How AI is Improving Cybersecurity with Real-Time Threat Detection

• Blockchain Council
• November 03, 2024

Artificial intelligence (AI) is increasingly contributing to improving cybersecurity, especially when it comes to spotting and reacting to threats in real-time. With the rise in the complexity of cyberattacks, AI’s ability to quickly analyze large sets of data and notice patterns that may be overlooked by older methods has made it an essential tool in today’s cybersecurity efforts.

The Rising Demand for AI in Security

Cyber threats are becoming more obvious, as attacks are not only happening more often but are also getting harder to predict. While human-led approaches remain valuable, they often can’t keep pace with the massive amount of data and the speed at which threats appear. Traditional rule-based systems sometimes fail to keep up with modern cyberattacks, especially those that change and adapt quickly.

AI and machine learning (ML) have stepped in to handle this challenge. These technologies provide the ability to detect threats as they happen, processing large amounts of data at speeds much faster than human analysts. AI systems can constantly monitor networks, looking for strange patterns that might indicate possible risks before they cause harm. This is particularly useful for spotting new or unknown vulnerabilities, often called “zero-day” attacks.

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Detecting Threats in Real Time

One major benefit of AI in cybersecurity is its ability to identify threats immediately. Instead of relying on occasional system checks, AI keeps analyzing network activity, user behaviors, and system functions continuously, spotting unusual actions that could hint at a security issue.

For instance, AI can detect things like odd login times, strange file transfers, or efforts to access restricted sections of a network. In a recent case, an AI-driven security system noticed a laptop trying to connect to an unauthorized public Wi-Fi network. It immediately disconnected the device and alerted the team, preventing a potential breach or malware infection. This kind of quick response helps organizations react to threats as they occur, limiting possible damage.

Recognizing Patterns and Monitoring User Behavior

AI excels at recognizing patterns and learning what is considered normal behavior for both devices and users. By studying historical data, AI can establish a baseline of typical actions across a network. Once it knows what normal looks like, it can quickly spot unusual activities that may signal an attack. For example, if a user suddenly shows much higher activity than usual, it could be a sign of a security breach or unauthorized data access.

User behavior analysis plays a critical role here. AI systems observe user activities, watching for signs like repeated failed login attempts, accessing unexpected files, or sudden large data transfers. This constant monitoring helps reduce the chance of unnoticed attacks, allowing quicker responses and giving attackers less time to exploit any weak points.

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Automating the Response to Threats

Speed is vital when responding to threats. AI systems handle much of the process automatically, reducing how much humans need to be involved. When AI spots a potential threat, it can immediately isolate compromised systems, block harmful IP addresses, and stop further access to the network.

In 2023, Akamai successfully defended a major U.S. financial institution from one of the largest Distributed Denial of Service (DDoS) attacks recorded. The attack reached a peak of 633.7 Gbps and 55.1 million packets per second, but the AI-driven Prolexic platform detected and neutralized the threat within minutes The AI could adapt in real-time to block harmful traffic, lightening the load on the servers. This rapid response is becoming more necessary as cyberattacks grow in complexity.

AI also helps security teams by prioritizing which threats to address first. With many alerts coming in, AI ensures that the most critical issues get handled first. This relieves some of the pressure on security teams, allowing them to focus their efforts on the most important threats.

Defending Against AI-Driven Attacks

As AI strengthens defenses, attackers are also using it to their advantage. Hackers now use AI to automate attacks and slip past defenses. For instance, AI-generated phishing attacks are becoming more difficult for traditional security measures to catch. Additionally, hackers use AI in brute force attacks to quickly guess passwords and break into systems.

To defend against these AI-driven attacks, security systems must stay ahead. Companies now use AI to detect when attacks are being automated and to spot unusual patterns that might suggest AI-generated threats. This back-and-forth between attackers and defenders is likely to continue, but AI’s ability to adapt gives it a strong position in facing these new challenges.

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Real-Life AI Uses in Cybersecurity

Several real-world examples show how AI is becoming crucial in protecting against cyber threats. 

In recent months, companies such as Google Cloud have released AI-enhanced security solutions, helping businesses deal with evolving threats more efficiently. These systems analyze data from various sources, identifying new trends like the increase in phishing attacks. This early detection lets companies prepare before the attacks become widespread.

Similarly, firms like Palo Alto Networks are incorporating AI into their security platforms to help with risk assessment and threat prioritization. This AI-driven analysis lets security teams concentrate on the most critical problems, leading to better management of overall security.

For example, Google’s Security Operations suite uses AI to provide real-time insights into threats. By pulling data from different places, AI can offer advice on how to respond to specific attacks, helping teams make quicker decisions and reduce the impact of security incidents.

Challenges with AI in Cybersecurity

While AI offers many advantages, there are some challenges. For AI systems to work well, they need access to large and accurate datasets. If the data used to train the AI is incomplete or biased, the system might miss real threats or mistakenly flag harmless activity. Moreover, AI itself can become a target for hackers, who may attempt to exploit weaknesses in the algorithms.

Another issue is the lack of clarity in how some AI systems reach their conclusions, which can make it hard for security teams to fully understand the decisions AI makes. This can lead to frustration, especially if AI incorrectly flags something as a threat or fails to spot a real one. That’s why it’s important to combine AI with human judgment for a balanced approach to cybersecurity.

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Final Thoughts

AI is significantly changing the way organizations protect themselves from cyber threats. Its ability to identify risks in real time, automate responses, and recognize unusual behavior helps companies react faster and more effectively to cyberattacks. Although AI has some limitations, it remains a critical part of modern cybersecurity. By combining AI with human oversight, organizations can better defend themselves against increasingly sophisticated cybercriminals.

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