GPT-3 has been around for over a year now but didn’t start gaining popularity until ChatGPT coupled it with Reinforcement Learning from Human Feedback (RLHF). RLHF is a machine-learning technique that uses human feedback to train and improve the accuracy of a model. It’s this combination of AI and RLHF that makes ChatGPT so powerful and accurate.
This same powerful combination can be—and is—used by modern email security solutions to combat phishing.
In the context of email security, RLHF can be used to train an AI model to detect and respond to potential threats more effectively. By using human insights, the model can learn from real-world examples of email threats and improve its ability to identify and respond to similar threats in the future.
One of the key advantages of using RLHF in email security is its ability to improve the accuracy of threat detection. Traditional email security solutions, like Secure Email Gateways (SEGs), rely on pre-defined rules and patterns to identify potential threats. However, these rules and patterns can become outdated quickly, leading to a high rate of false positives and false negatives. By using RLHF, the model can learn from human insights and adapt to new threats as they emerge.
Another advantage of RLHF is its ability to reduce the time it takes to detect and respond to potential threats. Traditional email security solutions often rely on manual review processes to identify and respond to potential threats. This can be time-consuming and labor-intensive, leading to delays in detecting and responding to threats. By using RLHF, the model can quickly and accurately identify potential threats, allowing for faster response times.
In addition to the advantages of improved accuracy and reduced time to detect and respond, the use of RLHF in email security also provides other important benefits.
Leveraging and empowering end users to report suspicious emails can increase visibility on what is being missed by AI and threat intelligence systems. When users are trained and encouraged to report suspicious emails, they can provide valuable information on new and emerging threats that may not be detected by traditional security systems. This can help security teams stay ahead of the latest threats and improve their overall defense posture.
By reporting suspicious emails, end users can help validate the model in real-time. Emails reported by users can be immediately reviewed and validated by the company's security team. This allows for quick and efficient reinforcement of the model, helping it to learn and adapt to new threats more quickly.
When done by a distributed team across different companies and different time zones, RLHF can help reduce the time to detect and respond to threats dramatically. By having a team of security experts working around the clock, organizations can quickly identify and respond to threats as they emerge, regardless of their location or time of occurrence. This can be especially beneficial for organizations with global operations, as it allows them to stay ahead of cyber threats 24/7.
In conclusion, the use of RLHF in email security solutions is a powerful tool that can help organizations improve the accuracy and speed of threat detection and response. By leveraging and empowering end users, validating the model in real-time, and working with a distributed team across different time zones, organizations can beat the clock and reduce the time to detect and respond to threats dramatically. Additionally, it gives more visibility on what is being missed by AI and threat intel, and it allows to empower end users to report suspicious emails and validate them in real-time.
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