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Blockchain Council -
November 3, 2024
Artificial intelligence (AI) is increasingly contributing to improving cybersecurityespecially when it comes to detecting and responding to threats in real time. With the increasing complexity of cyberattacks, AI’s ability to quickly analyze large data sets and detect patterns that might be overlooked by older methods has made it an essential tool in today’s cybersecurity efforts.
The growing demand for AI in security
Cyber threats are becoming more and more evident, as attacks are not only more frequent, but also more difficult to predict. While human approaches remain useful, they often fail to keep pace with the enormous amount of data and the speed at which threats emerge. Traditional rules-based systems sometimes fail to keep pace with modern cyberattacks, especially those that rapidly evolve and adapt.
AI And machine learning (ML) stepped up to meet this challenge. These technologies provide the ability to detect threats as they occur, processing large amounts of data at speeds much faster than human analysts. AI systems can continuously monitor networks, looking for strange patterns that might indicate possible risks before they cause damage. This is particularly useful for detecting new or unknown vulnerabilities, often referred to as zero-day attacks.
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Real-time threat detection
One of the main benefits of AI in cybersecurity is its ability to immediately identify threats. Instead of relying on occasional system checks, AI continues to continuously analyze network activity, user behaviors, and system functions, spotting unusual actions that might suggest a security issue. security.
For example, AI can detect things like strange connection times, strange file transfers, or efforts to access restricted sections of a network. In a recent case, an AI-based security system detected a laptop trying to connect to an unauthorized public Wi-Fi network. He immediately disconnected the device and alerted the team, preventing a potential breach or malware infection. This type of rapid response helps organizations respond to threats as soon as they arise, thereby limiting possible damage.
Recognize patterns and monitor user behavior
AI excels at recognizing patterns and learning what is considered normal behavior for devices and users. By studying historical data, AI can establish a baseline of typical actions on a network. Once it knows what normal looks like, it can quickly spot unusual activities that might signal an attack. For example, if a user suddenly exhibits much higher activity than usual, this could be a sign of a security breach or unauthorized access to data.
User behavior analysis plays a vital role here. AI systems observe user activities, watching for signs such as repeated failed login attempts, access to unexpected files, or sudden transfers of large data. This constant monitoring helps reduce the risk of unnoticed attacks, enabling faster responses and giving attackers less time to exploit weak points.
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Threat Response Automation
Speed is essential to responding to threats. AI systems automatically handle much of the process, reducing human involvement. When AI detects a potential threat, it can immediately isolate compromised systems, block harmful IP addresses, and prevent network access.
In 2023, Akamai successfully defended a major U.S. financial institution against one of the largest distributed denial of service (DDoS) attacks on record. The attack peaked at 633.7 Gbps and 55.1 million packets per second, but the AI-powered Prolexic platform detected and neutralized the threat within minutes. The AI was able to adapt in real time to block harmful traffic, thereby easing the load on servers. This rapid response becomes increasingly necessary as cyberattacks become more complex.
AI also helps security teams by prioritizing which threats to address first. With many alerts received, AI ensures that the most critical issues are addressed first. This relieves some of the pressure on security teams, allowing them to focus their efforts on the most important threats.
Defend against AI-based attacks
As AI strengthens defenses, attackers also use it to their advantage. Hackers are now using AI to automate attacks and bypass defenses. For example, AI-generated phishing attacks are becoming increasingly difficult for traditional security measures to detect. Additionally, hackers use AI in brute force attacks to quickly guess passwords and break into systems.
To defend against these AI-based attacks, security systems must stay one step ahead. Companies are now using AI to detect when attacks are 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 puts it in a strong position to face these new challenges.
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Real-world uses of AI in cybersecurity
Several concrete examples show how crucial AI is becoming in protecting against cyber threats.
In recent months, companies such as Google Cloud have launched AI-enhanced security solutions, helping businesses deal with evolving threats more effectively. These systems analyze data from various sources and identify new trends like the increase in phishing attacks. This early detection allows businesses to prepare before attacks become widespread.
Similarly, companies like Palo Alto Networks are integrating AI into their security platforms to help with risk assessment and threat prioritization. This AI-powered analysis allows security teams to focus on the most critical issues, leading to better overall security management.
For example, Google’s Security Operations suite uses AI to provide real-time threat intelligence. By pulling data from different locations, AI can provide advice on how to respond to specific attacks, helping teams make faster decisions and reduce the impact of security incidents.
The Challenges of AI in Cybersecurity
While AI offers many benefits, it does present some challenges. For AI systems to work properly, they need access to large and accurate data sets. If the data used to train the AI is incomplete or biased, the system could miss real threats or mistakenly report harmless activity. Additionally, AI itself can become a target for hackers, who could attempt to exploit weaknesses in the algorithms.
Another issue is the lack of clarity in how some AI systems reach their conclusions, which can prevent security teams from fully understanding the decisions the AI makes. This can lead to frustration, especially if the AI wrongly flags something as a threat or fails to spot a real one. This is why it is 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 against cyber threats. Its ability to identify risks in real time, automate responses and recognize unusual behavior helps businesses respond more quickly and effectively to cyberattacks. Although AI has some limitations, it remains an essential part of modern cybersecurity. By combining AI and human monitoring, organizations can better defend against increasingly sophisticated cybercriminals.
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