Why is AI the Key to Cutting-Edge Cybersecurity?

• By Hardik Shah
• 20-12-2022
• Artificial Intelligence

In the age of digitization, you’re connected to the internet for most of your day. So, the global threat landscape has also changed. Today, internet users face many challenges in terms of cyber attacks. On the one hand, there are automated bots aimed at infecting consumer devices. But on the other hand, there are social engineering attacks to fool users into giving money or any confidential information. Dealing with such a variety of attacks is a massive issue.

Currently, there are more than 13 billion connected devices worldwide, and it will reach 29 million users by 2030. So, the attacker or intruder has got a large surface. Also, every day or the other, you’ve new types of cybercrime that cost around $6 trillion annually, roughly 1% of the global GDP. In this scenario, you need a proactive approach to cybersecurity, where AI has a definitive role. It learns from historical data and identifies patterns/trends for cyber attacks.

With such patterns and trends, AI can make accurate predictions of cyber attacks. You can also use AI to respond to threats in quicker timescales. In this manner, AI in cybersecurity aims to reduce the risk of breaches and create a robust security framework for businesses. That’s why AI in cybersecurity will grow at a CAGR of 23.3% and become a $38.2 billion entity by 2026. This article will explore the role of AI and its various use cases in cutting-edge cybersecurity.

Use cases of AI in cybersecurity

Vulnerability management

Nowadays, every business is on a digital platform, which is affected by more than 2000 vulnerabilities yearly. Managing all these vulnerabilities with human efforts would be nearly impossible. That’s where AI can help you out immensely.

AI-based algorithms continuously track for any flaws and security vulnerabilities in your IT ecosystem. Using dark web hacker forums, hacker credibility, security trends, etc., AI conducts vulnerability assessments to protect your IT ecosystem from security loopholes.

Unknown threat identification

In today’s time, every day, you hear about a new type of cyber attack. So, it would be impossible for companies to track every security attack manually. Moreover, unknown threats can sometimes impose significant damage on your applications.

Here, AI comes to your rescue as it can continuously track security threats and prevent them from entering your ecosystem. It can also predict future threats by understanding the historical data related to cyber attacks to identify the patterns.

Password protection and authentication

Passwords have always been one of the weak links for web app security. For example, 81% of data breaches across the globe are due to poor passwords. So, biometric authentication and two-step verification got introduced to counter this threat. However, attackers have also found a way to breach such securities, so developers have a headache dealing with this scenario.

For this purpose, developers thought of developing an AI-based password mechanism that gave birth to face identification, which Apple introduced after iPhone X. Face ID detects users' facial features through infrared sensors and neural networks developed through AI. With this technique, the probability of a breach has become one in a million, protecting password security.

Phishing attack prevention and detection

Phishing is one of the widely used cyber attacks where a hacker tries to trick the user into revealing some confidential information. One in every 99 emails is a phishing attempt, and therefore, it becomes essential for businesses to detect and prevent such attacks.

Here, AI and machine learning algorithms can save you from such a malicious threat. It can track and identify more than 10,000 phishing attack sources. AI can also respond to those threats much faster compared to humans. It can quickly identify a fake and a helpful website, which saves you from comprising any sensitive information related to your business.

Fast and accurate anomaly detection

AI can detect malicious anomalies in network traffic. Applying machine learning algorithms to network data lets, you know precisely the peculiarities distorting your ecosystem. Security Information and Event Management (SIEM) and Security Orchestration, Automation, and Response (SOAR) are AI-based techniques that help increase your ecosystem's security.

Any network generates a lot of data. SIEM collects and aggregates those data to identify, categorize and analyze incidents and events with the help of machine learning algorithms. In comparison, SOAR helps security teams to catch security alerts and provides a rapid incident response. So, with AI development services, you can accurately detect various anomalies.

False positive reduction

When you have too many false positives at your disposal, it can divert you from fixing the real issues. However, with AI, you can reduce false positives, which allows your team to focus on real security threats. In addition, it can analyze many events and identify core security threats.

Another great thing about AI-based algorithms is that they continuously learn from historical data. You can feed the latest security vulnerabilities into the AI ecosystem, and AI can conduct a behavior analysis of that. So, the next time it detects such attacks, it will notify you. Also, with continuous learning, AI knows which attacks are real threats and which are false positives.

Automated bot recognition

Bots, in recent years, have become significant security threats to business openers across the globe. Recently, you must have heard Elon Musk highlighting this issue during Twitter’s acquisition. So, the danger is real and humongous.

AI and machine learning can come to your rescue as they can differentiate between good bots, evil bots, and humans. Through behavior pattern analysis, AI knows what an ordinary user journey looks like. So, based on that, it can identify abnormal behavior and detect that as bots.

Wrapping up things

With so many use cases of AI on the cybersecurity horizon, it’s evident that AI and machine learning-based algorithms are powerful tools to counter cyber threats. AI can read historical data, identify patterns and trends and predict future threats. It can also alert businesses about new threats and prevent the ecosystem from malicious attacks.

The proactiveness of AI will be the key to cutting-edge cybersecurity going forward. With many benefits and use cases of AI in cybersecurity known to you, it’s time to implement it into your ecosystem and reap rich rewards.