AI Safety 2026: From Risk to Resilience
A long-form analysis of capability acceleration, evaluation evasion, cyber risk, and governance gaps shaping AI safety in 2026.
Authors: Owen Sakawa; Bitange Ndemo; Moussa Bello; Aisha K. Patel; Marcus A. Rodriguez; Yan Zhu; Mina Narayanan; Suhani Gharial; Daniela Muhaj; Bosco Hung; Victoria Snorovikhina; Alexander Saeri; Jess Graham; Michael Noetel; Neil Thompson
Published: 2026-04-03
Institution: Elloe AI Research Lab
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Abstract
This report synthesizes the sharpest signals from the 2026 International AI Safety Report and adjacent public evidence, with a focus on capability acceleration, evaluation evasion, cyber risk, and the current state of governance.
The argument is practical: once advanced systems move into real workflows, runtime oversight, independent evaluation, and credible intervention paths become baseline requirements rather than optional safety language.