AI’s Role in Protecting Our Data

By: Dhanush Reddy Chinthaparthy reddy, Department of Computer Science and Artificial Intelligence , student of Computer Science and technology, Madanapalle Institute of Technology and Science, Angallu, 517325, Andhra Pradesh

Abstract

Thus, in the present context of the digital age when amounts of data created, stored, and shared are huge, the opportunities and threats should not be dismissed. Among all these challenges, one challenge that has been identified and is deemed to be a threat is the challenge of data protection. AI is considered today as one of the significant components in security innovations since it has new and different ways of perceiving threats concerning security of data. The following paper reveals how AI is employed as an instrument of preventing data threats, searching threatening patterns and anomalies, as well as data encryption and prediction in two cases. In process of studying in this course, examples of new methods of AI technologies, practiced in real-life situations to change the approaches to protecting the systems and the confidential information against real threats will be presented.

Keywords: Artificial Intelligence, Anomaly Detection, Data Protection, Security.

1.Introduction:

Autism has been dynamic at an exceptional and unprecedented fashion with automation being on the increase and the artificial intelligence being adopted all over the different disciplines. Advancements in the computational power, use of IoT in all ranges of industries from manufacturing to real estates and management and the digitalization of business through huge, collected data supports the use of AI based solutions. Yet when more of it is to be integrated it is assuring that questions about safety of the society and humanity’s welfare are being posed.

AI solutions can be found in almost every industry; therefore, beginning with a brief analysis of AI’s prevalence, opportunities, and development in cybersecurity is essential. Concerning goals and purposes, it is also necessary to mention that they may significantly differ in different companies and even depend on the ‘’interpretation’’ of the given terms according to the existing norms and standards on the international level. At the present time, security in cyberspace is a matter of major concern, and even more so because with the help of various electronics devices people interact and rely on digital systems in the year 2023 and beyond. Cyber security on the other hand can be described as protection of cyber-crimes with any ware, software and data that massages the internet.

In the case of novel AI solutions, the systemic control of virtual activity and specifically the criminal aspect can be helpful; however, it also causes both beneficial and adverse effects. There are subfields of AI that belong to reinforcement of cybersecurity and others relate to the penetration phase. The literature review illustrates that research on AI applications is quite heterogeneous. All the single polls indicate that AI should be integrated into the performance of analysing large amounts of data or looking for threat manifestations. The ongoing study has extended the application of the neural network models with an aim of enhancing cyber technical protection of the military facilities. In the same manner, boasting of ten years of improvement does not mean the entire possibilities and adversities of autonomous AI are not created and figured out.

2. AI in Threat Detection

On the same note, as a result of the improvement of the AI techniques the learning based approaches towards the recognition of the cyber-attacks is observed to show a rather huge improvement and this has made a lot of achievement. However, making the IT systems withstand new threats that appear all the time, is still an issue to address. Various invasions and unlawful practices in the network have placed the search for efficient protection methods as the most crucial challenge.

Traditionally, two primary systems detect cyber threats and network intrusions: To this we have Intrusion Prevention Systems (IPS) and Security Information Event and Management (SIEM). IPS addresses the protocols of the networks and traffic and categorizing the intrusion signatures that produce security events that are forwarded to the SIEM systems. out of these, SIEM enables the management and collection of these alerts so that analysts can assess the multiple related incidents that give way to the breach of activities.

However, most of these efforts are still associated with high false alarm rates, and the availability of huge amounts of security information hinders the identification and detection of intelligent network attacks. Such challenges have been researched in the recent past and mostly with the help of machine learning and Artificial Intelligence techniques. The analysis of the network intrusions with the aid of AI is efficient and automated because the AI algorithms are fed with previous threat data and the models are used to look for new threats.

Other techniques that relate to learning can be seen to possess a lot of potential for the analyst particularly in situations where they are expected to dissect large cases of events in each duration of time. Information security solutions typically fall into two categories: done through quantitative feeding of models by analyst gurus and the application of other machine learning approaches. The specified solutions that are constructed by analysts use rules that are defined by the analyst.

Although, they are well suited for identification of new threats since machine learning-constructed solutions alert of new cases that are unknown to hackers. However, the research on the learning-based techniques in detecting the cyber-attacks have not addressed four major problems.[1]

3. Anomaly Identification

In contemporary world every field has its’ own affiliations, from manufacturing industries to social networks, transportation system to economical system and from health care system to technology system. These systems that are complex structures of numerous interfaced parts and subassemblies are crucial to creation of proper economic conditions and economic growth, security of population, as well as improvement of people’s quality of life. However, the increasing number of such patterns leads to several problems, thanks to the fact that categorizing abnormalities is their fundamental task.

The term abnormal behaviours means that there are negative deviations in the model of functioning of various structures within complex systems; based on the behaviours mentioned above, it can be stated that these negative deviations pose a threat to the effective functioning, safety, and security of complex systems. These appear because of failure in equipment, hackers or because of environment challenges which are so many that they cause many losses, frequent failure in operations and at times even human lives. Therefore, for obtaining the more stable and reliable of the above complex systems, it is important and essential to utilize good anomaly detection and classification techniques that can cope up with the new complex threat and difficulties.

Outlier detection as a methodology of anomaly detection has previously been employing rules, statistical theory, and domain knowledge to determine the behaviors which are different from the norm. Even though these approaches have been quite successful in practice, they fail to meet the new requirements of complex adaptive systems: openness and diversity characteristic of the contemporary society. Moreover, given that these systems continuously produce large and complex [2]data, traditional solutions hit their floor, errors, and inefficiency concerning their capacity.

The new significant strategies to address these challenges consist of the new and modern methods like Artificial Intelligence and Machine learning. These technologies can handle the large data in real-time,[3] learn new patterns and can improve the dependability of classification of the anomalies such that the difficult systems are safe [4]and designed to be efficient in the changing environment.[5-7]

Conclusion:

Even though its usage remains rather limited, AI plays a very important role in the sphere of data protection. Mainly because of its capabilities to identify threats, find abnormalities, improve encryption, and offer predictions, it is an essential element of contemporary Security policies and measures. Thus, if threats in the sphere of cybersecurity remain a concern, the use of AI in data security will become more essential in protecting the safety and privacy of the digital environment.

Reference:

1. J. Lee, J. Kim, I. Kim, and K. Han, “Cyber Threat Detection Based on Artificial Neural Networks Using Event Profiles,” IEEE Access, vol. 7, pp. 165607–165626, 2019, doi: 10.1109/ACCESS.2019.2953095.
2. B. Dhamodharan, “Beyond Traditional Methods: A Novel Approach to Anomaly Detection and Classification Using AI Techniques,” Trans. Latest Trends Artif. Intell., vol. 3, no. 3, May 2022, Accessed: Jul. 29, 2024. [Online]. Available: https://www.ijsdcs.com/index.php/TLAI/article/view/488
3. M. Rahaman et al., “Port-to-Port Expedition Security Monitoring System Based on a Geographic Information System,” Int. J. Digit. Strategy Gov. Bus. Transform., vol. 13, pp. 1–20, Jan. 2024, doi: 10.4018/IJDSGBT.335897.
4. K. T. Putra, A. Z. Arrayyan, R. Z. Syahputra, Y. A. Pamungkas, and M. Rahaman, “Design a Two-Axis Sensorless Solar Tracker Based on Real Time Clock Using MicroPython,” Emerg. Inf. Sci. Technol., vol. 4, no. 1, Art. no. 1, May 2023, doi: 10.18196/eist.v4i1.18697.
5. Y. T. Negash, M. Moslehpour, P.-K. Lin, S.-C. Chiu, and Y.-Y. Liu, “Mapping Generation Y Tourists’ E-Loyalty: A Sustainable Framework through Hierarchical Structure and Fuzzy Set Theory,” Sustainability, vol. 13, no. 9, Art. no. 9, Jan. 2021, doi: 10.3390/su13094767.
6. Gupta, B. B., Gaurav, A., Arya, V., Alhalabi, W., Alsalman, D., & Vijayakumar, P. (2024). Enhancing user prompt confidentiality in Large Language Models through advanced differential encryption. Computers and Electrical Engineering, 116, 109215.
7. Chui, K. T. (2023, November). A Lightweight Generative Adversarial Network for Imbalanced Malware Image Classification. In Proceedings of the 5th International Conference on Information Management & Machine Intelligence (pp. 1-4).

Cite As

Reddy D.R.C (2024) AI’s Role in Protecting Our Data, Insights2Techinfo, pp.1

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