## 3 Results
### Cyberattacks in Smart Grids: Challenges and Implications of Cybersecurity
#### 3.2.2 Pairwise Comparison Matrix, Relative Weights of Criteria, and Weighted Sum of Alternatives
Overall, AI enables smarter threat management, reduces response times, and enhances the resilience and security of the smart-grid infrastructure [209]. While artificial intelligence (AI) offers significant benefits in enhancing the cybersecurity of smart grids, there are also several challenges that need to be addressed [210]. Here are some potential challenges:  
* **Data Quality and Availability**: AI algorithms heavily rely on high-quality and representative data for training and decision-making. In the context of smart grids, obtaining labeled and diverse cybersecurity data can be challenging. Collaborating with smart-grid operators, cybersecurity organizations, and researchers to collect and share cybersecurity data to ensure the availability, accuracy, completeness, and reliability of the data is crucial for the effective training of AI models. Establishing mechanisms for continuous acquisition and the labeling of training data to keep AI models updated with the latest threats and attack patterns is also important. * **Adversarial Attacks**: AI models can be susceptible to adversarial attacks where malicious actors intentionally manipulate or deceive the models to bypass security defenses. Adversarial attacks in the context of the smart grid can have severe consequences, such as disrupting power distribution or causing physical damage. Developing robust defenses against adversarial attacks is an ongoing challenge. * **Model Interpretability and Explainability**: AI models often operate as black boxes, making it difficult to understand their decision-making processes. In the context of smart grids, where the consequences of incorrect decisions can be significant,the interpretability and explainability of AI models are crucial to gain trust and enable effective human oversight. Prioritizing the development of interpretable and explainable AI models for cybersecurity in smart grids enables human analysts to understand the reasoning behind model decisions and ensures transparency and accountability in the decision-making process. * **Privacy Concerns**: smart-grid systems handle sensitive and personal data, including energy consumption patterns and user behavior. The application of AI may involve collecting and processing such data, raising privacy concerns. Ensuring proper data anonymization, encryption, and compliance with privacy regulations is essential to protect user privacy. * **Scalability and Performance**: Smart grids generate massive amounts of data in real-time, requiring AI models to process and analyze the data efficiently. Ensuring the scalability and performance of AI algorithms to handle the volume and velocity of the data in smart-grid environments is a significant challenge. * **Ethical and Bias Concerns**: AI models can inadvertently inherit biases present in the training data, leading to discriminatory or unfair outcomes. Biases in smart-grid systems can have societal implications, such as uneven energy distribution or unfair treatment of certain user groups. Addressing bias and ensuring ethical considerations in AI models are essential. * **Human Expertise and Collaboration**: AI technologies augment human capabilities but do not replace the need for human expertise in cybersecurity. Collaboration between domain experts, cybersecurity professionals, and data scientists is crucial to effectively apply AI techniques to smart-grid cybersecurity. Developing hybrid systems that combine the strengths of AI algorithms with human expertise for better decision-making, threat hunting, and incident response is essential. * **Regulatory Compliance**: Smart grids are subject to regulatory requirements and standards to ensure the security and privacy of energy systems. Integrating AI solutions in compliance with regulatory frameworks, such as data protection and cybersecurity regulations, presents a challenge that requires careful implementation and validation.