These companies and other researchers have already begun to develop methods for tackling deepfake interference, including by using the following techniques: Deepfake Zoos: Creating a secure data-lake of deepfake content that can be used to quickly identify ‘known’ deepfakes or that can be used as a trusted research environment (TRE) for deepfake detection technology. This is akin to hash matching - when hashes (techniques to create fingerprints of files on a computer system) are compared with another. Radioactive Data: Infiltrating the datasets that large language models (LLMs) - which are used to build GenAI applications - are trained on with data that is more easily identifiable to detection technologies. Meta has shown this is possible and could be done effectively even if only 1% of total training data is ‘radioactive’. Safety by Design in GenAI Development: Safety by design refers to an approach which places user safety and rights at the centre of the design and development of online products and services. As it relates to deepfake/disinformation campaigns, this includes improving understanding of user behaviours and user experience on GenAI applications to create design interventions that reduce the risk that harmful content is generated. Emerging Detection Tools: We have begun to see the development of deepfake detection tools employing various models of detection which demonstrate a range of effectiveness.