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We address the socio-technical cybersecurity risks of operationalising machine learning models in order to

  • to understand the cognitive processes and user behaviours that impact on the security of MLOps

  • to model these processes and behaviours so that we can securely deliver human-machine teaming in MLOps

  • to understand how to defend MLOps against subversion as well as how to attack the MLOps of adversaries



Work package 1

Task 1.1.

The Social Practice of Data Scientists in MLOps:

  • Qualitative analysis of MLOps

  • Identifying how links between different team functions can be vulnerable

Task 1.2.

Developing a Naturalistic Understanding of Analyst Decision Making:

  • Naturalistic Decision Making (NDM) study with analysts who rely on implementations of MLOps through the field work

Security Dialogues: DevSecOps

  • Addressing security as a social practice

  • Creating a peer-to-peer dialogue between software developers and security practitioners

  • Developing a better understanding of risk


Kim, G., Behr, K. and Spafford, K., 2014. The phoenix project: A novel about IT, DevOps, and helping your business win. IT Revolution. Vancouver

Work package 2

Task 2.1

Modelling the Socio-Technical Security Risks of Human-Machine Teaming:

  • comprehensive table of threats with historical probabilities of occurrence;

    • probabilistic future-looking mapping of human-machine teaming using input from WP1 as well as survey data

Task 2.2

Developing a Human-Machine Teaming Digital Twin:

  • MLOps Digital Twin Dashboard

digital twin.jpg

Work package 3

Task 3.1

Modelling Strategic Interactions for Benign and Adversarial MLOps:

  • Use game theory to explore how multiple human-machine players on both sides (i.e., in at least 4-player games) interact within and across human-machine teams in games of incomplete information

Task 3.2

Developing Computational Game Theoretic Algorithms for Cyber Deception:

  • Model realistic attack and defence on MLOps


This project

  • contributes to building a cybersecurity research capability between social and behavioural science, data science and computer science;

  • forges a new research partnership between team members and their research institutions;

  • aims to make a significant internationally recognised Australian-based contribution to cybersecurity, machine learning, data science and transdisciplinary research;

  • increases the capability of the national intelligence and security community by developing new practical methods for ensuring the cybersecurity of MLOps;

  • facilitates advances in MLOps that would be of benefit across the intelligence community;

  • aims to enable the national intelligence and security community to participate in the research and to co-design solutions.

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