Information, Geometry, and Trust: Foundations of Secure and Intelligent Decision Systems

Abstract: This manuscript develops a unified theory of decision for secure and intelligent systems by treating information, geometry, and trust as co-constitutive elements of high-consequence analysis and action. It argues that cryptographic infrastructures, intelligence workflows, and biosecurity programs share a deeper methodological architecture: each must reason over uncertain evidence, define decision boundaries under adversarial conditions, and justify interventions through transparent assumptions and auditable governance. Across five chapters, the book constructs a conceptual and operational framework that links research design, statistical explanation, causal inference, machine learning prediction, big data engineering, and lifecycle controls into a coherent analytic discipline. Particular emphasis is placed on the distinction between technical performance and institutional trustworthiness, showing how robust methods require measurement design, uncertainty taxonomies, validation logic, reproducibility protocols, and human oversight. The manuscript also translates these foundations into sectoral decision toolkits for digital public systems, intelligence analysis, biosecurity surveillance, and critical infrastructure governance, with attention to application across South Asia, Europe, Africa, and the Americas. The resulting contribution is a publisher-ready, academically oriented synthesis that supports researchers developing theory, practitioners building systems, and policymakers designing accountable decision regimes. Keywords information geometry, trustworthy AI, secure decision systems, cryptographic governance, intelligence analysis, biosecurity surveillance, uncertainty taxonomy, causal inference, statistical modeling, machine learning generalization, adversarial robustness, MLOps, reproducibility, data provenance, policy analytics, decision assurance