# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
## 5 Smart Grid Attacks
### GAN and LLM-Based Solutions
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Aspect** & **Challenge Description** & **Impact in OT/Smart Grid** & **Mitigation Strategy** \\ \hline \multirow{2}{*}{Legacy Device Integration} & Many legacy systems lack support for modern cryptographic techniques or remote update capabilities & Increased vulnerability due to inability to patch or apply modern security standards & Use of protocol wrappers, secure gateways, and segmentation of legacy devices from critical systems \\ \hline \multirow{2}{*}{Human Element} & Human error, lack of training, and social engineering attacks remain a common attack vector & Misconfigurations, phishing, or physical access compromises leading to potential system failures & Implement ongoing cybersecurity training, role-based access control, and insider threat monitoring programs \\ \hline \multirow{2}{*}{Penetration Testing Risks} & Traditional penetration testing can cause system crashes in fragile OT systems & Unexpected service disruptions, equipment failures, and operational/financial losses & Perform security assessments in testbeds, use digital twins or simulations instead of live system testing \\ \hline \multirow{2}{*}{System Resilience} & OT systems are not built for resilience against aggressive scans or tests & Even minor testing can lead to outages or degraded performance & Adopt non-intrusive monitoring tools, anomaly detection, and passive scanning \\ \hline \multirow{2}{*}{Operational Sensitivity} & Downtime in OT systems, even brief, can lead to cascading failures or significant financial loss & Grid instability, energy distribution, or failure to meet SLA requirements & Plan maintenance windows, leverage redundancy, and test recovery processes during simulations \\ \hline \hline \end{tabular}
\end{table} 
Table 30: Security challenges and mitigation strategies in OT-based smart grid environments. \begin{table}
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The use of LLMs offers significant potential benefits for enhancing cybersecurity in smart grid security and is proposed to enhance automated threat detection, incident response/decision-making, vulnerability management, and operational efficiency. However, they also present challenges related to data sensitivity, resource requirements, interpretability, and ethical considerations (see Table 32). Balancing these advantages and limitations is crucial for effectively leveraging LLMs in cybersecurity. Hence, several potential solutions are presented. In [299], [PERSON] et al. presented a new framework using AI and advanced data analytics for automated solution development in smart grids, aiming to streamline the software development process, capitalize on recent advancements in AI technologies, and generate revenue through various business models. In [300], [PERSON] et al. introduced a novel cybersecurity framework using LLMs to detect anomalies in IEC 61850-based digital substation communications, incorporating data pre-processing and Human-In-The-Loop (HITL) training, with comparative analysis conducted across various LLMs using a Hardware-In-The-Loop (HIL) testbed to evaluate performance metrics. In [301], [PERSON] et al. introduced LLMs to create automation routines in response to loosely constrained user commands, proposing Sasha, a smarter smart home assistant, to address these challenges. In [302], [PERSON] et al. highlighted the necessity to explore both capabilities and limitations of LLMs in enhancing the electric energy sector's operations, identifying key research directions such as fine-tuning data collection systems, embedding power system-specific tools to improve LLM responses, and safety-critical use cases. In [303], [PERSON] et al. examined the potential security threats associated with applying LLM in modern power systems for proactive research and development of countermeasures to mitigate these threats and safeguard operational integrity. Based on the presented solutions, many lessons can be deduced and learned.