Cyber-attacks are an important issue faced by all organizations. Securing information systems is critical. Organizations should be able to understand the ecosystem and predict attacks. Predicting attacks quantitatively should be part of risk management. The cost impact due to worms, viruses, or other malicious software is significant. This paper proposes a mathematical model to predict the impact of an attack based on significant factors that influence cyber security. This model also considers the environmental information required. It is generalized and can be customized to the needs of the individual organization.

The key parameter to differentiate the vendor capability in an IT industry is the value add that the customer experiences. To achieve this, it needs to be understood, what are the key parameters that drive this and what needs to be done to improve it. Process performance models help to understand customer satisfaction, it is a quantitative research, the associated process and probable parameters help in improving customer satisfaction. This process is established using a case study. The importance of process performance models in not clearly understood by the project team and exposure to lack of understanding of associated sub process is clearly articulated. Based on these gaps, the intent to identify the right sub process and models is addressed through a project plan.

Abstract-Management by metrics is the expectation from the IT service providers to stay as a differentiator. Given a project, the associated parameters and dynamics, the behaviour and outcome need to be predicted. There is lot of focus on the end state and in minimizing defect leakage as much as possible. In most of the cases, the actions taken are re-active. It is too late in the life cycle. Root cause analysis and corrective actions can be implemented only to the benefit of the next project. The focus has to shift left, towards the execution phase than waiting for lessons to be learnt post the implementation. How do we pro-actively predict defect metrics and have a preventive action plan in place. This paper illustrates the process performance model to predict overall defect density based on data from projects in an organization.