Implications of AI for cybersecurity

Many believe that artificial intelligence will save cybersecurity. As cybercriminals become increasingly sophisticated in their methods of attacking businesses, many believe that humans cannot keep up with the sheer volume of cyberattacks, let alone the sophisticated methods by which cybercriminals access networks.

The expansion of connectivity is one of the most significant obstacles faced by businesses. The rapid growth of cloud-based applications, the increasing number of Internet of Things (IoT)-connected devices, and the challenges of allowing an increasing number of people to work seamlessly from home have created an IT infrastructure that can no longer be managed manually.

AI and ML have therefore become essential technologies in information security, as they can rapidly analyze millions of events and identify a wide variety of threats.

This capability to analyze millions of events and compare what is observed to expected behavior enables threats such as zero-day vulnerabilities and phishing attacks to be quickly identified, and appropriate actions can be taken.

The benefit of both AI and ML is that they continuously learn and improve based on the results they are analyzing, drawing on past experiences and predicting cyber threats before they occur.

While some believe that AI will eventually eliminate the need for humans in the cybersecurity industry, many believe that while AI and ML are important tools, they are not the panacea when it comes to cybersecurity, and human input will always play a vital role.

how can ai strengthen your cybersecurity?

AI will play a vital role in enhancing the cybersecurity of both large and small businesses. Many businesses around the world are already utilizing AI to enhance their cybersecurity efforts; however, we can expect AI and ML to become more prevalent in commercial and personal cybersecurity software solutions.

AI is already assisting businesses in identifying and neutralizing cyberthreats, allowing them to stay on top of an ever-changing cybersecurity landscape.

Artificial intelligence (AI) learns by consuming billions of data points from both structured and unstructured sources. This data is quickly processed using machine learning and deep learning techniques, and artificial intelligence then enhances its knowledge, enabling the software to comprehend cyber security threats and risks.

The learning curve is steep because AI is capable of analyzing billions of data files. Using the insights gleaned from billions of data points and reasoning, AI software can identify any connections between threats such as malicious files, suspicious IP addresses, and unusual network activity.

The data can be analyzed in a matter of minutes, and the results can be reviewed and acted upon by cybersecurity professionals who are able to make quick, informed decisions.

This relationship between AI software and cybersecurity experts is maximizing the impact of AI in cybersecurity. There may one day be a time when AI can perform the duties of a cybersecurity professional, but that time appears to be quite distant. As a result of AI, analysts in the field of cyber security are now able to make quicker, more informed decisions, allowing them to perform their jobs more effectively.

adversarial ai

It should also be noted that while AI is being used by cybersecurity experts to combat cybercrime, cybercriminals are also turning to AI to accelerate their attacks, making them harder to detect and spread.

According to a report by Accenture, adversarial AI "causes machine learning models to misinterpret system inputs and act in a way that is advantageous to the attacker."

This contributes to the creation of a large number of false positives for AI-driven cybersecurity systems, thereby increasing the workload of cybersecurity analysts charged with separating actual attacks from those driven by AI.

In an article published on Medium, a student-led organization from Berkeley University in California discusses how adversarial AI could be applied to Face ID-based unlocking technology such as the Apple iPhone X. This feature relies on neural networks to recognize faces, making it susceptible to adversarial attacks. "People could create adversarial images to circumvent the Face ID security features."

Using machine learning and AI, cybercriminals are developing malware strains that are smarter and more adaptable, making them more difficult to detect.

The Wall Street Journal reported in 2019 that "Criminals used artificial intelligence-based software to impersonate a chief executive's voice and demand a fraudulent transfer of €220,000 ($243,000) in March in what cybercrime experts described as an unusual instance of artificial intelligence being used in hacking."

In a separate WSJ article, they describe the use of AI-powered malware designed to conceal itself until it reaches a particular target. "DeepLocker is capable of concealing any malicious payload within [harmless] software, and AI becomes the deciding factor for when to unlock the malicious behavior," said Marc Stoecklin, manager of IBM's Cognitive Cybersecurity Intelligence research.

Trustwave reported in 2020 that attacks on cloud services had more than doubled and now account for the third-highest number of targeted attacks, and that ransomware continues to grow, surpassing payment card data breaches for the first time.

cybersecurity advantages of ai

AI continues to play a vital role in cybersecurity, despite the fact that cybercriminals are increasingly employing it to launch more sophisticated attacks. In fact, one could argue that AI in cybersecurity is becoming essential if you want to keep up with the threat landscape and the sophistication of attacks occurring around the globe.

AI and machine learning, when combined, can help organizations keep up with the ever-changing landscape by automating threat detection, providing valuable insights to cybersecurity professionals, and responding more effectively than software-driven and manual cybersecurity techniques.

here are a few of the primary benefits of ai in cybersecurity:

new threat detection

The ability to quickly detect new threats is one of the most important aspects of using AI and ML in cybersecurity. Traditional cybersecurity software cannot keep up with the sheer number of new malware threats that are created every week.

AI enables cybersecurity systems to process these new threats rapidly, and because AI systems are constantly learning, they are being trained to detect malware, run pattern recognition, and detect even the tiniest behaviors that could indicate malware or ransomware, before they enter the system.

One of the primary benefits of AI in cybersecurity is the predictive analysis that cybersecurity professionals can extract from an AI-driven system. The system can acquire intelligence on new anomalies, cyberattacks, and prevention strategies by scraping articles, news stories, and studies.

prevent credit card fraud

The use of AI and ML by banks to prevent credit card fraud and safeguard customer information. AI is able to detect anomalous behavior, such as purchases made from a different device or unusual transactions, quickly.

Then, AI-powered systems can help to verify the credit card holder, thereby reducing fraudulent transactions.

bots-defeating

The majority of bot traffic is merely an annoyance for website owners, but bot traffic can also be hazardous.

Keeping up with the sheer number of bots is a task that is extremely challenging without AI. Manual response alone cannot keep up with the growing number of bots, but AI can assist in identifying the legitimacy of bots.

From account takeovers to stolen credentials to data fraud, bots can pose a serious threat to businesses if they go undetected. AI enables cybersecurity teams to analyze massive amounts of data and understand user navigation patterns on websites. Unusual behavior can be quickly identified, enabling cybersecurity experts to stay ahead of malicious bots.

prediction of breach hazard

Accounting for IT asset inventory, threat exposure, and controls' efficacy, AI-based systems can predict how and where you are most likely to be compromised, allowing you to allocate resources and tools to areas of weakness.

You can configure and improve your organization's cyber resilience by configuring and optimizing controls and processes based on insights derived from AI analysis.

The advantages of AI in cybersecurity extend far beyond these four areas, and as we move into 2022 and beyond, AI will become an integral and indispensable component of cybersecurity systems worldwide.