# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
## 9 Lessons Learned, Suggestions, and Recommendations
### Lessons Learned  
Here are some security lessons that have been learned from existing security breaches against smart grid systems and must be respected and adopted as a way to ensure safer and more secure smart grid systems for IoT domains:  
* **Risk Management:** This is essential for smart grid systems to reduce both the occurrence and likelihood of that risk occurring. Hence, several key points are presented to mitigate it. **Active Incident Response:** This necessitates continuous observation of vital smart grid systems to identify possible security breaches and start an operational incident response plan to react immediately to a security issue or incident. **Validation Testing:** This is to identify and address the vulnerabilities surrounding the smart grid system components before exploitation. This can be done by relying on vulnerability scanning and penetration testing. **Enhanced Risk Management Plans:** These are based on constant risk assessments being regularly conducted to develop enhanced risk management plans to mitigate identified risks using cryptography and non-cryptography-based measures, as well as technical (i.e., monitoring and access control) and non-technical controls (i.e., policies, standards, and incident response). * **Cyber-Physical Threat Intelligence:** CTI requires constant gathering, analysis, and sharing of information related to cyber threats with a potential impact to intentionally damage the infrastructure of the smart grid and interrupt its operations. This is frequently achieved by monitoring and evaluating threat intelligence data to identify and recognize new threats and weaknesses and then addressing them with proactive security measures. This move cannot be achieved without understanding the interdependencies between the smart grid's cyber and physical aspects. **Security \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Feature** & **Proposed MLTDAF Framework** & **Traditional SIEM** \\ \hline \multirow{2}{*}{Scope} & Unified IT–OT–DER coverage, suitable for smart grid environments & Primarily IT-focused, limited OT visibility \\ \hline \multirow{2}{*}{Detection Approach} & ML-based dynamic anomaly detection with adaptive learning & \multirow{2}{*}{Signature-based or rule-driven, less adaptive} \\ \cline{2-2} \cline{3-3}  & Yes, by including automated alert classification and tiered prioritization & & \\ \hline \multirow{2}{*}{Mapping to SGAM} & Designed to align across all layers: Component, Communication, Information, Function, and Business & Usually focused on Information and Function layers only \\ \hline \multirow{2}{*}{Scalability} & Architected to scale across distributed grid nodes and edge devices & Centralized processing may bottleneck in large-scale grid setups \\ \hline \multirow{2}{*}{Domain Coverage} & High: Includes DERs, legacy systems, IoT edge, smart meters & Low to moderate; lacks embedded support for DERs or grid-specific assets \\ \hline \multirow{2}{*}{Privacy-Awareness} & Supports pseudonymization and local anomaly processing before cloud transfer & Rarey considers privacy, centralized logging risks data exposure \\ \hline \multirow{2}{*}{Latency Tolerance} & Low latency design with support for edge and fog computing nodes & Higher latency due to batch processing and central correlation \\ \hline \multirow{2}{*}{Human-In-The-Loop} & SOC operators, OT engineers, and AI co-pilots in loop with adjustable oversight & Analysts play a reactive role; no domain-specific operational feedback loop \\ \hline \multirow{2}{*}{Resilience to Legacy Devices} & Incorporates wrappers and proxies for devices lacking native security support & Legacy device integration rarely addressed \\ \hline \hline \end{tabular}
\end{table} 
Table 36: Comparative analysis: MLTDAF framework vs. traditional SIEM.