Autonomous Decision Intelligence for Secure and Resilient Digital Enterprises
DOI:
https://doi.org/10.32996/jcsts.2026.5.8.3Keywords:
Autonomous Decision Intelligence; Artificial Intelligence; Big Data Analytics; Blockchain; Cybersecurity; Management Information Systems; Digital Transformation; Business Intelligence; Enterprise ResilienceAbstract
Few forces have reshaped organizational life as quickly as digital transformation. The way firms create value, manage risk, and hold their competitive ground now depends on systems that grow more entangled with one another every year. A typical enterprise sits at the center of a constant flow of data drawn from its operations, its cloud platforms, the sensors embedded in its products, its planning systems, and the many places where it meets its customers. Artificial intelligence (AI), machine learning, big data analytics, blockchain, and cybersecurity have each made it easier to turn that flow into useful judgment. Yet most organizations still adopt these tools one at a time, and the habit quietly erodes the strategic payoff that integration could deliver. This paper sets out an Autonomous Decision Intelligence (ADI) framework that gathers AI, cybersecurity, big data analytics, blockchain, and management information systems (MIS) into one coherent architecture built for resilient digital enterprises. The argument rests on a synthesis of recent work in decision intelligence, predictive analytics, business intelligence, federated learning, cloud computing, blockchain governance, cyber threat intelligence, and enterprise risk management. From that body of evidence, we construct a conceptual model for organizational decision-making that is trustworthy and capable of improving itself over time. The framework gives weight to secure data governance, explainable AI, privacy-preserving analytics, blockchain-based trust, cyber-resilience, and intelligent automation. It then asks how such a design might reinforce critical infrastructure protection, supply chain resilience, economic sustainability, IT project governance, and day-to-day agility. The contribution is at once theoretical and practical, because it shows how converging technologies can turn conventional decision-support systems into adaptive ecosystems that learn. Organizations that pair AI-driven analytics with strong security and decentralized trust look best placed to absorb uncertainty, keep operating under stress, and pursue digital transformation that lasts.
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