Sep 2026· Autonomous Adversaries and the Future of Cyber Insurance· pp. 173-206· 44 references
Abstract
This chapter examines the human layer as the primary battlefield in AI-driven cybersecurity. While traditional cybersecurity has focused on networks, applications and data, autonomous AI systems shift adversarial activity toward human cognition, trust and decision-making. Systems like Anthropic's Mythos demonstrate that machines can autonomously identify vulnerabilities, generate exploits, and accelerate operations with minimal human input. The human layer becomes increasingly vulnerable to AI-enabled manipulation including synthetic identities, deepfake personas, hyper-personalized phishing and real-time social engineering as attack timelines compress from weeks to hours. The chapter argues that defending the human layer requires moving beyond awareness training toward active, edge-computed autonomous defense capable of detecting suspicious interactions, verifying identity claims and interrupting deceptive workflows while preserving user agency. It also explores connected cyber insurance using privacy-preserving telemetry and behavioral signals to assess personal cyber risk.
This publication proposes a definition and a classification of agile software development approaches and analyses ten software development methods that can be characterized as being "agile" against the defined criterion.
P. Abrahamsson, O. Salo, Jussi Ronkainen et al.· arXiv.org· 727 citations· ⚡54
The study shows that agile practices improve both informal and formal communication, but indicates that, in larger development situations involving multiple external stakeholders, a mismatch of adequate communication mechanisms can sometimes even hinder the communication.
M. Pikkarainen, Jukka Haikara, O. Salo et al.· Empirical Software Engineeri...· 401 citations· ⚡48
The results indicate that software engineering work practices are chosen opportunistically, adapted and configured to provide value under the constrains imposed by the startup context.
Nicolò Paternoster, Carmine Giardino, M. Unterkalmsteiner et al.· Information and Software Tec...· 394 citations· ⚡54
The perception of the impact of agile methods is predominantly positive, and several challenge areas were discovered, but based on this study, agile methods are here to stay.
M. Laanti, O. Salo, P. Abrahamsson· Information and Software Tec...· 260 citations· ⚡20
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MIT News · Artificial Intelligence· news.mit.eduOct 8, 2026
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
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