In this session, we’ll explore how Artificial Intelligence (AI) can enhance cybersecurity by extracting attack vector linked to vulnerabilities, offering a more proactive and efficient approach. Traditional methods of detecting vulnerabilities rely on security researchers manually reverse-engineering attack traffic and emulating potential attack behaviors. While effective, this process is time-consuming and exposes systems to risk during testing, increasing the likelihood of compromise in production environments.

AI addresses this challenge by automating the detection of attack vector and behaviors tied to specific vulnerabilities. This capability enables security teams to identify suspicious activities without constant manual intervention or exposing live systems. By integrating AI into vulnerability prevention, organizations can reduce the risk of attacks in production environments. AI-driven systems can autonomously flag suspicious behaviors or protocols indicative of an active threat.

This AI-powered approach enhances vulnerability prevention, offering stronger and more automated protection, reducing the potential for system compromise and providing a higher level of security.

You may have seen a summary of various attack techniques on MITRE ATT&CK Enterprise. Do you know what are the top 10 ICS metrics that can be exploited?

You might have seen the CWE Top 25, but have you looked at the OT/ICS CWE Top?

We have compiled the top 10 most commonly used techniques (TIDs) in OT and the top 20 software weaknesses (CWE) that occur in OT.

More importantly, during the presentation, we will reveal multiple publicly disclosed ICS vulnerabilities as examples, explaining each vulnerability and exploitation method in a simple and easy-to-understand way, allowing the audience to clearly understand the threats to industrial control systems.