Predictions: AI Fuzzing and Machine Learning Poisoning - Security Boulevard

For many criminal organizations, attack techniques are evaluated not only in terms of their effectiveness, but in the overhead required to develop, modify, and implement them. To maximize revenue, for example, they are responding to digital transformation by adopting mainstream strategies, such as agile development to more efficiently produce and refine their attack software, and reducing risk and exposure to increase profitability. Knowing this, one defensive response is to make changes to people, processes, and technologies that impact the economic model of the attacker. For example, adopting new technologies and strategies such as machine learning and automation to harden the attack surface by updating and patching systems or identifying threats forces criminals to shift attack methods and accelerate their own development efforts. In an effort to adapt to the increased use of machine learning and automation on the part of their targets, we predict that the cybercriminal community is likely to adopt the following strategies, which the cybersecurity industry as a whole will need to closely follow.