A Glimpse of AI in Cybersecurity: Its Applications, Benefits & Also Drawbacks
Artificial intelligence, or simply AI, has been one of the most hyped technologies dominating the world in recent years, especially in the post-Covid-19 world where digitalization is being accelerated. And these days, the impact of AI is borderless as it is unstoppably expanding across industries, ranging from healthcare and education to recruitment, information technology, and more. All sectors are attempting to get started with artificial intelligence and benefit from it. So, it is no surprise that AI has also started to show up in the cybersecurity world.
According to a report by MarketandMarket, AI in the cyber security market is predicted to grow sharply at the highest CAGR of 23.3%, from only $8.8 billion in 2019 to $38.2 billion by 2026. As the report implies, the major driver for this growth lies within the accelerating adoption of the Internet of Things (IoT), and the rising number of connected devices has obviously led to the expansion of the attack surface, resulting in cyber threats and phishing attacks. In addition, the complexity and the huge volume of cyberattacks have also created a great need for advanced and sophisticated security solutions, which is where AI-based security systems kick in and help.
The Applications & Benefits of AI in Cybersecurity
Basically, AI technologies, such as machine learning, natural language processing, and deep learning, are being used to help security teams automate repetitive tasks, speed up threat detection and response, as well as improve the accuracy of their actions, eventually strengthening the security posture against security issues and cyber-attacks. These are a few cases of how organizations are integrating AI into their cybersecurity ecosystem:
Automated Threat Detection & Incident Response
Artificial intelligence is created to simulate human intelligence, but at an advanced level, so it is capable of analyzing large data sets more quickly and effectively than cyber security professionals can. When it comes to threat detection, AI systems can be employed to automatically identify anomalies occurring in computer systems. Machine learning algorithms are able to learn and detect threats more accurately over time, making them a valuable tool against sophisticated attacks that may go undetected by traditional security solutions. In addition, machine learning can also be used to develop anomaly detection systems that can identify unusual user behaviors of malware or ransomware attacks that may indicate a security breach risk. Once suspicious activity is detected, the system can then take appropriate actions, such as blocking malicious traffic or notifying the information security teams. With AI-powered systems automating threat detection and response, businesses can free their security analysts from time-consuming, repetitive tasks and let them prioritize their time for other value-added tasks.
Breach Risk Prediction
Apart from detecting current breaches, AI and machine learning systems can identify suspicious activity that may indicate malicious intent and predict security risks by studying and analyzing past data. As a result, businesses can take preventive measures in advance to deal with malicious actors even before they strike. For instance, if a company knows that its systems are vulnerable to a certain type of attack, it can take steps to patch the security holes and shore up its defenses against such threats. It is the advanced algorithms that make AI-powered solutions growable to keep abreast with cyber criminals and their emerging threats.
Reduce False Positives & False Negatives
In the cybersecurity world, a false positive is when a security system incorrectly flags a benign file or activity as malicious. A false negative is when a malicious file or activity goes undetected by the security system. This can happen when a rule-based system is used because it cannot always accurately identify the difference between malicious and benign activities. Both of these scenarios can pose serious risks to businesses, and therefore AI kicks in.
By analyzing huge amounts of risk data more effectively than humans can, AI-based systems can be trained to automatically fine-tune their own rules and configurations to reduce false positives and false negatives, which helps cybersecurity professionals to minimize the wrong security alerts and focus on remediating real threats.
Enhance Endpoint Security
In today’s digital era, we are relying heavily on laptops, smartphones, and other devices for our personal and business life. Unfortunately, these endpoints are also the weakest link in the security chain as they can be easily lost or stolen, making them a prime target for cyber-criminals. Artificial intelligence comes into practice to scan and monitor endpoints for malicious activity, as well as detect vulnerabilities that can be exploited by threat actors. In addition, AI-based systems can also be employed to automatically deploy security patches to endpoints to close any gaps in the security posture. This helps to reduce the attack surface, thereby leaving cybercriminals fewer or no chance to penetrate a company’s network.
Another area where AI is being used in cybersecurity is authentication. According to Ed Tech Magazine, AI is the pragmatic solution to outsmart hackers, strengthen passwords, and further secure users’ authentication against cybercrimes.
In order to gain authorized access to sensitive data, businesses usually rely on passwords as the primary method of protection. With so many passwords to remember for different accounts, it is no wonder that many of us are using the same password for multiple accounts or using simple and easy-to-guess passwords. This can be a disaster waiting to occur, as all it takes is one data breach at an online service to expose all your other accounts that use the same password. At this point, AI-driven tools come in and help to solve the problem by detecting reused or weak passwords across multiple accounts and generating strong and unique ones for each. AI-powered systems can also identify patterns of password reuse and notify users when their passwords are at risk of being compromised by assessing data from past breaches.
It does not stop there; AI enables continuous authentication by spotting any anomalies that could indicate unauthorized access by observing biometric behaviors, such as the way you type on your keyboard or how you move your mouse. This helps to prevent account takeovers, even if the hacker has your password.
User & Entity Behavior Analytics
Simply known as UEBA, this is a type of security solution that uses machine learning algorithms to detect anomalies in user behavior. By studying the historical data of users’ activity, UEBA systems can identify patterns that may indicate malicious intent, such as a sudden change in file-access patterns or login times. This information can then be used to generate alerts so that further investigation can be done to determine if there is indeed a security breach risk. In addition, UEBA systems can also be used to monitor for insider threats, as they are able to detect when a user’s behavior deviates from the norm.
Prevent Phishing Attacks
Phishing is one of the most common cyber threats that exist today. In this social engineering attack, the attacker impersonates a trustworthy entity to trick victims into divulging sensitive information, such as login credentials or financial details. In order to mount a successful phishing attack, attackers need to gather enough information about their targets, such as their names, job title, and contact details. With the help of AI-based tools, businesses can prevent these attacks before they happen by automatically analyzing huge volumes of data to detect these kinds of patterns and anomalies. AI can also be used to flag suspicious emails and prevent them from reaching users’ inboxes in the first place.
Potential Drawbacks of AI in Cybersecurity
In general, AI can be used in cybersecurity in two main ways: as a part of the security solution or as an attacker. In other words, AI-based security systems are designed to protect against cyber threats, while attackers can also utilize AI technologies to conduct malicious activities such as phishing attacks and fraud. Ultimately, AI is basically a technology, and it is neither good nor bad, no right or wrong. The nature of artificial intelligence is neutral, and it still is when it comes to the “cybersecurity battle.” So, the question is: can AI be used to do more harm than good in cybersecurity? There are certain risks when using AI for cybersecurity. However, it falls to us to control and manage them.
AI Can Be Taken Advantage of for Cyberattacks
First of all, cybercriminals can leverage AI systems to create more advanced attacks, such as automated threats, against our security infrastructure and solutions. In particular, they can utilize the capability of AI and machine learning to automate the process of launching attacks, for example, by creating bots that can carry out phishing campaigns at scale.
AI Can Be Tricked
Secondly, AI-based security systems can be fooled by adversarial attacks. Adversarial attacks are basically when an attacker deliberately crafts data that is designed to fool a machine learning algorithm. For example, an attacker can create a fake website that looks identical to a legitimate one and is able to trick the AI-based security system into thinking it is the real thing.
AI Can Make Mistakes
Thirdly, AI systems are not perfect, and they can make mistakes as well. In the context of security, this means that AI-based systems can generate false positives, which are basically when the system incorrectly flags a benign activity as malicious. This may happen for various reasons, such as incorrect data labeling or overfitting of the training data. False positives can be extremely costly for businesses, as they can lead to disruptions in business operations and loss of productivity. Even though it is true that AI can help us decrease false positives, if something goes wrong, it could backfire.
AI Can Be Biased
Last but not least, AI systems can be biased. This means that the results of these systems can be skewed based on the training data that is used. For example, if the training data is biased, then the AI system will likely be biased as well. This can have serious implications for security, as it can lead to false negatives, which are basically when the system fails to flag malicious activity as such.
Topics: Artificial Intelligence