Challenges remain in AI cybersecurity and healthcare

Organisations have a big challenge ahead of them when it comes to cybersecurity and artificial intelligence, says Sunil Gupta, president and COO of Paladion Networks.

“We need AI in cybersecurity because the threat landscape has changed,” he said at Artelligence 2018. “There is pressure on CEOs and IT departments to move all their operations and data to the cloud. This has brought about new threats that didn’t exist before. In addition to that, there has been an increase in the number of deadly cyberattacks in recent years.”

Gupta also revealed that companies that have gone out of business are those that have been slow to detect such attacks. “Today, it is crucial that we build early detection and fast responses to such threats. It is possible for us to still save our data, before the whole world knows about it after a leak.”

Speaking about the role that AI will play, Gupta explained that humans are not very good at processing a large amount of data in a short amount of time. AI, he stated, acts as a big support system in such cases. A sophisticated AI system can collect and process over 100TB of threat data daily from over 200 threat intelligence feeds.

“AI is giving us the power, speed, and accuracy that is required in such cases,” he explained. “While human intervention is still required, AI can work better on threat anticipation, threat hunting, security monitoring, and breach management.” He added that there are two core principles to apply AI in security: “AI can now drive every stage of detection and response. AI also works best within a unified comprehensive security programme.”

Vineet Shukla, director of Machine Learning, United Health Group also spoke about some of the progress that is being made in implementing AI systems in the healthcare industry.

“Healthcare is changing, and the challenge today is to be more reactive and preventive,” he said. “Today you can use AI to help monitor patients remotely, and you can also use AI to collect data on the effectiveness of certain drugs in regions across the world. Deep learning can also be used to predict if people will be at risk of certain diseases in the future and then plan accordingly for that scenario.”

He also stressed that AI will not result in doctors losing their jobs once AI becomes more efficient and commonplace. “Doctors will be able to better manage their time and see patients that really need their help. Also, AI and machine learning is not limited to providing healthcare; it can also be used to improve efficiencies across hospitals.”