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How AI and Machine Learning Will Affect Cybersecurity

The future should be here already. So then why do we still receive so much email spam and hear about so many data breaches? You’d think the internet would have matured by now and worked out all the kinks.

In reality, issues concerning cybersecurity will be a constant for the entirety of our digital lives. To put this in perspective, there were 5.99 billion recorded malware attacks in the first half of 2018, which doubled the number in 2017 over the same time period.

Obviously, security experts would like to shift that trend in the other direction. Cybersecurity is now more proactive than reactive. A big reason why is the major advancement of artificial intelligence, machine learning, and data science. Computers are getting smarter and keeping us all safer as a result.

Pattern Recognition

Unfortunately, you can’t simply turn on an AI system and expect it to add a strong layer of defense to your network and software. That’s because machine learning is all about taking data from the past and using it to your benefit in the future.

AI algorithms need to be fed months and months of activity logs in order to achieve competence at identifying anomalies and threats. They start by using the information to set a baseline of normal performance and then calculate new events from there. These patterns help the machine learning system to recognize a hacker or a threat to the system.

When it comes to cybersecurity, time is of the essence. Within an hour or less, a hacker can infiltrate a corporation’s system and either steal critical data or hold it for a ransom. The best AI tools are able to recognize an attack at the beginning and send off alerts to the right people.

Of course, cybercriminals are always looking for new ways to execute attacks that will be effective, and as a result some have begun to leverage AI for their own purposes. This means that companies of all sizes need to invest in the best machine learning software in order to stay ahead of the game.

Cloud Integration

The cloud computing movement has completely revolutionized how companies operate on the web. Now, instead of hosting servers and equipment in local offices or small data centers, systems have been shifted to cloud platforms like Amazon Web Services or Microsoft Azure.

This has been both a blessing and a curse in the cybersecurity world. Companies now have fewer pieces of physical hardware to worry about but must face the trust issue of storing all of their critical data in the cloud, which generates a new range of potential threats and vulnerabilities.

Because the latest AI and machine learning systems are all based on software algorithms, that makes it easier for companies to deploy them across their cloud infrastructure and services. For example, the best antivirus tools now rely on AI for scanning servers and finding instances of malware. The algorithms are smart enough to detect malicious software based on self-learned skills.

With machine learning intelligence watching over their systems, business of all sizes can secure their cloud environments and protect against the most typical means of malware penetration. It would be wise not to take the word of your cloud provider that they have top-of-the line security in place on the.

Risk is never absent, no matter what a brochure or landing page says. You should still keep your own applications and websites locked down via your own means, and AI offers increasingly effective tools for doing so.

Human Interaction

Will machine learning algorithms become so smart that they will eliminate the need for human input entirely? Let’s not worry about that scenario just yet, because even the strongest AI cybersecurity tools that exist still require collaboration with the human world.

Machine learning systems are getting better at natural language processing and trend analysis, but at the end of the day, humans can still do a better job of interpreting spoken and written text. This adds a great deal of value when trying to synthesize the reports that AI generates.

The Bottom Line

In general, you don’t want any machine learning system to gain too much control over the decision making process. That’s not because they are a dangerous threat to humanity, but simply because AI still identifies a lot of false positives when it comes to cyberattacks. Humans should always be alerted during threat detection in order to decide what action to take next.

About the Author

Gary Stevens is a front end developer. He’s a full time blockchain geek and a volunteer working for the Ethereum foundation as well as an active Github contributor.

 

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