Human intelligence and intuition are vital to training artificial intelligence (AI) and machine learning (ML) models to provide enterprises with hybrid cybersecurity at scale. Combining human intelligence and intuition with AI and ML models helps catch the nuances of attack patterns that elude numerical analysis alone. Experienced threat hunters, security analysts and data scientists help ensure that the data used to train AI and ML models enables a model to accurately identify threats and reduce false positives. Combining human expertise and AI and ML models with a real-time stream of telemetry data from enterprises' many systems and apps defines the future of hybrid cybersecurity. "Based on behaviors and insights, AI and ML allow us to predict [that] something will happen before it does," says Monique Shivanandan, CISO at HSBC, a global bank.

Check out all the on-demand sessions from the Intelligent Security Summit here. Weaponizing artificial intelligence (AI) to attack understaffed enterprises that lack AI and machine learning (ML) expertise is giving bad actors the edge in the ongoing AI cyberwar. Innovating at faster speeds than the most efficient enterprise, capable of recruiting talent to create new malware and test attack techniques, and using AI to alter attack strategies in real time, threat actors have a significant advantage over most enterprises. "AI is already being used by criminals to overcome some of the world's cybersecurity measures," warns Johan Gerber, executive vice president of security and cyber innovation at MasterCard. "But AI has to be part of our future, of how we attack and address cybersecurity."