Mileva Security Labs Research

Likelihood Analysis

This project seeks to fill a crucial gap in AI security research by quantifying the likelihood of AI incidents. Likelihood is a key parameter in risk assessment best practices in other security studies, including cyber security and national security.

Risk = severity x likelihood

In the practice of AI security there is a growing need to model risk, however while there is much research into severity there is very little in likelihood. Drawing from established risk assessment methodologies in cybersecurity, the study aims to construct a robust framework for evaluating and mitigating AI security risks, thus shedding light on the elusive dimension of risk associated with AI vulnerabilities.

This project is conducted in collaboration with MINT Lab, and the UNSW Canberra’s Innovation Lab for Cyber Security and Machine Learning. It is generously supported by the Foresight Institute.

Risk Modelling

This project provides methods to quantify and manage AI security risk based on an organisation’s unique risk profile.

By employing cutting-edge techniques and frameworks, we aim to enhance the resilience and security posture of AI systems in various industries. Our approach involves a comprehensive assessment of potential vulnerabilities and threats specific to each organisation's operational environment, followed by the implementation of robust risk mitigation strategies. It is generously supported by the ACT Government through the Canberra Innovation Network.

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