Dynamic Incident Response: A Framework for Security Teams is a new book by Joshua Wright on incident response. As of September 2026, the online, PDF, and EPUB editions are free to read, download, and share under CC BY 4.0. What is DAIR? Most incident response models describe a sequence: prepare, identify, contain, eradicate, recover, learn….
Cyber threat intelligence programs face a persistent challenge: their work may be operationally valuable while remaining difficult to express in financial terms. Reports produced, indicators collected, and briefings delivered can demonstrate activity, but they do not necessarily show whether intelligence reduced risk, prevented loss, or improved organizational decisions. In A Product-Level Profit & Loss Model…
AI governance is increasingly becoming evidence work. As lawmakers and regulators develop audit requirements, disclosure rules, and new theories of accountability, organizations must be prepared to demonstrate what they knew, what they tested, what they approved, and what they changed. An artificial intelligence policy may establish expectations, but inventories, risk assessments, testing records, change logs,…
Cyberpsychology examines how people think, feel, behave, and make decisions in technology-mediated environments. That includes social media, artificial intelligence, online communities, immersive systems, cybersecurity environments, and other digital spaces. In simple terms, cyberpsychology studies the human side of technology. That matters because technology does not operate in isolation. People design systems, interact with them, trust…
There is something remarkably powerful about reaching a conclusion without beginning with the conclusion at all. Instead, begin with a question. Then another. Establish one small point, followed by another. Each proposition may seem relatively unimportant by itself. But as those propositions accumulate, the possibilities begin to narrow. Eventually, the person answering the questions may…
Artificial intelligence systems are increasingly asked to reason under uncertainty: to weigh incomplete evidence, assess risk, and produce judgments where no fact set is complete. Almost without exception, the uncertainty frameworks embedded in these systems are probabilistic. Neural networks output probability distributions. Large language models are trained to predict likely continuations. Risk-scoring systems assign probability…