The Role, Impact and Risk of Artificial Intelligence (AI) on Cybersecurity

INTRODUCTION

In today’s modern age, data rule supreme in the world. The protection of data against cyberattacks is essential. Softwares, hardware and networks are protected by cybersecurity but in recent times, the threat from cyberspace is growing daily. Traditional algorithms are proving to be ineffective against them. The defensive tactics of cybersecurity professionals are unsuccessful. Artificial intelligence (AI) is now a necessary tool used to improve cybersecurity.

Artificial intelligence is prominent nowadays as it plays a leading role in cybersecurity. It has revolutionized how organizations protect against cyber-attacks. The increase in cyberattacks has necessitated the need for more advanced cybersecurity methods that would be more intelligent and robust. As a result, defence systems will make quick decisions and efficiently defend against complex attacks.

Taking into consideration the current state of cybersecurity all over the globe, it is expected that cybersecurity experts are giving artificial intelligence (AI) a serious thought as a potential field that would support advancements in traditional cybersecurity techniques. AI-based behavioural analysis techniques improve intrusion detection systems by spotting abnormalities and departures from expected behavior.

AI is also crucial for automating incident response, enabling quicker cyberattack mitigation, and minimizing human errors. AI methods ensure more precise identification of harmful software by enhancing malware detection and prevention.

Timilehin Olalusi
Executive Associate

1.0 CYBER ATTACKS AND ARTIFICIAL INTELLIGENCE

Cyber-attacks have become a huge problem in recent years, and traditional cybersecurity methods are unable to successfully address the issue. Artificial intelligence (AI) has evolved as a data reformatting tool capable of solving and securing data. Data organization and protection from cyber assaults is a major responsibility[1]. Firms and organizations can strengthen their defence mechanisms, proactively identify dangers, and respond to attacks in actual time by employing the use of AI.

Artificial Intelligence (AI) consists of a variety of technologies, such as natural language processing, machine learning, and neural Networks[2] that allow computers to replicate anthropomorphic intellect and the ability to make decisions. These forms of technologies have numerous benefits for cybersecurity applications. They can analyse massive volumes of data, identifying patterns, and boosting the preciseness as well as the effectiveness of threat identification and mitigation.

Artificial Intelligence (AI) powered systems may continually acquire knowledge from fresh data and change their defence techniques to keep up with the ever-changing or emerging threats. Artificial Intelligence (AI) has infiltrated several sectors in cybersecurity by designing applications for detecting intrusions, malware analysis, identifying anomalies, security of networks, and data privacy, among others.[3]

• ARTIFICIAL INTELLIGENCE (AI) APPLICATIONS IN CYBERSECURITY

1. a. NEURAL NET

Deep Learning is an advanced branch of Artificial Intelligence (AI). It is also known as Neural Nets. Neural nets were first discovered by Frank Rosenblatt as “Perception”.[4] Perception is an algorithm that is developed for supervisory learning of binary classifiers. The process of solving a problem can be learnt by working together in small numbers. The main part of neural nets is the artificial neurons. These Neural nets are used mostly for Intrusion Detection and Prevention.

There is research being conducted[5] to make use of these neural nets in areas of distributed denial-of-service (DDoS) attack detection, spam detection, malware detection, classification, etc. The important reason for the usage of these neurons is the speed with which they work when used in hardware and graphic processors.

1. b. MACHINE LEARNING

Machine learning has had a huge impact on Cybersecurity. It has been discovered that humans make mistakes when analysing data or information. This is the vast difference between an AI system and the human brain. One significant advantage of AI technology is that it is systemic. The system offers the advantage of preventing errors or missing attack details. Using AI to analyse logs and packets from networks has guaranteed that threats are caught as soon as possible. The AI technology recognizes systems, examines the accessible records, and discovers system logs. This feature ensures that system administrators can modify the information accessed to prevent additional loss. This feature has led to the belief that AI will slowly replace human analysts.[6]

1. c. MANAGEMENT OF VULNERABILITY

The management of vulnerability is a distinct feature of artificial intelligence robots that manages possible flaws in organizations systems. According to research conducted, approximately 20,362 vulnerabilities were reported in 2019. In comparison to 2018, there was an 18% increase.[7] This demonstrates that organizations are still undergoing growth. There is the existence of threats everyday. For human personnels, managing these vulnerabilities is becoming tiresome. This factor necessitated the integration of artificial intelligence technologies to manage recorded exposures. This has in turn, made it difficult for hackers to get access into systems. Vulnerability management is one of the significant advantages of Artificial Intelligence’s impact on cybersecurity.

• THE SIGNIFICANCE OF AI IN CYBERSECURITY[8]

Artificial Intelligence (AI’s) significance in cybersecurity lies in its ability to provide advanced threat detection, automate responses, adapt to evolving threats, and handle large-scale data analysis. As cyber threats continue to evolve, integrating AI into cybersecurity strategies becomes increasingly pivotal for maintaining robust and effective defences.

1. Advanced Threat Detection:

AI enable a more sophisticated and accurate threat detection. Machine learning algorithms can analyse vast data sets and identify patterns, anomalies, and potential threats in real-life. This proactive approach allows for the early detection of emerging threats, including previously unseen and sophisticated attacks.

2. Behavioural Analytics:

AI excels in behavioural analytics, which involves analysing patterns of user behaviours and network activities. By establishing a baseline of normal behavior, AI systems can detect deviations or anomalies that may indicate a security threat. This helps in identifying insider threats and zero-day attacks that traditional security measures might fail to detect.

3. Adaptive Security Measures:

AI enables security systems to adapt and evolve based on the changing threat landscape. As cyber threats have become more sophisticated, AI can continuously learn and update its algorithms to stay ahead of emerging risks. This adaptability is crucial in maintaining robust cybersecurity defences.

4. Large Scale Data Analysis:

Cybersecurity generates massive amounts of data from various sources, including logs, network traffic, and user activities. AI can handle and analyse data on a large scale, identifying patterns and trends that might be indicative of a security threat. This ability to process big data is essential for effective cybersecurity in modern day interconnected and data-driven community.

5. Reducing False Positives:

AI can help reduce the number of false positives in security alerts. Traditional security systems often generate false alarms, leading to alert fatigue and potentially overlooking real threats. AI’s ability to contextualize data and understand normal behavior patterns helps in distinguishing between genuine threats and false alarms.

2.1 THE IMPACT OF ARTIFICIAL INTELLIGENCE ON CYBERSECURITY

In the modern world today, there are different impacts of artificial intelligence on cybersecurity globally. These impacts are both positive and negative. However, the impact of AI on cybersecurity has helped and contributed to the growth of all Industries. Artificial intelligence has affected enterprises and corporations. However, the overall consequences of the embrace of AI for cybersecurity are both beneficial and harmful. Attacks on businesses have proven to be increasingly hazardous. Attackers/ hackers have discovered ways to improve their knowledge to find flaws in cybersecurity solutions.

The technology has demonstrated that machine learning algorithms are superior to humans in terms of security[9]. By incorporating artificial intelligence into cybersecurity, errors are removed or avoided. Artificial intelligence systems have taken on distinct responsibilities in guaranteeing cyber security. Artificial intelligence on a wider scale has also been predicted in the future. This element will imply that artificial intelligence will be extensively developed to ensure the highest level of safety in organizations.

As a result of technological advancements, cybersecurity is now faced entirely with new challenges. Although specific vulnerabilities are still present for years, cybersecurity experts must devise new ways to safeguard networks from persistent threats.

1. BOTNETS: These are used to execute Distributed Denial of Service (DDoS) attacks, and
2. IDPS: These generate large numbers of false alarms and mislead cybersecurity professionals from detecting serious threats.

These are two of the current challenges plaguing the cybersecurity space.

A botnet is a network of bots, which are computers and other devices. A botnet is a network of infected computers that are linked together[10]. AI can detect botnets lurking within networks. Botnet detection will aid in the prevention of distributed denial of service attacks, leaks of information, and the invasion of more devices. Network and system administrators use an IDPS to detect intrusions[11].

When an intrusion is detected, the IDPS may send email alerts to authorized administrators. To achieve a higher level of security, network administrators must effectively configure IDPS tools. Systems for detecting and averting intruders depend on software and hardware. Network administrators can install a system on the network or on a host, which is known as network based IDPS. One of the key concerns is that there is no standard setup, therefore setting up and configuring an IDPS takes quite a long time.

Network traffic varies by organization. As a result, IDPSs generate many false warnings or “false positives.” AI is expected to reduce false alarms and improve detection rates in cybersecurity and network management.

2.2 THE RISK OF ARTIFICIAL INTELLIGENCE IN CYBERSECURITY

 Like any other technology, AI has its benefits but integrating AI in the workplace can introduce more complex concerns or issues. Though AI systems can aid in the defence against hackers, they also additionally create new targets for hackers, potentially posing several new cybersecurity vulnerabilities to individuals and businesses. Regular users do frequently overlook security fixes for their devices. They frequently utilize and operate software that do not have patches. As a result, unpatched applications run in the background and are rarely used by typical users. As AI becomes more widely used, information regarding AI becomes more widely available to anyone.

In other words, AI contributes significant benefits in Cybersecurity but however, its use can result in several hazards.

PREVENTION OF THE RISK OF ARTIFICIAL INTELLIGENCE IN CYBERSECURITY

While AI provides significant advantages in cybersecurity, it also poses possible hazards that must be addressed. Below are some precautions to take to avoid the possible hazards linked with AI in cybersecurity.

1. ETHICAL CONSIDERATION:

Organizations should design and only use AI technologies in cybersecurity with clear ethical norms. Privacy, bias, justice, and responsibility should all be addressed in these rules. Regular ethics education and audits can assist in guaranteeing that AI systems follow ethical guidelines.

1. PRIVACY AND DATA SECURITY:

AI systems frequently acquire sensitive or personal data. To prevent illicit or unauthorized access or data breaches, organizations must incorporate robust data security methods such as the use of encryption, access controls, and safe storage.[12] Compliance with appropriate data protection rules is critical for protecting user privacy.

1. HUMAN SUPERVISION:

It is pivotal to have human supervision over AI systems to avoid unforeseen outcomes. It aids in the identification of potential biases, the making of ethical decisions, and the responsible usage of AI. Furthermore, artificial Intelligence (AI) models should be built to provide justifications and explanations for their actions, thereby increasing openness and accountability.

1. SYSTEM STURDINESS AND ENDURANCE:

AI systems should be built with resilience and sturdiness in mind. Implementing fail-safe measures, redundancy, and backup procedures can help guarantee that AI systems continue to work effectively even in the light of attacks or system failures.

Regular testing, verification, and updates are required to keep the system resilient. Organizations may effectively limit the risks connected with AI in cybersecurity by taking these preventive steps, allowing them to utilize the benefits of AI technology while guaranteeing a safe and reliable atmosphere.

CONCLUSION

Organizations may boost their cybersecurity status, preserve sensitive data, and efficiently defend against sophisticated cyber-attacks by using the power of AI while taking preventive measures. AI technology will continue to evolve, and innovation will continue to affect the future of cybersecurity, thus bringing new opportunities and challenges that necessitate continuous study, improvement, and interaction. Thus, a collaborative effort from the society, businesses and organizations, is required to help design and implement viable AI initiatives while maintaining human dignity.

FOOTNOTES:

[1] Samtani, S., Zhao, Z. and Krishnan, R., 2023. Secure Knowledge Management and Cybersecurity in the Era of Artificial Intelligence. Information Systems Frontiers, 25(2), pp.425- 429.

[2] Patel, H., 2023. The Future of Cybersecurity with Artificial Intelligence (AI) and Machine Learning (ML).

[3] Rjoub, G., Bentahar, J., Wahab, O.A., Mizouni, R., Song, A., Cohen, R., Otrok, H. and Mourad, A., 2023. A Survey on Explainable Artificial Intelligence for Network Cybersecurity. arXiv preprintarXiv:2303.12942.

[4] Gupta, B.B., Gaurav, A., Panigrahi, P.K. and Arya, V., 2023. Analysis of artificial intelligence- based technologies and approaches to sustainable entrepreneurship. Technological Forecasting and Social Change, 186, p.122152.

[5] Montasari, R., 2023. Countering Cyberterrorism: The Confluence of Artificial Intelligence, Cyber Forensics and Digital Policing in US and UK National Cybersecurity (Vol. 101). Springer Nature.

[6] Ansari, M.F., Dash, B., Sharma, P. and Yathiraju, N., 2022. The Impact and Limitations of Artificial Intelligence in Cybersecurity: A Literature Review. International Journal of Advanced Research in Computer and Communication Engineering.

[7] Naik, B., Mehta, A., Yagnik, H. and Shah, M., 2022. The impacts of artificial intelligence techniques in augmentation of cybersecurity: a comprehensive review. Complex & Intelligent Systems, 8(2), pp.1763-1780.

[8] https://www.encryptionconsulting.com/the-role-of-artificial-intelligence-ai-in-modern-cybersecurity/

[9] Rani, V., Kumar, M., Mittal, A. and Kumar, K., 2022. Artificial Intelligence for Cybersecurity: Recent Advancements, Challenges and Opportunities. Robotics and AI for Cybersecurity and Critical Infrastructure in Smart Cities, pp.73-88

[10] Turransky, A. and Amini, M.H., 2022. Artificial Intelligence and Cybersecurity: Tale of Healthcare Applications. Cyberphysical Smart Cities Infrastructures: Optimal Operation and Intelligent Decision Making, pp.1-.

[11] Taddeo, M., 2019. Three ethical challenges of applications of artificial intelligence in cybersecurity. Minds and machines, 29, pp.187-191.

[12] Bresniker, K., Gavrilovska, A., Holt, J., Milojicic, D. and Tran, T., 2019. Grand challenge: applying artificial intelligence and machine learning to cybersecurity. Computer, 52(12), pp.45-52.