# Cloud Security Using Fine-Grained Efficient Information Flow Tracking
## 7 Conclusions  
In our comprehensive review of existing IFT tools, we examined several analogs to CloudMonitor that address data leakage in complex cloud systems. Traditional methods often burden CSPs with significant overhead and management activities that drive the exploration of alternatives like IFT. Our analysis, focused primarily on the consumer side, augmented consumer data subsets with security tags and deployed a network of monitors which facilitated the detection and prevention of data leaks among cloud tenants. However, CloudMonitor stands out as a comprehensive framework designed explicitly for preserving data confidentiality and integrity in cloud services. By integrating IFT into its core modules, such as integrity detection and emulated execution, CloudMonitor offers real-time monitoring and enforcement of privacy policies with minimal overhead. Notably, CloudMonitor's ability to reject activities violating privacy policies in real-time and trigger alarms for audit purposes underscores its utility in maintaining cloud security.  
IFT introduces a novel architecture for information security in corporate settings. It focuses on managing critical data flow to and from the cloud to prevent leaks and monitor changes. Unlike previous efforts, IFT emphasizes achieving complete binary-level compatibility with prevalent cloud services.  
Our approach starts by considering consumer organizations' perspectives and proposing a monitoring tool by using a thin model approach positioned between consumer organizations and the cloud. This tool serves as a foundational element for a comprehensive information security platform. IFT aligns with existing cloud storage services while enabling thorough monitoring of user data and its interactions with external entities.  
Implementing dynamic taint analysis and policy fulfillment within the cloud service enhances security by monitoring data flow and enforcing end-to-end Data Loss Prevention (DLP) constraints. However, significant runtime overhead due to dynamic taint analysis hampers mainstream adoption. Our study introduces algorithmic solutions to mitigate performance costs, including native machine instruction-level monitoring and asynchronous taint tracking.  
Combining these optimizations, IFT achieves a notable performance improvement compared to previous approaches, into a framework we call CloudMonitor. Despite some overhead, particularly in CPU-bound scenarios, user feedback indicates manageable impacts on productivity. Notably, IFT stands out as the only system with a functional interactive interface and effective whole-system byte-level taint tracking.  
Semantic gaps between cloud storage services and consumer infrastructure pose challenges in distinguishing intentional and unintentional data transmissions. Practical examinations reveal false tainting instances and highlight the need for further research into taint-spreading dynamics. Standard IFT techniques may not fully align with legacy application binaries, necessitating application-level restructuring.  
To address these challenges, we propose methods for identifying and mitigating taint label leakage channels. While our initial results show promise, additional research is required to extend these methodologies to other cloud services.  
Despite these advancements, it is essential to acknowledge the challenges encountered along the way. The phenomenon of taint explosion poses a significant hurdle that necessitates ongoing research efforts to better understand its dynamics and develop effective mitigation strategies. Furthermore, the semantic gap between cloud storage services and consumer infrastructure presents a practical constraint that requires innovative solutions necessary to overcome said constraints.