Generative AI has proven to be useful in many sectors, and cybersecurity is no exception. Organizations that use the internet for their operations tend to have many potential attack points that are challenging to monitor through traditional means.
Fortunately, cybersecurity professionals can use generative AI to build security tools that can analyze threat databases to identify threat patterns long before a cyberattack occurs.
It is ideal to have a proactive approach to cybersecurity, and generative AI provides avenues for this in the following ways:
1. Threat Data Analysis
As mentioned above, you can use generative AI to identify cyber threat signals based on the analysis of large data sets. This analysis will teach the AI system how to spot cyberattacks before they are able to affect your organization’s IT infrastructure.
2. Vulnerability Assessment
Vulnerability assessment is an essential part of cybersecurity because it helps companies discover gaps in the systems that hackers can potentially exploit.
Generative AI can improve this practice by generating data sets that you can use to test the effectiveness of your vulnerability assessment tools.
3. Simulation of Cyberattacks
One of the most coveted features of generative AI in cybersecurity is the ability to simulate cyberattacks to train security personnel and gauge the response of the systems put in place to protect a computer network.
These attack simulations are similar to real-world incidents, so if they successfully overrun your system, an actual attack will likely do the same.
4. Improved Threat Detection
Besides threat pattern recognition, generative AI helps company security systems detect malware and other malicious elements by training machine learning models to detect new types of malware.
You can facilitate this by directing generative AI tools to create novel malware samples and exposing your security systems to these samples so they can detect them in the future.
This is useful because cybercriminals constantly upgrade their skills and create new malware to bypass existing security measures.
5. Automation of Security Tasks
You can use generative AI to automate routine security tasks like threat hunting and log analysis. This gives cybersecurity personnel the freedom to perform more sophisticated tasks to protect the network infrastructure.
Limitations of Generative AI in Cybersecurity
Generative AI is significantly beneficial to business cybersecurity, but it has the following limitations:
Shortage of skilled professionals
Generative AI has been mainstream for a relatively short time, so there is still a lack of skilled workers who have the expertise and experience necessary to build reliable AI cybersecurity tools.
Privacy concerns
You have to train AI systems on large data sets, but there are concerns across the industry about the privacy and security of these data.
High software development cost
AI security tools are more expensive to develop than mainstream cybersecurity solutions. Small and medium-sized companies might be unable to afford this Technology, leaving their systems exposed.
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Endnote
As generative AI continues to improve, there will be more ways to apply it in cybersecurity and beyond. Currently, they are mainly used to detect anomalies, signs of intrusion, and malicious code in a computer network.
They gain this ability after analyzing huge data sets to learn the patterns synonymous with and indicative of different forms of cyberattacks.
