## 2 State of the Art
### Security and Privacy  
Edge computing brings into the picture the notion of decentralized intelligence, as insight generation and knowledge output are increasingly moving to the edge, close to the point where data are generated and intelligence needs to be consumed. At the same time, the highly distributed nature of the IoT-edge-cloud continuum extends the attack surface and presents a number of challenges and threats related to the stack features.  
A modular and adaptable framework is essential for integrating multiple, loosely coupled tools across this spectrum. However, this integration poses several risks, includingvulnerabilities and compatibility issues arising from various security protocols, authentication mechanisms, and configurations among different tools and services. Inconsistencies in integration could accidentally facilitate security breaches or lapses. Furthermore, the ubiquity of generic APIs designed for interoperability might cause potential attack vectors if not adequately secured. An unguarded API, devoid of rate limiting, could be overwhelmed in a distributed denial of service (DDoS) attack, severely impairing system functionality.  
In addition, the process of transmitting and securely storing data across various nodes presents its own set of challenges, requiring rigorous encryption and secure storage protocols. The complexity of securely updating a multitude of devices, particularly within an IoT framework, emphasizes the vulnerability to security breaches. Reliance on third-party solutions introduces another layer of risk, as compromises within these external entities could have cascading effects throughout the entire framework.  
The orchestration of multi-cluster systems, especially within dynamic contexts, also introduces security vulnerabilities. For instance, workloads transitioned to new clusters may not be subject to the same security policies as in their original environments, posing a risk to system integrity. Furthermore, the need to rapidly scale services during demand spikes may lead to the bypassing of certain security protocols, compromising system security for the sake of maintaining service availability.  
Monitoring and analytical capabilities within distributed frameworks can also reveal vulnerabilities to AI and ML manipulations, whereby models could be tainted or misled, resulting in inaccurate predictions or analyses. On the other hand, the need for real-time monitoring can precipitate false positives or the neglect of genuine threats, as rapidly evolving data patterns may be misinterpreted as malicious activities rather than legitimate variations in user engagement. The decentralization of observability further complicates the security landscape, as isolated reports of minor anomalies from edge devices might be overlooked components of a broader, coordinated attack strategy.  
The issue of interoperability and data privacy also emerges as a significant concern within this continuum. Striking a balance between system interoperability and security maintenance can be challenging, as some interoperability frameworks might bypass security checks for ease of use. An IoT device employing an outdated communication protocol may be permitted to transmit data without modern encryption standards, risking data breaches. Moreover, the transmission of data across different jurisdictions can result in regulatory non-compliance, with IoT devices in one region potentially violating privacy regulations when transmitting user data to servers in another, as exemplified by the General Data Protection Regulation (GDPR) within the European Union (EU).