focused on Microsoft Azure and Amazon Web Services (AWS). Using an interpretivist paradigm to shed insight into subjective experience and inductively building theory from data, the research examines technical issues like system integration, user behaviors like adoption barriers, security requirements like policies, and expenses like costs on deployments. This study surveyed IT professionals with a quantitative Google Forms-based survey of 47 members. The questionnaire included 20 instruments across the 5-point Likert scale (1: Strongly Disagree through 5: Strongly Agree) gauging perception across the four sets of factors. The neutral midpoint was added in order to reveal ambivalence, reduce response bias, and improve the precision of the data, although potential central tendency bias was addressed through concise and pilot-tested item wordings. Data analysis utilized IBM SPSS Statistics with Cronbachs Alpha measuring internal consistency (0.499 for technical factors—marginally acceptable reliability—to 0.798 for cost-resource factors, with strong reliability), Kaiser-Meyer-Olkin (KMO = 0.680, appropriate for factor analysis),and Bartletts Test of Sphericity (p ¡ 0.001, verifying associations). Pearson correlation coefficients explored relations among factors, and multiple linear regression tested MFA effectiveness. Results indicated no one factor significantly predicted MFA effectiveness (all p ¿ 0.05), but the combined model was significant (F(3,43) = 6.282, p = 0.001), explaining 30.5. The study finds that the effectiveness of MFA in the cloud is contingent upon strategic planning, organizational agreement, user training, and equal funding. IT professionals, security managers, and cloud designers receive practical guidance in how to hone MFA infrastructures, improving defense against cyber assault in Azureand AWS. The limitations include the small sample size, which reduces generalizability, and the use of qualitative reports instead of objective measures. Areas of future research involve the application of larger, variegated sample sizes, mixed approaches within further insight, or long-term studies in the tracking of MFA over time.