The rapid growth of cloud computing has transformed how data is stored, processed, and accessed [1], [2]. Cloud services provide various benefits, such as scalability, Flexibility, and cost-efficiency [3]. However, the increase in the adoption of cloud environments has also led to a rise in security threats. Traditional security measures do not always work well for cloud environments because of their dynamic nature [4]. The dynamic and distributed nature of cloud environments and the growing complexity of cyber threats require more advanced security solutions. This has led to exploring Artificial Intelligence (AI) and Machine Learning (ML) techniques to enhance cloud security [5]. AI and ML have the potential to provide real-time threat detection, automated response, and continuous adaptation to evolving security challenges. Internet of Things (IoTs) is the best bet in technology [6]. Approximately 127 new devices are connected to the internet every second, and it is predicted that the worldwide number of connected devices will increase by more than 27 billion by 2025. The higher the deployment of IoTs devices, the higher the trend of IoTs market size growth. The security of IoTs devices and the constant cyber threat on the cloud network infrastructure have been critical security issues [7]. The IoTs, which is a network of a variety of interconnected devices, provides intelligent-based services using the internet, the users' privacy, and different cyber-attacks while data is in use or at rest, which requires the highest level of protection [8]. AI/ML models can be approached to meet this requirement, including supervised learning, unsupervised learning, and reinforcement learning. The application of AI provides a more secure environment on the cloud and helps to ensure the possibility of realizing the full potential of the IoTs.

It aims to investigate the integration of AI and ML in cloud security and its impact on threat detection and prevention. Specific objectives are:

I. To examine the various ways AI and ML address cloud security challenges.

II. To evaluate the effectiveness of AI and ML techniques in detecting and preventing cloud-based threats. III. To explore potential applications of AI-powered cloud security.

AI and ML-based security solutions can analyze large volumes of data, identify complex patterns, and detect threats with greater accuracy compared to traditional methods, improving the overall efficiency and reliability of the security system.

It reduces the time required to detect, analyze, and mitigate security incidents.

AI and ML algorithms can process and analyze a large amount of security-related data, including logs, network traffic, and user activities, which enables cloud security solutions to scale up to the growing complexity of cloud environments.

AI-powered cloud security systems can continuously learn from new data and adapt their models to detect and respond to emerging threats.

It reduces the workload for security teams and improves efficiency.

Handling sensitive data across IoTs devices in the cloud makes privacy and security crucial [13]. AI systems depend on large amounts of data to improve accuracy, but gathering and storing this data is difficult. Personal and sensitive information may be vulnerable to breaches without strong privacy safeguards.

IoTs networks are vast and constantly expanding, which makes real-time processing and analysis challenging. AI algorithms need computing power and memory, which can strain cloud resources and limit scalability. IoTs devices generate massive amounts of data in various formats and from different environments. Poor-quality or inconsistent data can lead to inaccurate threat detection and ineffective security responses.

AI-based security must analyze data and respond quickly, especially during an attack.

AI models, intense learning ones, can be complex and difficult to interpret. This makes it challenging for security teams to fully understand or trust AI-driven decisions.

Cyber threats constantly evolve, and AI models must be updated frequently to stay effective.

Developing, implementing, and maintaining AI-based security in the IoTs cloud ecosystem requires significant resources and expertise, which can be costly for many organizations.

This conclusion shows the study's core insights, emphasizing AI and ML's critical role in enhancing cloud security while acknowledging the challenges that need to be addressed to maximize their effectiveness. The study emphasizes the transformative role of AI-powered cloud security in tackling the increasing complexity of cyber threats associated with cloud computing. Organizations can enhance their threat detection, prevention, and response capabilities by integrating AI and ML techniques. The integration of AI and ML significantly boosts cloud security in IoTs environments. These technologies offer advanced capabilities for identifying and responding to complex cyber threats, making them essential for modern security strategies. Systems excel in detecting anomalies by analyzing large volumes of data related to user behavior and network traffic. This ability enables timely identification of potential security threats, facilitating prompt interventions. The application of predictive analytics allows organizations to mitigate risks before they appear. Using historical data, AI models can identify patterns indicative of future threats, enhancing overall security. AI-driven security solutions can automate responses to detected threats, ensuring rapid and consistent mitigation actions. AI systems continuously learn from new data, allowing them to adapt to evolving security challenges. Despite their advantages, deploying AI and ML in cloud security encounters challenges such as data privacy concerns, the necessity for high-quality datasets, and the complexities of model interpretability. Future improvements in AI-powered cloud security can be achieved by integrating emerging technologies like blockchain and edge computing. Such collaborations can enhance security measures, improve responsiveness, and ensure data integrity.

An AI-powered anomaly detection system can enhance the security of a company's cloud infrastructure. The system uses deep learning algorithms to analyze user behavior, network traffic, and resource utilization patterns, identifying anomalies that could indicate security threats.

AI models, especially ML algorithms, are highly effective at detecting abnormal patterns, making them ideal for identifying unusual behaviors. Neural networks can recognize complex attack patterns with high accuracy. Many studies reveal that reinforcement learning algorithms enable systems to learn from experience and improve response actions over time, which is valuable in IoTs environments. This helps reduce the time it takes to respond to incidents and minimize damage.

Research shows that predictive models improve as they gather data over time. AI-based solutions in the cloud provide scalable, cost-effective security, especially for organizations with extensive IoTs networks.

Research also shows the challenges associated with AI in IoTs cloud security. AI systems rely on large and diverse datasets, raising concerns about data privacy, security, and quality. If data is inconsistent or low quality, it can lead to poor AI model performance, reducing reliability in detecting threats effectively.

AI-powered cloud security can be further enhanced by integrating with emerging technologies such as blockchain, edge computing, and the IoTs. Blockchain technology can provide additional security and data integrity for cloud environments.

By integrating AI and ML models with edge devices, cloud security solutions can rapidly identify and mitigate threats without the need for centralized processing, improving the security system's overall responsiveness and resilience.

The authors declare no conflict of interest.

All data are included in the text.

This research received no specific grant from funding agencies in the public, commercial, or not-for-profit sectors.