# Developing a Cybersecurity Training Environment through the Integration of OpenAI and AWS
## 3 Results
### AI Efficiency  
For the evaluation of the efficiency of AI tools, in particular, GPT-3 and DALL-E, in the context of test data generation and their contribution to the improvement of the cybersecurity training environment, extensive measurements and tests were carried out to evaluate the efficiency of these tools in terms of speed, accuracy, and resources used.  
One of the key metrics to evaluate AI efficiency is the speed of data generation. The time it takes for GPT-3 and DALL-E to generate test data was measured compared to traditional methods. The results showed that AI data generation is significantly faster, accelerating the process of obtaining test data for analysis.  
Accuracy in data generation is essential to ensure the quality of data sets used in cybersecurity. Accuracy tests were performed by comparing the AI-generated data with traditional data sets. The metrics used included the following:  
* Accuracy: Proportion of data generated that is relevant and accurate of the total data generated.
* Recall: Proportion of relevant and accurate data generated of the total relevant and accurate data in the cybersecurity domain.  
In addition to speed and accuracy, the resource savings provided by AI compared to traditional approaches were evaluated. This includes computational resources, time, and human effort. Detailed comparisons were made to determine how many resources were saved using AI in cybersecurity data generation and analysis. The ability to scale the use of AI for test data generation and safety analysis was also evaluated. We measured how GPT-3 and DALL-E can handle large volumes of data and whether efficiency is maintained as the scale of operation increases.  
Figure 5 compares data generation times between GPT-3, DALL-E, and traditional methods. GPT-3 is the fastest method, with an average data generation time of 0.5 s. DALL  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Threat Type** & **AI Data Set (\%)** & **Traditional Data Set (\%)** \\ \hline DDoS attack & 95.2 & 94.5 \\ Identity fraud & 97.1 & 96.2 \\ Malware & 95.8 & 94.7 \\ SQL injection & 97.0 & 96.5 \\ XSS attacks & 96.5 & 95.9 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison of AI-generated and traditional data sets.  
E is slightly slower, with an average build time of 0.7 s. Traditional methods are significantly slower, with an average data generation time of 5.0 s. This variable highlights the efficiency of AI in generating data compared to conventional approaches.  
The figure shows two data generation accuracy metrics: precision and recall. The accuracy is 94%, which means that the AI generates accurate data in a high percentage of cases. The recall is 93%, indicating that AI is also effective in retrieving relevant data. These metrics demonstrate that AI achieves high accuracy and has the ability to identify cybersecurity data.  
Furthermore, the figure presents three categories of resource savings when using AI compared to traditional approaches. CPU resource savings are 80%, meaning that AI uses significantly fewer processing resources. The time savings are 75%, indicating that AI accelerates data generation. The human effort savings is 90%, highlighting automation and manual workload reduction. These data demonstrate that AI saves computational resources, time, and human effort.  
The last graph within the figure shows how AI maintains its efficiency as the operation is scaled into three levels: small, medium, and large. The AI supports low data generation times at all scale levels, with 0.7, 0.8, and 0.9 s, respectively. This indicates that AI is scalable and can efficiently handle workloads of different sizes.