The remainder of the paper is organized as follows: section "Literature review" presents a critical analysis of existing studies on cybersecurity risks, vulnerabilities, and AI applications in cybersecurity and secure software coding. Section "Research methodology" explains the ANN-ISM framework and the processes to analyze and mitigate cybersecurity risks and vulnerabilities. Section "Results and discussions" discusses the results detailing the hierarchical structure of cybersecurity risks and AI mitigation strategies. Section "Development of AI-driven cybersecurity mitigation model for secure software coding: using ANN-ISM approach" presents the proposed AI-Driven Cybersecurity Mitigation Model for Secure Software Coding (i.e., a framework using ANN-ISM). Finally, section "Implications of the study" concludes the paper by summarizing the findings, discussing the contributions of the proposed framework, and suggesting potential directions for future research.
Today, society's dependency on the Internet and its associated service extends across various sectors, transforming into a vital infrastructure underpinning modern life. The growing reliance on digital technologies highlights the indispensable role of cybersecurity, a field that extends its influence across numerous disciplines. As digital systems become integral to everyday life and business processes, cybersecurity has evolved from an optional safeguard to a critical foundation. It forms the cornerstone of protection for other sectors, ensuring data remains secure, accessible, and private.
In cybersecurity, AI is pivotal in improving the transparency and interpretability of machine learning models. This enables security experts to analyze and identify vulnerabilities, threats, or adversarial attacks with greater precision, strengthening and fortifying cyber-defense systems. The literature on AI-driven cybersecurity for secure software coding reflects significant advancements and ongoing challenges in this domain. This section scrutinizes existing research, highlighting key contributions, methodologies, and gaps in the field. The review is structured around the following four main themes.
AI has become a game-changer in cybersecurity by automating intelligence practices in a timely, accurate, and holistic manner to fight against threats. Some of the major application areas of AI in cybersecurity include threat detection and prediction, in which case an AI system sorts through enormous datasets to identify warning signs that may indicate a security risk. Machines use algorithms to analyze past data to predict future security weaknesses and enable organizations to avert system attacks. Furthermore, AI-oriented Intrusion Detection Systems (IDS) enable monitoring of network traffic in real-time to detect unique anomalies and malicious security events; advanced methods such as deep learning and support vector machines (SVMs) further improve the accuracy of zero-day attack detections. AI is critical in fighting phishing and social engineering defense via natural language processing (NLP) and machine learning (ML) methods by which message contents are evaluated, while URL and sender credibility are checked. Moreover, AI-driven tools analyze malware in detail using static and dynamic approaches and provide high detection accuracy for malware types using the Convolutional Neural Network (CNN). As regards dedicated data monitoring focusing on endpoint security, AI-based endpoint detection and response (EDR) systems observe the operations of devices to block them from unauthorized access and potential harm, ensuring enhanced detection of advanced persistent threats (APTs). AI further automates incident response activities, drastically increasing the speed required to remediate possible threats. Security Orchestration Automation Response (SOAR) systems employ artificial intelligence through platforms to mitigate threats efficiently. Additionally, AI enhances cryptographic protocols and optimizes algorithms to improve secure communication methods. Lastly, AI technologies also aid organizations in vulnerability management by creating risk ratings for prioritized assessments, facilitating targeted remediation actions, and leveraging predictive models to anticipate potential exploits by cybercriminals.
So, a compelling impact on cybersecurity risks alongside exploitable vulnerabilities may guide the software coding results of how a set of systems would usually defend their security architecture. Unresolved code vulnerabilities function as attack pathways that enable attackers to compromise data through unauthorized entry and disrupt operations. The susceptibility of systems arises from poor input validation alongside insufficient secure authentication methods because these vulnerabilities would allow attackers to conduct SQL injection and cross-site scripting. Secure coding practices have become more urgent than ever because sophisticated threats, including zero-day vulnerabilities, continue to appear. Zero-day exploits attack unidentified system flaws while systems remain unprotected, so developers must maintain continuous vulnerability management programs. Strong encryption methods are vital because insufficient encryption strategies and aging cryptographic standards create security risks that permit sensitive data to reach unauthorized parties. Third-party library combinations with framework integration during development carry potential risks that can affect project performance according to developmental models. Coded components contain active vulnerabilities and harmful code requirements, demonstrating the essential character of strict dependency administration. Disclosure attacks on model integrity caused by artificial intelligence and machine learning models in software development require builders to establish effective defensive measures during development. Secure software development requires multi-faceted solutions that combine standardized, secure coding practices, periodic security examinations, and developer training about emerging cyber risks. According to research, every phase of software development must incorporate cybersecurity elements so organizations can defend against potential risks and create more robust systems.
Programming software with secure coding principles protects both software systems' integrity, confidentiality, and availability of information. These practices work to stop exploitive attacks and protect sensitive data while building trust within digital systems. Developers support security through the least privilege principle by enforcing user and system privileges restrictions to the required levels necessary for their work tasks. An application that handles essential user data must limit access to this data only through authorized program processes to decrease data exposure to unauthorized breaches. Another cornerstone of secure coding is input validation and sanitization. The main entry point for attackers is through user-supplied inputs, especially when inadequately validated. Developers must use parameterized queries, vigorous validation checks, and output-escaping measures to reduce risks. Secure programming requires developers to separate input parameters from actual statements, so they should employ prepared statements instead of direct SQL query concatenation. Software deployment requires a double strategic approach combining Code reviews and static analysis to find flaws beforehand. Multiple team members assessing code during peer reviews help organizations receive different viewpoints regarding quality and security standards compliance. The code analysis tools SonarQube and Veracode monitor security threats by warning developers about threats consisting of insecure cryptographic usage and buffer overflows. Integrating computerized tools in the SDLC results in lower post-sectional vulnerability correction expenses than traditional post-deployment detection practices.
Additionally, incorporating secure libraries and frameworks is crucial. OpenSSL cryptographic interfaces and secure APIs for authentication exist as tested established frameworks that help developers avoid putting insecure code into their systems. Developers are responsible for library updates because outdated dependencies can act as security vulnerabilities. Finally, comprehensive logging and monitoring provide insights into application behavior and potential security incidents. The practice of secure logging involves both data anonymization along with measures to guarantee tamperproof log file management. The monitoring software Splunk and ELK Stack notify teams about atypical behavior, which helps them respond quickly to cybersecurity threats. Organizations create resilient information software systems by integrating strong cybersecurity practices across the Systems Development Life Cycle framework. Integration of AI technology within these cybersecurity practices proceeds at a slow pace. For example, studies by showed that automatic secure code analysis tools perform marginally due to their limited capacity to understand contextual situations, which causes recognition mistakes and incorrect results. AI-based reinforcement learning techniques can improve these tools' versatile functionality and precise performance.
Integrating AI frameworks with maturity models is pivotal in aligning AI adoption with organizational readiness and strategic growth. AI frameworks, encompassing tools, methodologies, and technologies for developing and deploying AI solutions, provide the technical foundation for automating processes, enhancing decision-making, and improving predictive capabilities. Professional tools evaluate how well organizations handle multiple dimensions like strategy and technology through defined stages of development. Combining AI frameworks with maturity models helps organizations move beyond user-dependent AI use and creates alignment that produces optimal results from technology adoption despite minimized risks. For example, the Capability Maturity Model Integration (CMMI) addresses distinct AI-related concerns, especially ethical aspects, along with data integrity and model coordination requirements. AI frameworks can assist organizations in systematically discovering operational skill deficit areas while collaborating with established maturity models to guide technology investments and specify the deployment of suitable solutions based on the organization's current readiness levels. Organizations with essential maturity struggle with data silos and knowledge gaps. However, AI solutions (e.g., TensorFlow or PyTorch) need to be implemented strategically and workforce preparation. Better matured organizations can unite artificial intelligence and sophisticated strategies like generative AI and independent systems for complicated decision-making and innovation. The integration also helps to improve governance and compliance. Explainability features and fairness mechanisms (e.g., AI Fairness 360 or LIME) that AI frameworks use to enforce some levels of rigor before products are put into production also benefit organizations through maturity models that help them inject such known-good criteria needed to satisfy regulators and society. AI implementation achieves a better return on investment when the investment connections are directly tied to measurable organizational business results. McKinsey conducted broad-scale research indicating that applying both AI structures and maturity models allows enterprises to deploy AI initiatives quickly and even quicker than with standalone AI tools. Results show that organizations following this combination are achieving 30% higher efficiency in adopting AI projects than their strategic alignment fellows the intersection of AI frameworks and maturity models presents organizations with a system that not only progresses transformational AI adoption but also integrates sustainability and scalability into the journey. Hence, integrating AI frameworks with organizational readiness assessments are advancing AI implementations by building ethical ways of attaining meaningful outcomes across various sectors.
With the recent advancement of Artificial Intelligence (AI) and Large Language Models (LLMs), AI-based code generation tools become a practical solution for software development. GitHub Copilot, the AI pair programmer, utilizes machine-learning models trained on a large corpus of code snippets to generate code suggestions using natural language processing. Large Language Models (LLMs) are a type of natural language processing (NLP) technique based on deep learning that is capable of automatically learning the grammar, semantics, and pragmatics of language and generating a wide variety of contents. Due to the extensive number of parameters and large-scale training datasets, LLMs have demonstrated powerful capabilities in NLP, often approaching or even surpassing human-level performance in NLP tasks such as text translation and sentiment analysis. Recently, AI code generation tools driven by LLMs that have been trained on large amounts of code snippets are increasingly in the spotlight (e.g., AI-augmented development in Gartner Trends 2024. AI tools can produce solutions that outperform those created by novice programmers in the case of simple and moderately complex coding problems. Generative artificial intelligence (GenAI) technologies using LLMs (large language models), such as ChatGPT and GitHub Copilot, with the ability to create code, have the potential to change the software-development landscape.
However, in this regard, the proposed "AI-driven Cybersecurity Framework for Software Development Based on the ANN-ISM Paradigm" outsmarts the traditional cybersecurity maturity models, such as the NIST framework or CMMI, because of its advanced nature of using predictive AI methods on continuous learning with the ISM. Therefore, here are some reasons why this model beats this already existing framework:
Unlike the traditional models of NIST and CMMI, the Cybersecurity Framework for Software Development Based on the ANN -- ANN-ISM paradigm provides a more dynamic, adaptive, and predictive solution. NIST and CMMI frameworks do provide a good framework to manage and improve security practices over time; however, they are reactive and costly to tackle new and unanticipated threats, as they require manual intervention. The ANN-ISM paradigm powered by AI makes a more real-time, more automated, and easier to scale to changing environment cybersecurity problem with the trade-off that many of the weaker tools of human bureaucracy are lost.
