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@@ -37,6 +37,8 @@ We implemented two caches
     pull all the data from the ai recommendation every time it wants to notify 
     the users about new movies to recommend
 \end{enumerate}
+\paragraph{Database}
+We use postgresql database to contain data about users, movies and user ratings
 
 
 \begin{figure}[H]
@@ -47,8 +49,31 @@ We implemented two caches
 
 
 \section{Automated Infrastructure Management solution}
+We use \textbf{Dockerfiles}
+for each microservice, webinterface and database which 
+later get combined in docker compose file, after each commit on 
+\textbf{GitHub} main repository docker compose gets automatically run 
+on \textbf{Google Cloud} platform and deployed 
 \section{Federated authorization and authentication management in the project}
+We use industry standard \textbf{OAuth} protocol in our webinterface, 
+user creates their account and logs in, we use user token to 
+authorize their access on backend to their ratings and 
+recommendations. We use \textbf{firebase} services to manage OAuth protocol.
 \section{Threat model with mitigations}
+Our single most important asset are user likes for specific movies \\ 
+We expect either bots or human agents trying to access those likes for a specific users or to modify user ratings to improve or decrease certain movies ratings \\ 
+To mitigate that we use: 
+\begin{enumerate}
+    \item Certificates on our frontend, which encrypt data transmitted between website and user 
+    \item OAuth which is used to authenticate user and lower amount of bots accessing our Infrastructure
+    \item TLS encryption between our microservices so that even our inside communication is encrypted 
+    \item Google cloud default security policies allowing us to monitor odd and potentially harmfull behaviours
+\end{enumerate}
+\begin{figure}[H]
+    \caption{Threat model}
+    \centering
+\includegraphics[width=\textwidth]{images/threat_model.png}
+\end{figure}
 
 
 \end{document}
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