How AI and machine learning are supercharging cybersecurity (VB Live)
Every year malicious cyber activity rips off the U.S. alone anywhere between $57 billion and $109 billion, and the average cost of a cyber attack is $3.85 million but can reach $350 million for megabreaches. And the cyberthiefs are getting smarter, more aggressive, and harder to stop, since their attacks evolve as quickly as traditional security methods find a way to spot the intrusions and stop them.
It’s a consequence of the widespread penetration of internet connectivity, which also increases the potential impact of a threat, the growth in the number of black-hat programmers dedicating themselves to cyber warfare, and the increasing sophistication of attack tools, techniques, and strategies designed to ramp up the speed and power of cyber assaults.
That includes high-scale, context-sensitive attacks, in which criminals target high-ranking employees conducting sensitive business, such as mergers, or attacks at home when they’re traveling and potentially out of reach.
In cloud ransom attacks, cybercriminals snatch control of an organization’s cloud storage and computing, and threaten the company with deletion or release of sensitive information, unless the company hands over a wheel barrel full of cash and prizes.
Cybercriminals are also increasingly going for the long game, worming their way into company systems and lurking behind the scenes to log activity, look for sensitive information, and choose the best time to launch an attack, whether that’s theft or destruction.
Why the old methods aren’t working — and why AI is the next line of defense
Traditional methods of cybersecurity, which rely on a wide variety of manual processes, as well as static rules and signatures, are becoming increasingly ineffective; putting out a fire in one area of your systems too often leaves other areas vulnerable, creating a game of continuous catch-up and harm reduction, never proactive prevention. Known threats can be fought against; new threats can wreak havoc in the space between detection and the manual update of your software rules.
Cybersecurity needs to catch up with the thieves — and that means adding artificial intelligence and machine learning to your cybersecurity solutions in order to keep pace with the threats that just keep on coming.
Artificial intelligence and machine learning adds automation to your threat detection. Your software is trained on large datasets of cybersecurity, network, and even physical information, and it keeps learning out in the field, catching up with new threats as soon as they appear and evolving along with the attacks it detects — at scale.
Humans are still the last line of defense for any major cyber threat, able to make the important decisions when and where they’re needed, but AI-powered cybersecurity systems are your boots on the ground, your eyes in the field, and as cybercrime picks up speed, AI is the only way to keep pace.
To learn more about how AI can level up your cyberattack prevention, how to develop and implement an AI-powered cybersecurity strategy for every level of your organization, and a look at real-world AI applications, don’t miss this VB Live event!
Source: VentureBeat
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