Serious Managers' Guide to AI Network Security
Operationalizing AI for Network Defense
No se pudo agregar al carrito
Add to Cart failed.
Error al Agregar a Lista de Deseos.
Error al eliminar de la lista de deseos.
Error al añadir a tu biblioteca
Error al seguir el podcast
Error al dejar de seguir el podcast
Prueba gratis de 30 días de Audible Standard
Compra ahora por $8.99
-
Narrado por:
-
Virtual Voice
-
De:
-
JC Louis-Charles
Este título utiliza narración de voz virtual
Serious Managers Guide to AI Network Security: Operationalizing AI for Network Defense is a practical leadership guide for managers responsible for securing modern, hybrid networks in an era where artificial intelligence shapes both attack and defense. As security quietly shifts from static rules and signatures to probabilistic, machine-speed decisions, this book makes that transformation visible—and manageable—for leaders who must remain accountable for outcomes.
Rather than focusing on algorithms or tool configuration, the book reframes network security as a decision-management problem. It explains how AI actually operates inside today’s security stacks: ingesting telemetry, correlating signals, ranking risk, and increasingly acting without human approval. Readers are guided through the realities of AI-augmented threats, including automated reconnaissance, adaptive malware, AI-driven lateral movement, and identity-based attacks that exploit dissolved perimeters across on‑prem, cloud, SaaS, IoT, and OT environments.
The book then moves from threat understanding to architecture and operations. It shows managers how to map their true attack surface, apply zero trust principles in real-world environments, and redesign security operations centers to move from “logs and dashboards” to “signals and outcomes.” Human‑in‑the‑loop and human‑on‑the‑loop oversight models are explored in depth, emphasizing how to balance speed with control so automation strengthens—rather than undermines—organizational resilience.
Later chapters address the governance challenges that determine whether AI becomes a strategic asset or a new source of unmanaged risk. Topics include bias, blind spots, data quality, model drift, adversarial manipulation, automation boundaries, kill switches, auditability, and board‑level accountability. Throughout, the book provides pragmatic frameworks, metrics, and roadmaps designed for organizations with legacy systems, cultural constraints, and real operational pressures.
Written for managers navigating transition rather than greenfield design, this guide equips leaders with the language, mental models, and tools needed to operationalize AI safely and effectively. It is not a checklist for “set and forget” security, but a way of thinking about networks as living systems, AI as a decision fabric, and leadership as the force that ensures speed, control, and accountability evolve together.