Strengthening Vulnerabilities

Automated threat detection 

AI algorithms can scan website traffic and user behavior to detect unusual patterns and anomalies.

One of the main advantages of using AI to detect threats is that it can process massive volumes of data very quickly. Traditional methods, where security analysts comb through data manually, are time-consuming and inefficient. Human error can also increase the chances of security concerns being overlooked. With AI algorithms, this process can be automated, drastically reducing the time that it takes to identify potential threats. 

AI can also identify a range of threats from malware exploiting vulnerabilities on a site to identify risky behavior. One study found that cybercriminals can penetrate company networks 93 percent of the time; but because AI is fast learning, it can recognize patterns and characteristics based on previous cyberattacks.

AI can predict future attacks and take measures to prevent them. For instance, if the AI identifies a pattern of suspicious activity, it can alert website administrators. AI can scan website traffic and user behavior by analyzing data collected from various sources, such as server logs, network traffic and user activity logs to identify any unusual behavior. This is how AI can identify whether a user has logged on from a different location than normal.

While cybersecurity experts may be practiced at recognizing patterns in datasets, machine learning models can analyze much larger and more complex datasets with ease, which makes them ideal for picking up anomalies.

Common techniques include clustering or deep learning on a machine learning model. The clustering model groups similar data points together and identifies outliers that do not fit into any of the clusters. Deep learning algorithms have been used to detect malware and identify phishing emails, among other applications.

Enhanced user authentication

Traditionally, passwords have been the main method of user authentication, but poor password hygiene is common. One study found that 53 percent of people use one password across multiple accounts, and even use the same password for both personal and professional security.

Despite the security risks they pose, the most-used passwords are 123456 and password.

Research found that a seven-character password could be cracked in just a few minutes. However, AI-centered authentication processes like facial recognition and biometric authentication are much more difficult to hack.

Making use of these security processes is more efficient, especially when professionals need to remember multiple complex passwords. One study found that biometric safety measures are also less likely to be hacked than traditional passwords.

Biometric authentication is fallible. Some researchers have found that a false positive (when the system incorrectly allows someone access) or a false negative (when a system cannot detect a valid fingerprint) occurs about 1 percent of the time.

However, machine learning makes it possible for these authentication measures to continuously improve. For devices that use fingerprint scanning, skin conditions, scarring on fingerprints or a small fingerprint surface area have previously posed challenges.

But using techniques such as artificial neural networks and deep learning has allowed devices to adapt to these conditions over time.

Previously, facial recognition posed some challenges as devices could not process a range of skin tones and features. But there has been significant improvement in this area. During the COVID-19 pandemic, developers were able to create algorithms that would allow AI to recognize a face, even with a mask.

Using biometric methods tends to be more secure than traditional authentication methods like passwords because it is much harder to replicate a user's behavioral patterns.

Real-time response  

Real-time analysis refers to artificial intelligence’s ability to accurately monitor and analyze data in real-time to create notifications under certain circumstances. This technology has applications in several fields, including security.

A 2021 report found that it took financial institutions an average of 233 days to detect and contain a data breach, and the average cost of each data breach at a financial institution is nearly US$6 million. Not only do the financial implications seriously impact these businesses but the delay in detecting data breaches can seriously impact reputation and customer faith.

Immediate responses by AI can include disabling access to certain pages or services that are under attack, which can help prevent any further access and limit the damage of the attack.

AI can also block traffic from certain IP addresses it has found to be malicious or associated with suspicious activities. Research found that bulk suspicious IP addresses are structured in similar ways, making it possible for AI to predict which IP addresses are potentially harmful.

AI can make use of active firewalls to detect and block attacks in real time, without the need for human intervention.

Predictive threat analysis  

The most common cyber threat facing U.S. businesses is phishing, but information from these past attacks can be a valuable resource to help prevent future phishing instances.

While many people know to be aware of any suspicious-looking emails or websites, phishing attacks are becoming increasingly advanced. In 2022 there was a 61 percent increase in phishing attacks compared to the previous year.

This study also makes an important note that most phishing attacks are successful because of social engineering.

Phishers capitalize on anxiety by making an urgent or important request, which pressures people into deciding quickly, which could have detrimental results. In a recent phishing attack on Activision, a video game publisher, an employee clicked on an SMS that had the title” Employment Status: Under Review.”

The average cost of a phishing attack in 2022 was US$4.9 million, a 16 percent increase from 2021. However, using AI can eliminate these risks by analyzing factors including the sender, recipient, location, and suspicious attachments or links to determine whether it is a risky message and provide a warning before the recipient acts on it.

AI can also analyze the content within the email to look for language patterns or characteristics that are commonly used in phishing emails. More advanced phishing attempts are structured to look safe to the general computer user, but AI deep learning can adapt to these new techniques to identify even the most impressive-looking phishing attempts.

For example, a new tool using generative AI, which can create various types of content like text and images, claims that it is successful in detecting 99 percent of phishing attempts. The AI tool can create thousands of emails applying specific tactics used in previous attacks, referencing these examples to detect future phishing attempts.

Vulnerability scanning  

AI can identify potential security weaknesses that could be exploited by hackers that might not otherwise be detected by data analysts. As cybersecurity systems become more advanced, so do the hackers that attempt to take advantage of any weak points.

One report found that cybercrime will cost businesses over US$10 trillion by 2025. Vulnerability scanning ensures that any security issues can be addressed before they harm a business. AI can scan an entire system to determine if there are critical security concerns that need to be addressed immediately. AI can provide a risk score to help cybersecurity experts determine which order to address these security concerns.

AI algorithms can monitor network traffic and system logins to determine if there are any unusual patterns that may indicate a data breach. Based on past experiences of cybersecurity attacks, AI can be trained to identify suspicious behavior and predict future threats before they become a cause for concern.

It was recently found that AI can be used to automatically detect vulnerable data in query-based systems, such as Google Maps or Facebook. Previously, these vulnerabilities had to be discovered manually, making it a challenging and time-consuming process.

AI can be used in penetration testing at various stages. Penetration testing typically begins by gathering information from publicly accessible sources. AI can quickly gather this information, analyze it and provide recommendations.

Following the information gathering phase, AI can also be used to test these vulnerabilities and attempt to gain access. Cybersecurity experts can feed AI systems weaknesses to exploit, and AI systems can provide feedback on these attempts.

AI can streamline the reporting process to provide the organization actionable advice. This vulnerability scanning can reduce fatigue in cybersecurity teams. Rather than having to undergo the time-consuming task of identifying the risks, they can focus on improving these security systems.