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Purpose: Small and medium enterprises (SMEs) are the most significant contributors to maximum employment generation, the gross domestic product (GDP) of many countries, and the overall global economy. It is also evident that cyber threats are becoming a big challenge for SMEs, which is directly impacting global economy.

Methodology: Existing research inputs were accessed to understand current cyber threats for SMEs and their cybersecurity posture. Additionally, this research has collected the latest insights by taking direct inputs from SMEs and conducting a well-designed research survey. It has provided a few direct inputs to designing solutions for the SME segment. For analysis and recommendations, cybersecurity best practices and core cybersecurity concepts are considered at the centre of the solution.

Findings: Implementing existing cybersecurity standards or frameworks is not easy for SMEs, as they generally have limited resources and different priorities for their business when it comes to the implementation of any cybersecurity controls. Currently, many cybersecurity standards are not able to support the implementation of business domain-specific controls.

Practical implications: Along with the research findings shared in this chapter, as a resolution to the problems faced by SMEs, the authors will propose a new framework as a solution. This framework is designed using core concepts of cybersecurity such as confidentiality, integrity, and availability (CIA triad) as well as defence in depth (DiD) mechanisms in each layer of organisation. The authors will also share a high-level idea about how reliable artificial intelligence-based software can help identify recommended controls for particular SMEs.

Background: Human resource management (HRM) is the tactical method for a business enterprise’s optimistic and systemic administration. This study aims to identify the common and major triggering attributes and the knowledge gap between HRM and an organisation’s employee attrition rate.

Method: The employee Attrition Case Study Dataset used is an anecdotal data set that tries to figure out relevant variables that determine employee behavioural aspects towards attrition. This study investigates why attrition occurs, the major triggering attributes for employee turnover, and how it might be anticipated to employ artificial intelligence (AI) to avert corporate losses.

Results: Employees’ monthly income, age, average monthly hours, distance from home, total working years, years at the company, per cent of salary hike, number of companies worked, stock options level, job role and other factors are taken into consideration. A feature importance extraction framework was devised to investigate the various dormant factors. The findings also show feasible hypotheses that help enhance employee engagement, reinvent the worker dynamic, and higher levels of risk decrease attrition rate.

Implications: Employees’ monthly income, age, average monthly hours, distance from home, etc., are all major variables in employee attrition in the Indian IT business. This research adds to the theory development of behavioural elements in people analytics based on AI.

Purpose: Can we predict employee attrition through employee behavioural patterns advancement using AI tools.