Abstract | The (new) 20’s have allowed us to dream big on protecting our infrastructure from natural hazards. Powerful computers, machine learning, terrestrial and airborne sensors are at our disposal to help us quantify the consequences of potential hazardous events that may come in the future, are already unfolding, or have already happened. Owing to its origins in four European projects, namely HYPERION, ARCHYTAS, PANOPTIS and INFRASTRESS, the HAPI framework has been formulated to perform pre/trans/post-event risk and resilience assessment of diverse infrastructure, comprising different layers of networked, loosely-connected or autonomous assets within a city, region or country. Building upon the well-worn basis of hazard-exposure-vulnerability that underpins practically all insurance risk estimates, HAPI enables assessment of cascading (e.g., mudflow/landslide after earthquake) and cotemporaneous (e.g., extreme precipitation, temperature, ice and wind scenario) hazards, while it offers sensor integration with near-real-time updating of predictions based on hazard/asset/consequence information input. Both “static” memoryless hazards (e.g., earthquake), as well as “dynamic” time-dependent hazards (e.g., climate projections) are incorporated in tandem with static/dynamic vulnerabilities, allowing the tracking of complex phenomena, such as climate change, and their effect on the aging/corrosion/fatigue of a diverse set of assets, including buildings, bridges, piping, powerlines, highways and cultural heritage monuments. At the very basis lies a vast database of hazard and asset realization scenarios, employing Total Probability Discrete Event Simulation to explicitly track network interdependencies and propagate uncertainty from our source information to the projected integrated-system functionality and eventual recovery.