About this book:

This book provides a comprehensive industrial framework for the lifecycle management of safety-critical Artificial Intelligence within military operations. It argues that trustworthy deployment requires a transition from static checklists to a continuous discipline spanning requirements engineering, formal mathematical verification, and rigorous testing in both high-fidelity simulations and physical mission-focused testbeds. By synthesizing practices from systems engineering and modern software assurance, the text offers practitioners concrete methods for defining safety taxonomies, constructing structured assurance cases, and implementing automated CI/CD pipelines to gate model updates against safety regressions.

The technical core of the book addresses the unique risks of the military landscape, such as adversarial attacks, distribution shift, and the "fog of war." It details specialized strategies for out-of-distribution detection, explainable AI for human-machine teaming, and the verification of complex planning and control algorithms for autonomous platforms. A significant emphasis is placed on "runtime assurance," utilizing independent safety monitors and real-time telemetry to provide a final layer of defense that can intervene when an AI system approaches the boundaries of its safe operating envelope or encounters unforeseen operational conditions.

Beyond technical implementation, the book explores the vital intersection of engineering with ethics, the Law of Armed Conflict, and regulatory certification. It provides guidance on navigating defense procurement frameworks and aligning autonomous behaviors with legal principles like proportionality and distinction. The final chapters establish a programmatic roadmap for organizations, utilizing maturity models and key performance indicators to track systemic progress. Ultimately, the work advocates for a culture of continuous safety improvement, where incident response and postmortem analyses are institutionalized to ensure that military AI remains reliable, accountable, and resilient in contested environments.

What You'll Find Inside:

• Comprehensive safety taxonomy and hazard analysis tailored to contested military environments, distinguishing autonomous vs. decision-support failures and technical, human, and adversarial origins.
• Rigorous requirements engineering that translates mission needs into verifiable safety properties, incorporating formal specifications, adversarial robustness, and ethical/legal constraints.
• Application of formal methods (model checking, theorem proving) to prove critical safety properties of AI components, complemented by simulation, digital twins, and mission‑focused testbeds for empirical validation.
• End‑to‑end data governance, curation, and dataset risk assessment to ensure training data provenance, representativeness, label integrity, and resilience against bias and adversarial manipulation.
• Structured safety cases and assurance arguments (e.g., GSN) that aggregate evidence from verification, testing, runtime monitoring, and human‑machine teaming to support certification and continuous safety improvement.

Who's It For:

This book is intended for defense AI engineers, system safety specialists, program managers, and certification authorities working on safety‑critical military systems. It provides practitioners with concrete processes, verification techniques, and governance practices needed to develop, test, certify, and operate trustworthy AI in land, sea, air, space, and cyber domains. Oversight bodies and legal advisors will also find guidance on evaluating safety cases, ethical alignment, and compliance with the Law of Armed Conflict and defense acquisition regulations.