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This paper addresses the challenge of ensuring the safety of autonomous vehicles (AVs, also called ego actors) in realworld scenarios where AVs are constantly interacting with other actors. To address this challenge, we introduce iPrism which incorporates a new risk metric – the Safety-Threat Indicator (STI). Inspired by how experienced human drivers proactively mitigate hazardous situations, STI quantifies actor-related risks by measuring the changes in escape routes available to the ego actor. To actively mitigate the risk quantified by STI and avert accidents, iPrism also incorporates a reinforcement learning (RL) algorithm (referred to as the Safety-hazard Mitigation Controller (SMC)) that learns and implements optimal risk mitigation policies. Our evaluation of the success of the SMC is based on over 4800 NHTSA-based safety-critical scenarios. The results show that (i) STI provides up to 4.9× longer lead-time for-mitigating-accidents compared to widely-used safety and planner-centric metrics, (ii) SMC significantly reduces accidents by 37% to 98% compared to a baseline Learning-by-Cheating (LBC) agent, and (iii) in comparison with available state-of-the-art safety hazard mitigation agents, SMC prevents up to 72.7% of accidents that the selected agents are unable to avoid. All code, model weights, and evaluation scenarios and pipelines used in this paper are available at: https://zenodo.org/doi/10.5281/ zenodo.10279653.