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3.2 Data Airlock and Harmful Materials Recognition Australia
In Australia, the Australian Federal Police (AFP) has been pursuing several opportunities for the use of AI and machine learning to automate or assist with tasks such as tagging and organizing data. This includes the creation of a search engine system, akin to Google, for investigation data that will support AFP in making both sense and use of the large amounts of structured and unstructured data in its databases. Without the support of tools such as this, it may otherwise encounter difficulties in locating data points and making critical connections.
Another topic AFP is working on is the creation of a ‘data airlock’ system, which enables researchers to develop new algorithms without having access to the data. The data airlock is equipped with cryptography to provide an isolated and secure environment where researchers can put their algorithms and models in, execute them against the data, and extract the results of the research and analysis. Accordingly, data never leaves the data owner’s environment, a feature especially relevant for organizations dealing with sensitive data. It is expected that the data airlock system will enable third parties to train, validate and test machine learning tools against real-world seized data, without requiring direct access to these materials. This could, for instance, help researchers to better understand and monitor the dark web.
A notable application of this system that is being explored by AFP focuses on the use of deep learning models to recognize, tag and cluster images and videos containing harmful material, such as child sexual abuse materials. Automated recognition of harmful materials, combined with the data airlock, will effectively protect law enforcement officers, investigators and researchers by diminishing their exposure to these materials.
