The capability of generative AI to detect abnormalities is one of its most significant advantages for cybersecurity. Finding events or patterns that differ from the norm is the process of anomaly detection. As many assaults involve atypical behaviour, this might be a useful tool for identifying potential security issues. For instance, a baseline of typical network traffic patterns could be created using a generative AI model. Then, any variations from this standard might be marked as potential dangers.
Realistic phishing emails can also be created using generative AI. One of the most popular methods for attackers to enter a victim’s system is through phishing emails. Generative AI can assist security teams in training their staff to recognize and avoid these assaults by producing realistic phishing emails. Additionally, honeypots, which are fictitious websites or systems intended to draw attackers, can be made using generative AI. Security teams can follow an attacker’s activities once they’ve been drawn into a honeypot to learn more about their tactics.
Generative AI can be utilized for a range of different cybersecurity activities besides anomaly detection and phishing, including:
- Creating fake data for machine learning models to be trained
- For the purpose of evaluating security products, creating realistic malware samples
- Automating security operations like patch management and vulnerability screening
- Creating new security algorithms and protocols
Generative AI is likely to become an increasingly more useful tool for cybersecurity experts as it develops. Organizations may strengthen their security posture and defend themselves from a variety of threats by effectively utilizing generative AI.
Here are a few instances of how generative AI is currently being applied in cybersecurity:
- Cisco is using generative AI to detect and respond to DDoS attacks. A DDoS assault is one that floods a target system with traffic, rendering it inaccessible to authorized users. Cisco is modeling typical network traffic using generative AI. Any departures from this paradigm are marked as possible DDoS assaults. As a result, Cisco can stop these attacks in their tracks and swiftly detect and react to them.
- IBM is using generative AI to create realistic phishing emails. In order to produce convincing phishing emails that are indistinguishable from genuine ones, IBM is utilizing generative AI. This is used to establish honeypots to entice attackers and train staff to recognize phishing emails.
- Google is using generative AI to develop new security protocols. In order to create new security protocols that are more resistant to assault, Google is using generative AI. Google, for instance, is utilizing generative AI to develop new encryption algorithms that are more challenging to crack.
These are only a few instances of how generative AI is now applied to cybersecurity. Generative AI is expected to advance and become an even more useful tool for defending businesses against intrusions.
Naturally, there are inherent cybersecurity vulnerabilities associated with generative AI. For instance, phony news stories or social media posts intended to propagate rumors or sow strife might be produced using generative AI. Additionally, malware that is more challenging to find and eliminate could be made using generative AI.