Cybersecurity has become a critical concern for organizations as the increasing use of digital technologies, cloud computing, remote work, and interconnected information systems has led to a rise in cyber threats and data breaches. While organizations invest heavily in advanced security technologies, employee awareness and compliance with cybersecurity policies remain essential for protecting organizational information assets. This study examines cybersecurity awareness and employee compliance behavior at Wipro Limited by evaluating employees knowledge of cybersecurity practices, adherence to security policies, and the factors influencing secure workplace behavior. The research focuses on key aspects such as password management, phishing awareness, secure internet usage, data protection, incident reporting, cybersecurity training, and organizational support. A descriptive research design was adopted using both primary and secondary data. Primary data were collected through structured questionnaires administered to employees, while secondary data were obtained from company reports, academic journals, books, industry publications, and credible online sources related to cybersecurity and information security management. The collected data were analyzed using percentage analysis, mean analysis, and graphical representations to assess the level of cybersecurity awareness and compliance behavior among employees. The findings indicate that regular cybersecurity training, continuous awareness programs, management support, and clearly defined security policies significantly improve employees compliance with organizational security practices and reduce the likelihood of cyber incidents. However, challenges such as evolving cyber threats, human error, social engineering attacks, insufficient awareness, and varying levels of digital literacy continue to affect organizational cybersecurity. The study concludes that fostering a strong cybersecurity culture through continuous education, policy enforcement, employee engagement, and proactive risk management is essential for enhancing cybersecurity awareness, strengthening compliance behavior, and improving the overall security posture of Wipro Limited.
Domakonda Vamshi Krishna, K.Shashidhar, Srilekha Rageru· International Journal of AI...· 0 citations
This study, titled "Stock Market Price Prediction Using Machine Learning Algorithms for Investment Decision-Making," evaluates the economic viability and operational efficiency of implementing algorithmic prediction models in retail and institutional portfolio management. In modern financial markets, predicting stock prices is highly challenging due to nonstationarity, noise, and complex nonlinear relationships. This research investigates the implementation of machine learning algorithms—specifically Linear Regression, Support Vector Regression (SVR), Random Forest, and Long Short-Term Memory (LSTM) Networks—for real-time price forecasting. The study conducts a cost-benefit analysis of the technological investment, evaluating capital expenditure, operational maintenance costs, and the net financial benefit of stock price forecasting and portfolio optimization from 2021 to 2025. Standard financial appraisal metrics—Net Present Value (NPV), Internal Rate of Return (IRR), Payback Period (PBP), and Benefit-Cost Ratio (BCR)—are applied to determine the long-term profitability of the investment. The empirical analysis indicates that the LSTM model achieved the highest prediction accuracy, with a Mean Absolute Percentage Error (MAPE) of only 2.1%. An ML-optimized portfolio generated consistent excess returns (alpha) over the Nifty 50 Index across the five-year planning horizon, yielding a positive NPV of 392.4 Crores and an IRR of 45.1%. The study concludes that the integration of machine learning algorithms into investment decision-making processes represents a highly viable and financially feasible strategy for modern banking and asset management portfolios, delivering significant risk-adjusted financial returns.
Mukkala Aravind, M. Prasad, Srilekha Rageru· American Journal of AI Cyber...· 0 citations
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