# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
## 5 Smart Grid Attacks
### GAN and LLM-Based Solutions
These lessons are presented in the next section in full detail. In smart grid settings, GANs and LLMs provide sophisticated capabilities for both attack simulation and defense augmentation. For instance, LLMs can automate threat intelligence by examining security records and generating alerts or action plans that are accessible by humans, while GANs can be used to generate adversarial scenarios for training anomaly detectors. An in-depth analysis of GAN and LLM-based solutions for smart grid security is presented in this paragraph (see Table 33). For GAN-based solutions, the lack of labeled attack data is one of the main obstacles to training security models for smart grids. GANs can provide synthetic but realistic cyberattack samples to supplement constrained datasets, allowing for more robust and generalizable IDSs training. GANs can model adversarial methods to mimic adaptive or covert attack behaviors. These adversarial scenarios help in exposing hidden weaknesses in security procedures and stress-testing current defensive mechanisms. Mode collapse and training instability are well known to affect GAN models. Customized architectures, such as TimeGAN or conditional GANs, are needed to apply them to smart grid data, which frequently consists of time-series telemetry and operational logs. To prevent overfitting or the introduction of noise, the synthetic data must also be assessed for realism and diversity. For LLM-based solutions, LLMs provide a strong basis for real-time danger analysis, policy creation, and intelligent monitoring in smart grid settings. They are used for automation and decision support in addition to general NLP activities. LLMs are able to analyze both structured and unstructured SCADA system records, spot unusual operator behavior patterns, and provide corrective measures. They are ideal for changing threat environments due to their zero-shot and few-shot learning capabilities. LLMs can help with real-time diagnostics and reaction generation during security events by being trained or refined on domain-specific corpora, such as utility laws, incident response manuals, and cybersecurity best practices. LLMs can help with real-time diagnostics and reaction generation during security events by being trained or refined on domain-specific databases, such as utility laws, incident response manuals, and cybersecurity best practices. Despite its potential, latency, interpretability, and data privacy are among the key implementation challenges that LLMs must overcome in real-time smart grid systems. For edge deployment, lighter or more condensed versions (such as LoRa or quantized models) might be more useful. To adjust to evolving grid designs and threat profiles, attentive prompt engineering and frequent retraining are also necessary. For combined use, a hybrid GAN-LLM strategy might be particularly effective. While GANs create artificial attack scenarios, LLMs assess and interpret them for downstream automation pipelines or human operators. Proactive defense, improved situational awareness, and semi-autonomous response mechanisms are all supported by this confluence. Utilities may advance towards more intelligent, flexible, and robust cybersecurity infrastructures by integrating these AI-driven models into the smart grid ecosystem. Stakeholder cooperation, domain-specific adjustments, and thorough validation are necessary to guide their integration.