Bot attacks are drawing more and more headlines with tales of identity theft. The wealth of consumer data available on the dark web through breaches, social media and more are sold to hackers to compile online consumer profiles to take over accounts for money, products or services.
The question of who is real and can be trusted, and how companies should defend against this issue remains unanswered. For next generation bot detection solutions to be effective, there is a need for much higher precision in the level of user behavioral analytics that must be implemented.
Automated versus AI-powered bots
Most people are familiar with automated bots – chatbots and the like – that are actually software applications that can use AI to interact with human users to accomplish a task (i.e. book a hotel, answer customer service questions, etc.), though some are simply rules-based. However, advances in deep and machine learning, natural language understanding, big data processing, reinforcement learning, and computer vision algorithms are paving the way for the rise in AI-powered bots, that are faster, getting better at understanding human interaction and can even mimic human behavior.
