Cybersecurity is one of the many uses of artificial intelligence. Going by a recent report by Norton, the global cost to recover from a typical data breach is USD 3.86 million. Studies also conclude that it takes a whole 196 days to recover from any data breach. As such, it makes sense for companies to use AI to avoid both financial losses and waste of time. That said, this article highlights how AI can help in cybersecurity.
AI offers insights that enable companies to understand threats easily, thereby reducing response times and making firms compliant with security best practices. On the other hand, machine learning (ML) helps in recognizing patterns in data to enable machines to learn from experience. So, by leveraging cyber threat intelligence, machine learning, and AI, companies can respond to issues with speed and confidence.
Let us now examine how AI can help in cybersecurity.
Automated security measures show the increasing role of AI in cybersecurity. Together with machine learning, AI can help companies to identify threats and find links between potential risks fast. This form of detection eliminates human errors from the process.
Thanks to ML, AI can adapt and learn from experience and patterns instead of cause and effect. In fact, today ML has made it possible for machines to teach themselves. It means that they can create models for pattern recognition instead of waiting for humans to develop them. AI is trained to process large amounts of data, for example, new stories and blogs, meaning that it has a better comprehension of cyber threats. After that, AI in cybersecurity taps into reasoning to determine various risks, for example, suspicious addresses, strange files, and so forth, before initiating suitable remedies.
Quickly Identifying Errors
AI is significantly improving the duration it takes to identify suspicious issues on websites. For instance, in 2016, Google blacklisted about 20,000 sites for having malware and another 50,000 for phishing scams every week. Taking these figures into account, you can easily determine that this means about 280,000 sites are affected every month. Now, while humans are fast, they are not quick enough to scrutinize millions of sites each month and identify 280,000 suspicious websites.
Likewise, developers are leveraging AI to identify people with bad intentions on sites. This process is referred to as anomaly detection and has several uses with cybersecurity topping the list. Depending on your Artificial Intelligence techniques, the program can analyze tons of visitors and categorize them based on their threat level and behavior in a few seconds.
If you own a site that needs visitors to log in, feature forms that require input or want to provide another layer of security on the site’s backend, AI can better the authentication process to a safe level.
The first way secure authentication can be achieved is physical identification, where AI uses different factors to identify a person. For instance, a smartphone can use fingerprint scanners and facial recognition to allow you to log in. The process behind this entails the program analyzing main data points about your face and fingers to discern if the login is authentic.
Apart from that, AI can look into other factors to determine if a specific user is authorized to log in to a technology device. The tech scrutinizes the way you enter keys, your typing speed, and your error rate while spelling.
Quicker Response Times
AI can process massive amounts of unstructured information to provide insights with greater efficiency. What’s more, with ML, AI can learn patterns much more quickly, thus accelerating response time, making it faster and easier to stop threats before they cause problems. Case in point, IBM is now using cognitive technologies and AI in cybersecurity to enable companies to identify threats fast and respond accordingly.
Mistake-free cybersecurity is an excellent application of artificial intelligence in security. Unlike humans, AI cannot tire or get bored when performing repetitive tasks. As such, the risk of human error reduces significantly. Nonetheless, humans need to work with AI for better results. For one, humans offer the common sense that machines lack. Apart from that, they are better decision-makers in nonstandard situations.
These are some of the examples of AI in cybersecurity. And as developers continue to provide more data points to existing programs as they create new forms of AI, it is safe to contend that AI will help fight cybercrime even more effectively in the future. Keep visiting our page to find out how will AI affect cybersecurity.
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