How Artificial Intelligence Is Changing Cyber Security Landscape and Preventing Cyber Attacks
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The world is going digital at an unprecedentedly fast pace, and the change is only going to go even faster. The digitalization means everything is moving at lightning speed – business, entertainment, trends, new products, etc. The consumer gets what he or she wants instantly because the service provider has the means to deliver it.
While the conveniences and benefits of this digital era are many, it also brings with it several negatives. One of the most significant and destructive threats it poses is that our private information is at risk like never before. The last decade or so has seen hundreds of cases of identity theft, loss of money, and data breaches. Cyberattacks in nature are very pervasive and affect every individual, business, and government bodies alike. We are moving towards an era where cybercriminals can reach their targets in any part of the world at any time; the need for cybersecurity has never been more critical than now.
A typical cyber-attack is an attempt by adversaries or cybercriminals trying to access, alter, or damage a target’s computer system or network in an unauthorized way. It is systematic, intended, and calculated exploitation of technology to affect computer networks and systems to disrupt organizations and operations reliant on them.
Is Artificial Intelligence & Machine Learning A Bane To Cybersecurity?
With the genuinely significant potentials of Artificial Intelligence, the probability of attackers weaponizing it and using it to boost and expand their attacks is a huge threat. One of the biggest concerns is that hackers can use AI to automate cyberattacks on a massive scale. Now, our adversaries are relying on human resources to craft and coordinate their attacks. Cybercrime and cybersecurity landscape are going to change –not for the better – if and when they learn to use AI and machine learning to do the dirty work.
Another significant issue is that, just like we can deploy AI and machine learning to complement the shortage in human resources and to save cost in cybersecurity, our adversaries can also use it for the same. The finances and resources needed for launching and coordinating such attacks will go down massively – more threat to cybersecurity at relatively lower investment for the cyber attacker.
Further advancement in AI can also give birth to new types of cyber threats. AI can also hack into a system’s vulnerability much faster and better than a human can. AI can be used to disguise attacks so effectively that one might never know that their network or device has been affected.
So, the three main implications of Artificial Intelligence to the threat landscape are the augmentation of today’s threats and attacks, the development of new threats, and the variation of the nature of existing threats.
How Is Artificial Intelligence A Boon To Cybersecurity?
While the future seems bleak, there have also been developments in technology with significant impacts on cybersecurity. One such major game-changer in the field of cybersecurity is tools and techniques developed and supported by Artificial Intelligence (AI) and Machine Learning (ML) as a subset of it.
Artificial Intelligence is no longer just a buzzword and is being used extensively in industries of all kinds. Customer service, education, automation, etc. are only some of the many sectors where AI has instigated advancement by leaps and bounds. It is also playing a significant role in the ongoing fight against cybercrime.
Following are some of the ways Artificial Intelligence (AI) and Machine Learning (ML) are making a difference by giving the much-needed boost to cybersecurity.
Organizations have to be able to detect a cyber-attack in advance to be able to thwart whatever the adversaries are attempting to achieve. Machine learning is that part of Artificial Intelligence which has proven to be extremely useful when it comes to detecting cyber threats based on analyzing data and identifying a threat before it exploits a vulnerability in your information systems.
Machine Learning enables computers to use and adapt algorithms based on the data received, learning from it, and understanding the consequent improvements required. In a cybersecurity context, this will mean that machine learning is enabling the computer to predict threats and observe any anomalies with a lot more accuracy than any human can.
Traditional technology relies too much on past data and cannot improvise in the way that AI can. Conventional technology cannot keep up with the new mechanisms and tricks of hackers the way AI can. Additionally, the volume of cyber threats people has to deal with daily is too much for humans and is best dealt with by AI.
Passwords have always been a very fragile control when it comes to security. And they are often the only barrier between cybercriminals and our accounts. Let’s face reality, most of us are quite lazy with our passwords – often using the same one across multiple accounts, relying on the same password since ages, keeping account of them neatly as a draft message in our device, etc. Biometric authentication has been tested as an alternative to passwords, but it is not very convenient, and hackers can easily circumvent this, too. For example, a face recognition system can be irritating to use when it can’t recognize you because of a new hairstyle or when wearing a hat. Attackers can also get through it by using your images from Facebook or IG.
Developers are using AI to enhance biometric authentication and get rid of its imperfections to make it a reliable system. Apple’s face recognition technology, used on its iPhone X devices, is one example. Called ‘Face ID,’ the technology works by processing the user’s facial features through built-in infra-red sensors and neural engines. The AI software creates a sophisticated model of the user’s face by identifying key correlations and patterns. Apple claims that, with this technology, there's only one-in-a-million chance of fooling the AI and opening your device with another face. The AI software architecture can also work in different lighting conditions and compensate for changes like getting a new hairstyle, growing facial hair, wearing a hat, etc.AI-ML In Phishing Detection And Prevention Control
One of the most commonly used cyber-attack methods, where hackers try to deliver their payload using a phishing attack, is phishing. Phishing emails are extremely prevalent; one in every 99 emails is a phishing attack. Fortunately, AI-ML may play a significant role in preventing and deterring phishing attacks.
AI-ML can detect and track more than 10,000 active phishing sources and react and remediate much quicker than humans can. Also, AI-ML works at scanning phishing threats from all over the world, and there is no restriction of its understanding of phishing campaigns to any specific geographical area. AI has made it possible to differentiate between a fake website and a legitimate one quickly.Usage of AI-ML In Vulnerability Management
Within this year alone, there have been over 2,000 unique vulnerabilities reported. Managing all of these with human resources or traditional technology is extremely difficult. AI, however, can tackle this with a lot more ease.
Systems based on AI-ML do not wait for a vulnerability to be exploited by online threats. Instead, these AI-based systems proactively look for potential vulnerabilities in organizational information systems, and they do so by effectively combining multiple factors, such as hackers’ discussions on the dark web, reputation of the hacker, patterns used, etc. These systems can analyze these factors and use the information to determine when and how the threat might make its way to vulnerable targets.Network Security & Artificial Intelligence
Two important parts of network security are the creation of security policy and figuring out an organization’s network topography. Typically, both of these activities are very time-consuming. Now, we can use AI to expedite these processes, which it does by observing and learning network traffic patterns as well as suggesting security policies. That does not only save time but also a lot of effort and resources which we can instead apply to areas of technological development and advancement.
6. Behavioral Analytics with AI
Another promising enhancement of security by AI comes from its behavioral analytics ability. What this means is that ML algorithms can learn and create a pattern of your behavior by analyzing how you usually use your device and online platforms. The details can include everything from your typical login times and IP addresses to your typing and scrolling patterns.
If at any time, the AI algorithms notice unusual activities or any behavior that falls outside your standard patterns, it can flag it as being done by a suspicious user or even block the user. The activities that tick off the AI algorithms can be anything from large online purchases shipped to addresses other than yours, a sudden spike in document download from your archived folders, or a sudden change in your typing speed.
So, is AI an answer to all my cybersecurity problems?
While the thought of entirely letting AI takeover is very tempting, we must remember that AI consists of a lot of things and, therefore, is very versatile. While AI is doing wonders for cybersecurity, it is also making its way into the employ of hackers for malicious purposes. In the wrong hands, it can do exponential damage and become an even stronger threat to cybersecurity.
As technology evolves, the adversaries are also enhancing their attack methods, tools, and techniques to exploit individuals and organizations. There’s no doubt that Artificial Intelligence is incredibly useful, but it is somewhat of a double-edged sword. AI-ML can be used to detect and prevent the attacks before it takes place. As AI sees more advancement, we will be witnesses to how far we can take this technology to be both a boon and a bane to cybersecurity and the society in general.