By: Rishitha Chokkappagari, Department of Computer Science &Engineering, Madanapalle Institute of Technology & Science, Angallu (517325), Andhra Pradesh. chokkappagaririshitha@gmail.com
Abstract
The practices of cloud storage involving artificial intelligence (AI) and machine learning (ML) are steadily and dynamically changing how information is stored and protected. As the storage needs are increasing day by day due to the enhancement of data volumes in an unprecedented manner, traditional ways of managing storage do not provide the viable solution. Machine learning and artificial intelligence offer new solutions to these problems, which can be characterized as intelligent, automated and increase the speed and reliability of cloud storage. In this article, the use cases of AI & ML in cloud storage are discussed based on some of the most important areas including storage capacity forecasting, automated sorting, classifying, and data search & retrieval, security assessment using anomaly detection, and smart tiering that considers cost and performance at the same time. About the use of AI and ML algorithms, predictive analytics can produce the forecast on storage requirements for the future based on the experience of the data usage in the past. It includes this predictive characteristic which enables organizations to plan their capacity requirements for storage in a much better way hence avoiding instances whereby an organization has invested a lot in infrastructure, but they end up having not enough storage capacity. Implementation of AI and ML in improving the organization of files and the required data accelerates the system and improves the user experience. Natural language online search using AI-based search engine is a great feature of use which can quickly and efficiently deliver the needed information. This article examines the real-world applications, case studies and benefits of cloud storage using AI and ML.
Keywords: Cloud storage, Artificial Intelligence, Machine Learning
Introduction
The roles of AI and ML are unvaluable serious in cybersecurity since it helps to distinguish risks and fix them. Regular screening of the frequencies of data access and other activities helps machine learning algorithms to identify trends associated with insecurity threats. These models are learning-based and become more accurate with time, thereby providing a very strong protection against cyber-attacks. Moreover, AI and ML automate the response to threats that the tools have detected, which increases the efficiency of the measures being taken. AI and ML based intelligent tiering helps in managing storage costs as well as performance where prioritized data is automatically migrated from one tier to another based on usage and importance. This way, the most critical information is easily retrievable and maintained while less used data increases costs in expensive solutions.
Prospects for the use of AI and ML in cloud storage are high due to constant work with increasing the effectiveness, speed, and protection of space. Such trends as AI at the edge, quantum computing in data management tasks is some of the trends that are anticipated to shape the future cloud storage. That is why as the technologies advance and develop they will be able to present even more complex and versatile solutions to the increased requirements of the information society.
Another advantage is the possibility of using the AI and ML in cloud storages to carry out predictive analysis for storage requirements. Old school capacity management methodologies were a mere guesswork, that often ended up with taking either wrong decision of either over-provisioning leading to unwanted overhead expenses or under-provisioning that in-turn led to possible service interruption. AI and ML solutions scan data using history to determine how that storage will be used in the future and assist organisations formulate an ideal capacity strategy to ensure it utilises its infrastructure efficiently. Furthermore, breakdowns in the usage of cyclic processes in analysis such as AI and Machine Learning further enable the organization and identification of data in an automated manner hence improving the user experience and efficiency. The employment of manual efforts and classification of large chunks of data is an activity that takes a lot of time and subject to errors. AI-enabled technologies can help in sorting data through content, usage rate and importance, thus helping the client get to the correct data easily and promptly. The modern search algorithms based on AI can naturally parse the submitted query, which makes the process of data retrieval even easier.
Another such essential field where AI and ML intervene is security. Cloud storage systems are very vulnerable to cyber threats; most of the conventional security approaches cannot handle today’s threats. It also makes cybersecurity stronger through usage of AI and ML as to control data access patterns and network traffic constant check is performed to detect any unusual activity. These technologies can be trained from past and improved detecting performances through time, and they can present a solid defence mechanism against continuously emerging threats. The fig.1 below the shows the role of AI and ML in cloud security[1].
Benefits of AI and ML in cloud Storage
Predictive Analytics
There are two main important areas that have been impacted by AI and ML in the field of cloud storage construction. Conventionally, decisions on the storage capacity needed are more of a best guess hence leading to either over provisioning or under provisioning. Over-provisioning leads to inefficient resource allocation and added expenses, while over-allocating and/or under-allocating resources lead to various operational problems.
Statistics are utilized by AI and ML to predict future use of data to ensure proper usage of storage at a given time. It provides the ability to ‘predict’ future storage needs, thus saving an organisation from having to over-provision its infrastructure investments. It is thus judicious for businesses to formulate means and ways of ensuring that storage requirements are predicted effectively hence avoiding any interruption that may occasion high costs of storage. The table1 shows the benefits of AI and Ml in cloud storage.
Computerization of data storing and data search
It is practically impossible to manage large amounts of data through the traditional technique of data dealing manually. AI and ML also ease work, especially when it comes to the classification of files through the content, frequency of use and their importance. This intelligent organization enhances the efficiency of the usage and access of information by users since the time taken to search for information is reduced.
AI-based search functionality can analyse the plain text and, therefore, can better respond to more natural questions. These advanced search functionalities go a long way in enhancing the experience of the users through fast and accurate provision of required information leading to higher productivity.
Anomaly Detection for Cybersecurity
As we know cloud storage systems are always designed to be hacked and thus the issue of security in the system is very important. Most of the older security strategies prove to be inefficient in coping up with the newer forms of threats present in the digital world. The use of AI and Machine Learning is instrumental in the recognizing and protection against security threats.
This is because machine learning models can be trained to scan through the data usage and networks traffic constantly and identify security threats inherent in regular use of data. Such models use data of previous events to increase their rates and become a reliable protection against cyber threats. Also, AI and ML can subsequently respond to a threat once it has been identified making the countermeasures efficient and quicker. This proactive approach allows the safeguard of confidential information within cloud environments and the preservation of the implementations’ trustworthiness[2].
Table 1 Key benefits of AI and ML in cloud storage
Role | Description |
Predictive Analytics | Forecasts future storage needs based on historical data usage patterns, optimizing infrastructure investments. |
Automated Data Organization | Categorizes files intelligently, improving data retrieval times and enhancing user experience. |
Enhanced Security | Detects anomalies in data access patterns and network activity, providing robust defence against cyber threats. |
Intelligent Tiering | Automatically moves data between storage tiers based on usage, balancing cost and performance. |
Efficient Search Functionality | AI-based search engines process natural language queries, providing quick and accurate access to information. |
Suggested Intelligent Tiering as a mechanism to Check on Costs and Performance
Cloud storage providers enhance different kinds of storage, and every type has general capability in addition to diverse cost. Consequently, there is intelligent tiering which uses AI and ML techniques to move data from one tier to another based on its activity and importance. The data that is most often used can be placed in classes providing high read/write performance, while less often accessed data can be transferred to less expensive classes with lower performance[3].
This intelligent tiering method helps keep important data easily retrievable while keeping the costs tied to the lower frequent data stored in the expensive storage solutions in check. When understanding which activities should take place at the strategic and operational level, it is possible to manage the required storage effectively and efficiently meeting the organization’s needs.
Real Life Implementation and Examples
The applicability of AI & ML in cloud storage is well seen in the vast reference real world implementations. Many kinds of businesses and companies apply artificial intelligence storage systems in their activity to make it more effective[4]. For example, the banking and other financial sectors use predictive analytics to address big data, namely transactions; they set their priorities towards an easy and fast access to required data, reducing the expenses on storage at the same time.
In the healthcare sector, they keep documents to enable specialists to search for patients’ records much faster and accurately provide the necessary diagnosis and therapy. The usage of big data in retail involves the employment of artificial intelligence algorithms that help in forecasting demand and supply trends as well as information that relates to stock and the resultant expenditure on stocks. In these instances, the concept of AI and ML focuses on solving specific issues in data storage while offering clear value for organizations that implement such technologies.
Future Developments
It can be alternatively stated that the future of utilizing AI- and ML-based technologies in cloud storage seems rather promising as the researchers are further working to advance the efficiency, performance, and security of the technologies in this context[5]. New trends are the coupling of AI with the edge, to process inputs faster at the point of origin. This helps minimize the time it takes to make decisions throughout various organizations enhancing the real-time decision-making flexibility.
More subjects of interest involve the application of quantum computing to AI-driven cloud storage solutions. Advanced data management can be handled with higher efficiency and speed with quantum computing and large scaled data storage and processing can be done faster with quantum computing. These progressed technologies are going to advance further in the future and are also going to bring profound change in the concept of cloud storage to address the increasing complexity of the new age digital storage requirements.
Conclusion
It is safe to predict that future developments of AI & ML for cloud computing are on the rise with optimism about its rolls and smoother progress in providing enhanced results, better performances, and measures of security. New tendencies like integration of AI with edge computing and utilization of the quantum computing in the data management tasks are waiting for development. These developments will allow for higher speed data computation, interaction with new sorts of data and higher-level data organization, as well as efficient methods of storing data that will enable cloud storage systems to remain a viable solution in the face of the constantly changing needs of modern society.
Therefore, it can be concluded that AI learning and Machine learning are essential tools in the development of cloud storage. A general advantage is that they open ways of bettering predictive analytics, handling data automatically, fortifying security and costs to performance ratio which is helpful for businesses and personal users. Since the utilization of cloud storage is progressing, the importance of incorporating AI and ML in cloud storage is likely to rise and consequently result in the advancement of smarter, organized, and more secure data storage methods. The future of cloud storage is even more of the expansion and incorporation of AI & ML so that the storage space of today can accommodate the triumphant increase in data demands of the world.
References
- S. Gupta, H. K. Sharma, and M. Kapoor, “Artificial Intelligence -Based Cloud Storage for Accessing and Predication,” in Blockchain for Secure Healthcare Using Internet of Medical Things (IoMT), S. Gupta, H. K. Sharma, and M. Kapoor, Eds., Cham: Springer International Publishing, 2023, pp. 157–168. doi: 10.1007/978-3-031-18896-1_13.
- C.-W. Ten, J. Hong, and C.-C. Liu, “Anomaly Detection for Cybersecurity of the Substations,” IEEE Trans. Smart Grid, vol. 2, no. 4, pp. 865–873, Dec. 2011, doi: 10.1109/TSG.2011.2159406.
- Tabassum F, Rahaman M (2024) An Enhanced Multi-Factor Authentication and Key Agreement Protocol in Industrial Internet of Things, Available: https://insights2techinfo.com/an-enhanced-multi-factor-authentication-and-key-agreement-protocol-in-industrial-internet-of-things/
- N. Q. Do, A. Selamat, O. Krejcar, E. Herrera-Viedma, and H. Fujita, “Deep Learning for Phishing Detection: Taxonomy, Current Challenges and Future Directions,” IEEE Access, vol. 10, pp. 36429–36463, 2022, doi: 10.1109/ACCESS.2022.3151903.
- Rahaman M (2024) Foundations of Phishing Detection Using Deep Learning: A Review of Current Techniques Available: https://insights2techinfo.com/foundations-of-phishing-detection-using-deep-learning-a-review-of-current-techniques/
- Mishra, P., Jain, T., Aggarwal, P., Paul, G., Gupta, B. B., Attar, R. W., & Gaurav, A. (2024). CloudIntellMal: An advanced cloud based intelligent malware detection framework to analyze android applications. Computers and Electrical Engineering, 119, 109483.
- Bai, S., Shi, S., Han, C., Yang, M., Gupta, B. B., & Arya, V. (2024). Prioritizing user requirements for digital products using explainable artificial intelligence: A data-driven analysis on video conferencing apps. Future Generation Computer Systems, 158, 167-182.
Cite As
Chokkappagari R. (2024) The Role of AI and Machine Learning in Cloud Storage, Insights2Techinfo, pp.1