Enterprise networks face rapidly evolving threats that exceed the capabilities of reactive or historically trained defenses. This paper presents a digital-twin based framework for proactive cybersecurity strategy evaluation. The digital twin models enterprise infrastructure, user behaviors, defensive controls, and both observed and speculative threats through an executable, actor-based representation. By capturing deterministic operations and stochastic attacker decisions, it supports systematic in-silico experimentation across diverse attack paths. The approach enables evidence-driven assessment of mitigation strategies prior to deployment, addressing the limitations of retrospective methods and improving enterprise cyber resilience.

Environmental uncertainties and hyperconnectivity force technosocio-economic systems to introspect and adapt to succeed and survive. Current practices in decision-making are predominantly intuition-driven with attendant challenges for precision and rigor. We propose to use the concept of digital twins by combining results from Modelling & Simulation, Artificial Intelligence, and Control Theory to create a risk free 'in silico' experimentation aid to help: (i) understand why a system is the way it is, (ii) be prepared for possible outlier conditions, and (iii) identify plausible solutions for mitigating the outlier conditions in an evidence-backed manner. We use reinforcement learning to systematically explore the digital twin solution space. Our proposal is significant because it advances the effective use of digital twins to new problem domains that have new potential for impact. Our approach contributes an original meta model for simulatable digital twin of industry scale techno-socioeconomic systems, agent-based implementation of the digital twin, and an architecture that serves as a risk-free experimentation aid to support simulation-based evidence-backed decision-making. We also discuss the rigor of our validation of the proposed approach and associated technology infrastructure through a representative sample of industry-scale real-world use cases.

Effective decision-making of modern organisation requires deep understanding of various aspects of organisation such as its goals, structure, business-as-usual operational processes etc. The large size and complex structure of organisations, socio-technical characteristics, and fast business dynamics make this decision-making a challenging endeavour. The state-of-practice of decision-making that relies heavily on human experts is often reported as ineffective, imprecise and lacking in agility. This paper evaluates a set of candidate technologies and makes a case for using actor based simulation techniques as an aid for complex dynamic decision-making. The approach is justified by enumeration of basic requirements of complex dynamic decision-making and the conducting a suitability of analysis of state-of-the-art enterprise modelling techniques. The research contributes a conceptual meta-model that represents necessary aspects of organisation for complex dynamic decision-making together with a realisation in terms of a meta model that extends Actor model of computation. The proposed approach is illustrated using a real life case study from business process outsourcing industry

This paper takes a practitioner's perspective on the problem of organisational decision-making. Industry practice follows a refinement based iterative method for organizational decision-making. However, existing enterprise modelling tools are not complete with respect to the needs of organizational decision-making. As a result, today, a decision maker is forced to use a chain of non-interoperable tools supporting paradigmatically diverse modelling languages with the onus of their co-ordinated use lying entirely on the decision maker. This paper argues the case for a model-based approach to overcome this accidental complexity. A bridge meta-model, specifying relationships across models created by individual tools, ensures integration and a method, describing what should be done when and how, and ensures better tool integration. Validation of the proposed solution using a case study is presented with current limitations and possible means of overcoming them outlined.