S01E04: Cybersecurity – present and future of AI to detect cyber threats and attacks

Punto AI s01e04 Security Cover
The Focus
S01E04: Cybersecurity - present and future of AI to detect cyber threats and attacks
Pill extracted from episode S01E04 of the "Disruptive Talks" podcast: see the complete program of the episode on this page.

Today we live constantly connected online, for work, study, entertainment. Our lives are immersed in digital. But this unfortunately also attracts hackers. In Italy alone, serious cyber attacks grew by 169% in 2022 (Clusit Report 2023)!

To protect ourselves, we must use the most advanced tools. And this is where artificial intelligence makes a difference, with its ability to quickly process large amounts of data and spot anomalies.

How AI can help us

Thousands of pieces of information are generated in our systems every second. It is impossible for a human to monitor everything and understand if there is an attack. AI, however, succeeds.

With machine learning algorithms trained on normal network models, AI can quickly identify anomalous traffic, suspicious access, infected files. And thus block an attack in the bud, before it causes damage!

But not only. Today, AI is also used to thoroughly analyze software and applications, looking for flaws in codes that could be exploited by hackers. By finding these vulnerabilities, systems can be hardened.

Threat detection and vulnerability assessment

Let's look in more detail at two main ways in which AI is used in cybersecurity:

Threat detection

Threat detection, or the identification of cyber threats, is one of the most widespread uses of AI in the cybersecurity field.

Artificial intelligence algorithms are trained on large quantities of data to learn to recognize the typical "signatures" of different types of cyber attacks and malware. This allows them to analyze network traffic in real time and identify anomalous patterns that could indicate an attack in progress.

For example, if they detect an anomalous spike in traffic coming from a certain IP address, or they notice repeated attempts to access a server with different passwords, they can classify these activities as suspicious and send a real-time alert to security analysts.

AI-based threat detection systems integrate supervised, unsupervised and deep learning machine learning techniques to process terabytes of network data, detecting increasingly faster and more sophisticated threats.

Compared to traditional signature-based systems, AI can also identify zero-day attacks and unknown malware, thanks to its ability to recognize anomalies in traffic patterns.

The vulnerability assessment

Vulnerability assessment, on the other hand, aims to identify latent flaws and weaknesses in systems before they are exploited in an attack.

Again, AI is used to analyze large amounts of data relating to network configurations, application source code, installed software versions, etc., in search of known or unknown vulnerabilities.

For example, machine learning algorithms can scan millions of lines of code to identify bugs, logical flaws, backdoors, or other flaws that could be exploited by hackers. While deep learning techniques help analyze system and network configurations to find weaknesses in terms of permissions, authentication and encryption.

By proactively identifying these vulnerabilities, organizations can patch them before they are exploited, dramatically reducing the attack surface. Automating vulnerability assessment using AI greatly accelerates and enhances this crucial cybersecurity process.

The future of AI in cybersecurity

AI will be increasingly integrated into IT infrastructures as a central component of security. Machine learning algorithms will allow us to process and cross-reference enormous quantities of data in real time, identifying increasingly faster and more sophisticated threats.

Response automation will become more advanced, with AI agents capable of applying patches, isolating compromised components, modifying configurations to stop attacks in real time.

Hackers will also use AI, so security experts will need to constantly update themselves on new techniques. Ethical, transparent and inclusive AI systems will be needed to avoid prejudice and abuse.

In conclusion, the future of AI in cybersecurity is full of opportunities but also complex challenges. With forward-thinking minds and open hearts, we can build a safer and more prosperous digital tomorrow for all.


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