How Machine Learning and Artificial Intelligence are Powering the Future of Cybersecurity
Even before the coronavirus pandemic, cybersecurity was an important concern for small businesses and enterprises alike. Statistics show that worldwide spending for cybersecurity has increased from 34 billion USD in 2017 to 43.1 billion USD in 2020.
This, of course, is a response to the rise in cyber threats targeted at businesses all over the globe. There have been several stories in the past years about data leaks, service disruptions, and network breaches which have affected industry giants and even governments. These stories are indeed alarming, reinforcing how much of a threat cyberattacks can be. Fortunately, technologies such as artificial intelligence (AI) and machine learning (ML) are enabling businesses to better counter these threats. In this article we discuss in detail the massive impact AI and ML are making on the world of cybersecurity.
The Roles of Human Error in Cybersecurity Breaches
As sophisticated as cyber threats have become, human error is still the biggest contributor to breaches in cybersecurity. Whether it is carelessness, lack of training or security protocols, the human component is the most vulnerable part of an organization’s cybersecurity. According to Kaspersky Daily, most businesses admit that their workforce poses the most danger to their security.
Imagine an employee using the same simple password for the longest time on all company accounts and software apps. Or one who is convinced to click a link from a phishing email, which downloads ransomware. Even more common, the executive who accesses confidential files using the public network in their favorite morning coffee shop.
These are all risks that can be mitigated by employing AI and ML in cybersecurity, especially if these risks are deconstructed thoroughly and provided as training for the workforce.
Working Hand in Hand with Cybersecurity Professionals
The challenge for cybersecurity professionals is to always stay ahead of the technologies that attackers use. A lapse in this race for the bleeding edge of cybersecurity will have devastating consequences for the companies these professionals are protecting. Time and time again, we have seen organizations fall victim to the newest threats all because their security components could not keep up.
This is because attackers do not really follow a set path when developing their arsenal. They employ creativity and experimentation to discover new ways of bypassing security. It is virtually impossible to always predict the methodology of the next attack. So, instead of working to predict these threats, the more effective approach would be to beat attackers with speed, and that is what AI can do.
With AI and ML, experts can scan, identify, and react to attacks at a rate that will prevent even the most creative attacks from doing any damage to systems. Catching and containing these threats as they happen is a feat that is only possible with the help of these technologies.
Identifying and Responding to Complex Threats
Detection of threats is inarguably the most important component of cybersecurity. Most measures rely on the speed and effectiveness of detection before they can address the threat. Artificial intelligence and machine learning enable quick and masterful detection of even the most complex threats. By analyzing thousands of anomalies in mere seconds, AI and ML give cybersecurity experts the ability to prevent or counter these attacks in a timely manner.
Furthermore, according to Tech Crunch, machine learning even allows security teams to reverse engineer complex threats and make adjustments to their systems to protect them from similar attacks in the future. This significantly reduces the time it takes teams to formulate the best response against attacks.
Adding Layers of Cybersecurity
Having multiple layers of security doubles protection for a network, especially if it uses artificial intelligence-based cybersecurity. Its adaptive nature makes it a significantly more formidable wall against threats.
AI and ML also make it easier for teams to manage cybersecurity in general. These technologies can automatically analyze threats and pinpoint vulnerabilities within a network. This automated process can build reports and recommendations based on the data it analyzes, making security management simple, faster, and easier. Not to mention that it reduces the manpower needed to accomplish similar tasks.
It is important that companies learn from attacks, whether they are successful or not. A more informed and prepared security team is better equipped to prevent similar incidents in the future.
Lowering the Cost of Cybersecurity
Cybersecurity spending has been rapidly increasing in the last few years. Part of this is because companies are now realizing the enormous burden on time, energy, and resources breaches can become. Apart from being a more effective measure against cyber-attacks, AI and ML are also more cost efficient. The initial cost of investment will easily be outweighed by the degree of safety and confidence organizations will gain.
The reduced cost in manhours to accomplish cybersecurity is also noteworthy here. There is significant savings to be gained if AI and ML take over the more tedious tasks of detecting, containing, and preventing attacks. Security experts are then given the leeway to strategize better measures to handle future attacks.
With the help of AI and ML, training the workforce will also be simplified. AI and ML lend cybersecurity measures a more robust compiling and organizing system. This allows companies to better understand the threats they encounter, which can make training courses more targeted and effective overall.
Artificial Intelligence and Machine Learning in Action
There are already many real-world applications of AI and ML in cybersecurity. Here are a few concrete examples of these technologies in action.
Google’s Battles with Spam Emails
According Google, the machine learning-based email filtering technology they used prior to 2019, was already filtering and blocking more than 99.9 percent of spam, phishing, and malware messages. Despite this, the tech giant continued to develop their email tech, and in 2019, they announced a breakthrough that promised to mitigate 100 million more spam messages. This new technology uses a machine learning framework called TensorFlow, which has shown significant improvements in detection accuracy.
Google is now able to identify spam messages that used to be extremely difficult to detect. The software helps block image-based threats in messages, and even emails that have hidden content embedded in them. The new technology allows them to scale their efforts even further, and enabled their experimentation to be more efficient, requiring fewer engineers.
Microsoft’s Deep Learning-based Threat Detector
Deep learning is essentially a function of artificial intelligence and machine learning that imitates how the human brain works. It is a computer’s way of learning with the intervention or supervision of a human.
According to Microsoft, their use of deep learning in threat detection on endpoints, emails, documents, apps, and identities has improved their coordinated defense against threats. They classify this application of deep learning as a category of machine learning algorithms that combine different behavior detections to create a decision-making model.
Since the deployment of this technology, Microsoft has significantly increased their ability to correctly identify complex attacks and malware campaigns. They use several blocks of deep learning called Convolutional Neural Networks (CNNs) and Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN). These enable their program to better classify and correctly identify threats in significantly less time than before.
One of this program’s most successful feats to date is how it was able to uncover a new form of Bondat worm and prevent it from causing damage. Bondat worms basically turn infected machines into ‘zombies’ that hack websites on the net. In some cases, these zombies can even mine for cryptocurrency. The new form that Microsoft’s technology caught uses USB devices to spread.
An Uncomfortable Truth
The truth is, while AI and ML can be used to suppress cyber-attacks, they are also being used by the attackers. In 2017, the WannaCry ransomware attack breached the security of several organizations across more than 150 countries. This was just the beginning of a new era for cyberattacks.
Today, ‘Offensive AI’ is already being widely used in attacks. There are highly complex malware that are AI-powered. This means, without an equally powerful defense against these attacks in the future, the cybersecurity of businesses around the world will be vulnerable.
Cybersecurity has always been about which side has the cleverer and faster technology. This race has now been significantly boosted by the introduction of artificial intelligence and machine learning. In the future, the use of AI and ML in cybersecurity will shift from an impressive nice-to-have, to an essential that businesses of all sizes need to employ.
Here at Outsource IT, we employ advanced cybersecurity tools which utilize AI and ML to protect our client’s critical business data and devices. Contact your Outsource IT account manager to learn how we can help protect your organization.
