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According to the fail fast principle in Chap.  , we need to learn from systems’ abnormal behavior and downright failures to achieve anti-fragility to classes of negative events. The earlier we can detect problems, the smaller the negative consequences are and the faster we can start learning how to improve the systems. Since humans are not good at detecting anomalies, especially in streaming data from large cloud applications, a form of automatic anomaly detection is needed. This first chapter of Part IV introduces a general learning algorithm based on Jeff Hawkins’s developing theory of how the brain learns, called hierarchical temporal memory (HTM). The HTM learning algorithm is used in the next chapter to detect anomalies in a system’s behavior.

We model information and communications technology (ICT) systems as complex adaptive systems. Since we cannot hope to predict all future incidents in complex systems, real-time monitoring is needed to detect local failures before they propagate into global failures with an intolerable impact. In particular, monitoring is required to determine the consequences of injecting artificial errors into production systems and to learn how to avoid or limit the impact of future incidents.

We have come to the end of the book, which has investigated different aspects of anti-fragile information and communications technology (ICT) systems. This chapter summarizes the book’s main insights into the development and operation of anti-fragile ICT systems, discusses the design of future systems, and outlines the need for anti-fragile processes, especially to handle attacks on the confidentiality, integrity, and availability of ICT systems.