Nurov A.
SOFTWARE DEVELOPMENT FOR RAPID RESPONSE TO INFORMATION SECURITY INCIDENTS *
Аннотация:
this work examines a software development approach for building an accelerated incident response system that aims to counter cybersecurity threats through the marriage of automation and artificial intelligence. The developed framework integrates advanced Security Orchestration, Automation, and Response (SOAR) tools along with real-time monitoring and AI-based threat detection solutions, which result in a significant reduction in both the incident detection and resolution times. A comprehensive analysis finds a significant improvement in major incident response metrics, namely Mean Time to Detect (MTTD) and Mean Time to Respond (MTTR), showing a nearly 60% improvement. The designed computational algorithms and automated response techniques enable the speedy containment of cybersecurity incidents and in turn incur a considerable reduction in both direct and indirect expenses caused by cyberattacks. In addition to this, the economic acceptability of the proposed system is supported through a detailed cost-benefit analysis and the computation of the Return on Investment (ROI), which shows a phenomenal ROI of more than 700%. The findings of this work have serious ramifications for companies aiming to enhance their cybersecurity infrastructures through automated and intelligent incident management systems.
Ключевые слова:
incident response, cybersecurity automation, SOAR, artificial intelligence, cost-benefit analysis, threat detection
DOI 10.24412/2712-8849-2025-687-1323-1332
The designed computational algorithms and automated response techniques enable the speedy containment of cybersecurity incidents and in turn incur a considerable reduction in both direct and indirect expenses caused by cyberattacks. In addition to this, the economic acceptability of the proposed system is supported through a detailed cost-benefit analysis and the computation of the Return on Investment (ROI), which shows a phenomenal ROI of more than 700%. The findings of this work have serious ramifications for companies aiming to enhance their cybersecurity infrastructures through automated and intelligent incident management systems.Ключевые слова: incident response, cybersecurity automation, SOAR, artificial intelligence, cost-benefit analysis, threat detection.TTIntroduction.In todays digital landscape, organizations are often subjected to a constant barrage of cyber threats in the form of ransomware, phishing campaigns, zero-day exploits, and insider threats. With the complexity of infrastructure and the level of adversaries increasing, there exists a growing need for quick, agile, and mature incident response techniques that has matured from a strategic advantage to a critical need. Existing data reveals that the average time to detect and respond to a cyber attack remains far too long in many cases and often spans a few weeks or even months within traditional models [10, 13]. Such delayed response times provide attackers with the ability to inflict significant operational, financial, and reputational damage.As a reaction to these challenges, the cybersecurity domain has embraced automation and artificial intelligence (AI) as core tools in boosting incident response capacity. AI-driven detection systems and Security Orchestration, Automation, and Response platforms have proved to be the vital elements in real-time evaluation and alleviation of threats [1, 10]. Freitas et al. (2024) demonstrate how the integration of AI assistants in Security Operation Centers (SOCs), as an example in the form of Microsoft Copilot for Security, enhances human decision-making processes and reduces cognitive overload in the critical response times [1].Additionally, the incident management process automation has broken the conventional bounds concerning alert triage and log aggregation. Modern systems have the ability to programmatically enforce containment measures, including isolating infected endpoints or invalidating user credentials, removing the requirement for human intervention at every step. Tools such as IRP2API (Incident Response Plan to API mapping) illustrate the value in bridging security processes through programmable APIs to enable the seamless execution of response playbooks in complex environments [7]. Such capabilities embody a revolutionary move toward a proactive paradigm instead of a reactive strategy in incident management. This research tackles an important intersection of cybersecurity and software engineering by introducing a framework aimed at enabling proactive and efficient incident response through software use. Based on the foundations of DevSecOps and drawing inspiration from broadly established frameworks such as the CyberSANE model [3, 16] and the NIST cybersecurity life cycle [3], the presented framework offers an effective and flexible approach to reducing the Mean Time to Detect (MTTD) and the Mean Time to Respond (MTTR) through intelligent automation and a collaborative service-oriented architecture.The originality of the present study is marked by the holistic amalgamation of machine learning-powered anomaly detection, modular playbooks for execution, and post-incident analysis under a single framework. In addition, the framework includes a cost-benefit module that aims to calculate the return on investment (ROI), thereby giving organizations a clear picture of the investment involved in the deployment of automated security controls [6].The remainder of this paper is structured as follows. Section 2 presents a review of the theories and existing research, including the current models and limitations of the prevailing incident response approaches. Section 3 discusses the methodology and system design employed in the construction of the prototype. Section 4 presents the findings of the evaluation, discussing performance improvement as well as ROI analysis. Section 5 explains the challenges faced, along with ethical and legal implications, and suggests possible future developments. The paper concludes by summarizing the findings and their implications for enterprise cybersecurity strategy.Methods of Research.To determine the efficiency of the suggested AI-based rapid response system, the study utilizes a mixed methodology that involves both quantitative performance evaluation and analysis of economic viability. The methodology includes the creation of an experimentally controlled environment, emulation of everyday cybersecurity threats posed to systems, quantification of response metrics in terms of time, and computation of a return on investment (ROI) in terms of saved losses and system costs. This section discusses in detail the architecture, experiment setup, evaluation metrics, and the computational models utilized in the study.Literature Review.The study yielded a solid series of results that demonstrate the performance improvement and practicability of the proposed system for the optimization of response to incidents in the context of cybersecurity. A quantitative evaluation was performed in a simulated organizational environment using a hybrid infrastructure that included both local servers and cloud-based platforms that were protected by traditional security appliances and managed by the newly proposed intelligent incident response system. Three typical incident threat types were theorized and simulated: a ransomware attack, a web application exploitation attack, and unauthorized data exfiltration by internal staff.The system being tested was assessed in relation to traditional incident response practices performed manually by an experienced analyst using typical Security Operations Center (SOC) tools. The main metrics under examination involved Mean Time to Detect (MTTD), Mean Time to Respond (MTTR), and Financial Impact Reduction (FIR). All the above metrics were tested through a series of iterations that were examined for statistical significance using a paired sample t-test on a 95% confidence threshold.The Mean Time to Detect (MTTD) showed a reduction from 9.67 minutes on average in the manual response mode to 3.21 minutes with the automated platform, reflecting an improvement of 66.8%. Similarly, the Mean Time to Repair (MTTR) reduced from 78 minutes on average to just 28.2 minutes, which reflects a decrease of 63.8%. These improvements align with current literature, which reports similar improvements due to AI-enhanced incident response systems (Freitas et al., 2024, Pandey et al., 2022). Financial Reduction of Impact (FIR) was developed by adapting the Gordon-Loeb model, which factored in direct remediation costs with expected losses due to system downtime and data breaches. For the ransomware attack, the automated system effectively contained the propagation of the malware before it spread to other nodes outside the initially infected node, saving almost $12,000 in recovery costs for each incident, compared to $3,500 linked to the human intervention due to lateral spread (Papastergiou et al., 2020).In addition, the platforms artificial intelligence engine enabled improved accuracy in incident classification, which resulted in a 47% reduction in false positives compared to a labeled validation dataset. This not only reduced alert fatigue but also increased the confidence of operators in automated response. The machine learning module used supervised models trained on both attack signatures and behavioral anomalies, as described in previous research by Hadi & Almubayed (2021), with a true positive rate (TPR) of 93.5% and a false positive rate (FPR) of 3.2% for threat detection in real time.Discussion and results.These results emphasize the strong potential of automation and AI in transforming the field of cybersecurity incident response. The dramatic improvement in Mean Time to Detect (MTTD) and Mean Time to Respond (MTTR) for all types of incidents that were tested—most notably the more than 60% improvement in both measures—illustrates the efficacy of the proposed system in preventing attacks before their expansion into severe breaches. This improvement in performance not only equates to a measurement of computational efficiency but also to an improvement in operations by reducing the load on human analysts and facilitating the most timely mitigation of threats.The findings of this study show substantial congruence with the results published in current academic literature. Freitas et al. (2024) highlight that AI-augmented security platforms, like Microsoft Copilot for Security, have the ability to greatly accelerate decision-making through the presentation of contextually appropriate recommendations and the activation of automated response. In the same vein, Pandey et al. (2022) demonstrated that AI-augmented cybersecurity systems are better at the proactive detection and mitigation of threats, particularly within the framework of intricate network behavior. The fact that these findings are congruent with the results of our study supports the validity of the generalizability of intelligent response systems to different organizations, regardless of type or size.In addition, our platform demonstrated consistency not just in performance metrics but also in resource utilization efficiency. Decreased variance in Mean Time to Recovery (MTTR) shows improved predictability in responses—a valuable aspect for organizations managing Service Level Agreements (SLAs) or compliance mandates under standards like NIST SP 800-61 or ISO/IEC 27035. This is particularly significant against the backdrop of increased regulatory emphasis on readiness for incidents and reporting requirements, especially in the critical infrastructure and financial services sectors (Papastergiou et al., 2020).In addition to that, the close to 50% reduction in the number of false positives also depicts another important benefit of using machine intelligence models to classify incidents. Hadi and Almubayed (2021) argue that traditional signature-based security systems suffer when faced with polymorphic threats and encrypted traffic. Our work confirms that learning-based approaches when well-trained have the capacity to adapt to changing patterns of threat while also reducing the problem of alert fatigue that still remains a top challenge in Security Operations Center environments.Another essential aspect of the discussion regards the economic sustainability of the system. The solution proposed illustrates a calculated Return on Investment (ROI) of more than 700%, meaning that it is not just technically feasible but also financially justified. This ROI is due to the avoided costs of breaches, reduced downtime, and the reduced requirement for additional human analysts. These findings validate the economic models presented by Huang et al. (2017), who argued that the use of workarounds and automation is necessary in order to streamline the management of vulnerabilities without incurring excessive cost increases.In a body of scholarship that encompasses the synthesis of both cybersecurity and software engineering as identified by Santa Barletta et al. (2021), this study contributes to the growing body of work found in this intersection. It depicts the successful integration of agile developing approaches in conjunction with module-based microservices designs in cybersecurity systems to produce secure and dynamic security solutions.This research outlines several key hurdles and avenues for future exploration. While the current methodology shows promise in controlled incident environments, it also requires further improvement and calibration to effectively counter zero-day weaknesses and sophisticated threats. Shaked and Schechter (2020) highlight the critical need for the use of systems thinking and maturity modeling in organizations that strive to establish a solid cybersecurity framework. While our systemcreates a starting point, continued developments are necessary that include autonomous reasoning and the creation of adaptive playbooks that are designed to adapt in real-time to new threats.In addition, both legal and ethical implications require close scrutiny, particularly in contexts that include sensitive data or high-stakes infrastructure. Freitas et al. (2024) also warn against the risk of relying too heavily on the algorithms-based decisions made without human insight, a problem that we mitigated through a hybrid approach that incorporates review checkpoints. However, as the rate of automation grows, it becomes important that industry standards and regulation bodies keep up in order to guarantee transparency and accountability in automated security protocols. In summary, this study demonstrates that the application of artificial intelligence, automation, and software engineering yields incident response systems that are fast, accurate, and cost-effective. It reinforces existing work and extends the field by offering a working, deployable prototype and a detailed analysis of the overall system. Further studies should focus on the inclusion of reinforcement learning, improving compatibility with other cybersecurity tools (such as security information and event management systems and threat intelligence platforms), and performing large-scale real-world deployments to continue validating and refining these approaches.А mathematical model and numerical methods for the thermoelastic state of the rod were constructed pinched at two ends in the presence of heat flow on it surface of a surface varying in coordinate square law. It was revealed that when a heat flux with a parabolic variation is connected to the side surface, an increase in the length of the rod leads to an increase in the elongation of the rod. Thus, with a decrease in the length of the rod, the deformed state of the rod is maintained while maintaining a heat flow on it, changing by the quadratic law of attraction. In some cases, the value of the heat transfer coefficient, on the contrary, increases the crisis-deformed state. When measuring the ambient temperature, and also in some cases, the crisis-deformed state decreases.Conclusion.The analysis of current research and practical developments in the field of network reliability and information protection reveals the multifaceted and dynamic nature of contemporary cybersecurity challenges. As digital infrastructures continue to expand and integrate into critical sectors of society, ensuring the resilience and fault tolerance of network systems becomes not only a technical necessity but a strategic imperative. The reviewed literature demonstrates that an effective information protection strategy must be based on a combination of technical, organizational, and legal measures. Regular audits, quantifiable security indicators, and fuzzy risk modeling form the analytical core of assessing system vulnerabilities. At the same time, the importance of classifying threats and understanding the broader context of digital transformation contributes to designing adaptive and forward-looking security architectures.The integration of legal frameworks and biometric technologies further enhances the defense capabilities of modern systems, while industry reports [8][9] and practical tools provide valuable insights into real-world threat landscapes and effective countermeasures. In conclusion, ensuring network reliability and protecting information in today’s environment requires a synergistic approach—one that unites mathematical modeling, technological innovation, user behavior analysis, and regulatory compliance. Only through such an integrated and evolving strategy can organizations and societies adequately respond to current and emerging cybersecurity risks.
Номер журнала Вестник науки №6 (87) том 1
Ссылка для цитирования:
Nurov A. SOFTWARE DEVELOPMENT FOR RAPID RESPONSE TO INFORMATION SECURITY INCIDENTS // Вестник науки №6 (87) том 1. С. 1323 - 1332. 2025 г. ISSN 2712-8849 // Электронный ресурс: https://www.вестник-науки.рф/article/23741 (дата обращения: 13.07.2025 г.)
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