Applied Science and Engineering Journal for Advanced Research

1. Introduction

Cyber threats are getting more complicated, more common, and more harmful in the digital age. They are a major danger to the privacy, integrity, and availability of important information systems. Traditional, manual ways of responding to cyber incidents sometimes take a long time, are reactive, and don't work well with the way new cyberattacks change. As companies work to keep their digital infrastructure and sensitive data safe, there is a rising need for smart, flexible response systems that can work in real time.

In this case, artificial intelligence (AI) has become a game-changing technology that can automatically find threats, spot anomalies, and make decisions in real time. AI can improve the speed and accuracy of incident response, lower the risk of human mistake, and allow for proactive defensive plans by using machine learning, natural language processing, and predictive analytics. But putting AI into cybersecurity isn't without its problems. Algorithmic openness, trust, explainability, data quality, and the potential of adversarial attacks are all issues that present crucial ethical and practical questions.

This study looks at how the role of AI in automating cyber incident response is changing. It looks at both how it could change how threats are handled and the real-world problems that need to be solved for it to work. The research attempts to give a balanced view on how AI can be efficiently integrated into cyber defense systems while keeping human oversight and trust. It does this by combining performance evaluation with expert insight.

2. Literature Review

Adepu and Ramakrishna (2021) looked at the current status of controlled drug delivery systems and pointed out that there is a growing interest in biodegradable polymers, nanocarriers, and materials that respond to stimuli. They stressed that these kinds of systems have already showed a lot of promise in making drugs more available, lowering the number of doses needed, and reducing side effects. They also said that problems with formulation stability, scalability, and getting regulatory approval were still big problems.

Pillai, Bhande, and Pardhi (2023) In their book chapter, they talked about many methods and uses

The goal of the study was to find patterns, assess performance, and get expert opinions on what AI can and can't do when it comes to resolving cyber crises on its own.

2.1 Research Design
The study used a mixed-methods research design, which means it used both quantitative and qualitative methods. Quantitative data were gathered by testing system performance in simulated situations, while qualitative data were gathered by talking to cybersecurity professionals in semi-structured interviews.

2.2 Data Collection
1. Simulation-Based Performance Testing
We developed a controlled simulation environment by deploying a virtualized network infrastructure that looks like a medium-sized business network. Using standardized datasets like NSL-KDD and CICIDS2017, we carried out simulated cyber-attacks like phishing, ransomware, and insider threats. The simulation included three AI-based technologies for responding to incidents: anomaly detection models, machine learning-based threat classification, and automated playbook execution engines. The system's replies, such as how long it took to find the problem, how accurate it was, and how well it contained the problem, were noted.

2. Expert Interviews
We talked to 15 cybersecurity experts, including CISOs, SOC analysts, and AI researchers, in-depth and in a semi-structured way. We chose participants using purposive sampling to make sure they had the right skills. Interviews looked at their experiences, perceived benefits, technical and ethical problems, and how ready their organizations were to use AI in incident response. We taped the interviews, wrote them down, and then looked at them thematically.

2.3 Data Analysis
1. Quantitative Analysis
We used statistical software to look at the performance measures of AI-based technologies. We figured out and compared key metrics including precision, recall, false positive rate, mean time to detect (MTTD), and mean time to respond (MTTR) for different types of incidents. We performed a t-test and ANOVA to find out whether there were any big differences in how well different AI technologies worked.

Figure 1: Performance Metrics of AI-Based Incident Response Tools

Table 1 shows that the Threat Classification System (TCS) did better than the other AI-based tools on most metrics. It had the highest precision (94.1%) and recall (92.4%), the lowest false positive rate (4.3%), and the fastest detection time (4.1 seconds), making it the best tool for finding threats quickly and accurately. The Anomaly Detection Model (ADM) also did well, with a precision of 91.3% and a recall of 88.6%. However, it was a little slower to respond. The Automated Playbook Executor (APE) had the fastest response time (9.4 seconds), but it also had a higher false positive rate (7.9%) and somewhat worse detection accuracy. This shows that there is a trade-off between speed and dependability. Overall, TCS was the most balanced and useful tool. APE's speed shows that it is good for quick containment when used with precise threat detection systems.

3.2 Discussion of Quantitative Results
The Threat Classification System (TCS) has the best precision (94.1%) and recall (92.4%) of all the models examined. This means that it is very good at accurately identifying and categorizing threats. The Automated Playbook Executor (APE) had the fastest mean reaction time (MTTR = 9.4 seconds), which means it is good for jobs that need to be done right away. However, APE had a somewhat greater probability of false positives, which might cause operations to be interrupted for no reason.

Overall, the results back up what we already know: AI-based systems can cut down on detection and reaction times by a lot, making incident response more efficient (Kumar et al., 2021). However, the precision and recall scores showed that false warnings and incorrect classifications are still problems that need to be fixed by improving AI systems.

3.4 Discussion of Qualitative Findings
Most professionals agreed that AI makes responses much faster and more accurate, especially in circumstances with a lot of alerts. However, a lack of faith in AI judgments and the fact that they can't be explained were two of the main reasons people didn't want to use them. These worries are in line with research by Gadepalli et al. (2020), which stresses the importance of explainable AI (XAI) in applications that are important for security.

Participants also stressed the importance of a hybrid strategy, in which AI handles triage and automates low-risk occurrences while humans handle complex or unclear instances. People often talked about integration problems, especially with old systems and data silos, as problems that organizations face.

4. Conclusion

In conclusion, the study showed that AI-powered solutions make cyber incident response more faster and more accurate by cutting down on the time it takes to find and respond to threats and making threat classification more accurate. Still, trust in AI judgments, lack of explainability, and trouble integrating AI into existing systems are still major obstacles to adoption, even though these operational benefits exist. Experts pointed out that a collaborative approach is needed, where AI helps human analysts instead of replacing them. To fully exploit the potential of AI in automating cyber event response while keeping trust and security in the company, we need to use explainable AI models and strategic implementation frameworks to deal with these problems.

References

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