The technology and creativity underlying breaches are continuously evolving.  The moment one approach is shut down, another one emerges. Simulating all possible permutations of breach attempts or even a subset thereof is clearly beyond the human scale.  GAN technologies are needed to effectively simulate breach scenarios in an AI-controlled cat-and-mouse game between a hacking and a defending neural network.  

GAN for Breach Simulation and Finding Techniques for Breach Detection:

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AI Engine employs Generative Adversarial Networks (GANs), a machine learning technique that consists of two neural networks: a generator and a discriminator.

• The generator is trained to generate synthetic data that mimics real-world breaches or attack scenarios.
• The discriminator is simultaneously trained to differentiate between real and synthetic data, forcing the generator to improve its output continually.
• By utilizing GANs, Theom's AI engine can simulate breach scenarios and identify potential weaknesses in the system, helping organizations proactively address vulnerabilities and enhance breach detection techniques.