Evaluating the Perceived Effectiveness of AI-Based Cybersecurity Tools in Finnish Financial Institutions
Chowdhury, Remon (2026)
2026
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-202605049257
https://urn.fi/URN:NBN:fi:amk-202605049257
Tiivistelmä
This thesis explores the usefulness of AI-based cybersecurity tools in financial institutions in Finland. The research explores the perceptions of cybersecurity professionals and other respondents regarding cybersecurity issues, AI-based cybersecurity solutions, and the value of these solutions in assisting with threat detection, monitoring and response.
The study employed quantitative research design, a positivist research philosophy and deductive approach. Data were gathered via a questionnaire from 50 respondents having cybersecurity-related knowledge or decision-making authority in Finnish financial institutions. The data were analyzed using SPSS through descriptive statistics, correlation analysis, regression analysis, and reliability testing.
The findings indicate that respondents perceived cybersecurity challenges in Finnish financial institutions as significant. AI-based cybersecurity tools were also rated positively, and respondents generally perceived them as useful for supporting cybersecurity activities. Correlation analysis showed positive and statistically significant relationships between the main variables. However, the regression results require cautious interpretation.
The thesis concludes that AI-based cybersecurity tools are perceived as valuable additions to cybersecurity practices in Finnish financial institutions, but the findings should not be interpreted as objective or causal proof of effectiveness.
The study employed quantitative research design, a positivist research philosophy and deductive approach. Data were gathered via a questionnaire from 50 respondents having cybersecurity-related knowledge or decision-making authority in Finnish financial institutions. The data were analyzed using SPSS through descriptive statistics, correlation analysis, regression analysis, and reliability testing.
The findings indicate that respondents perceived cybersecurity challenges in Finnish financial institutions as significant. AI-based cybersecurity tools were also rated positively, and respondents generally perceived them as useful for supporting cybersecurity activities. Correlation analysis showed positive and statistically significant relationships between the main variables. However, the regression results require cautious interpretation.
The thesis concludes that AI-based cybersecurity tools are perceived as valuable additions to cybersecurity practices in Finnish financial institutions, but the findings should not be interpreted as objective or causal proof of effectiveness.