Articles

Quantum-Safe Artificial Intelligence: A Systematic Review of Post-Quantum Cryptography Applications

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Objective: The rapid development of quantum computing poses an existential threat to classical cryptographic systems that currently secure global digital infrastructure. In direct response to this quantum threat, post-quantum cryptography (PQC) has emerged as a critical field dedicated to designing algorithms resistant to quantum attacks. Simultaneously, artificial intelligence (AI) — particularly machine learning (ML) and deep learning (DL) — has demonstrated promising and emerging capabilities across cybersecurity domains, including cryptography.

Methods: This systematic review was conducted by searching IEEE Xplore, ACM Digital Library, Springer Link, Google Scholar, Scopus, and Web of Science using targeted keywords related to AI and PQC, covering literature published between 2015 and 2025. A total of 62 peer-reviewed studies meeting predefined inclusion criteria were analysed.

Results: A total of 38 key studies were identified and analysed across four principal application domains: algorithm design and parameter optimization (31.6%), cryptanalysis and security assessment (26.3%), side-channel attack detection and defense (23.7%), and secure deployment on resource-constrained devices (18.4%). Practical case studies demonstrate measurable performance gains, including a 27% reduction in key exchange time reported in a specific study [60] and 98.3% accuracy in side-channel attack detection reported in a specific study.

Conclusions: The synergy between AI and PQC represents a pivotal frontier in cybersecurity. This review provides a structured foundation for future interdisciplinary research in quantum-safe intelligent systems and identifies explainable AI (XAI) integration as the most critical open research direction.

The Role of AI in Customer Sentiment Analysis for Strategic Business Decisions

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Customer sentiment analysis has become a vital tool for businesses seeking to understand consumer emotions, preferences, and feedback in real-time. Traditional sentiment analysis methods often struggle with scalability, contextual interpretation, and processing unstructured data from diverse sources such as social media, customer reviews, and survey responses. Artificial Intelligence (AI) has revolutionized this domain by leveraging advanced Natural Language Processing (NLP) techniques, including transformer-based models (e.g., BERT, GPT), recurrent neural networks (RNNs), and sentiment-aware embeddings, to extract nuanced insights with higher accuracy and efficiency. AI-driven sentiment analysis enhances customer experience, optimizes marketing strategies, and informs strategic business decisions in areas such as product development and risk management. However, challenges such as algorithmic bias, data privacy concerns, and model interpretability remain critical hurdles. This paper explores these challenges while discussing potential solutions, such as debiasing techniques, federated learning for privacy-preserving sentiment analysis, and explainable AI approaches. Furthermore, it highlights future advancements that could improve the accuracy, reliability, and ethical application of AI in sentiment analysis, ultimately strengthening data-driven decision-making for businesses in dynamic market environments.

Revolutionizing DevOps Security: AI and ML-Enabled Automated Testing Approaches

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In modern DevOps environments, the integration of security practices poses significant challenges due to the fast-paced nature of Continuous Integration/Continuous Deployment (CI/CD) pipelines. Traditional security testing methods are usually too slow and reactive to address vulnerabilities effectively in such dynamic settings. To overcome these challenges, organizations are increasingly adopting automated security testing solutions that leverage Artificial Intelligence (AI) and Machine Learning (ML). This paper discusses AI and ML capabilities in automating security testing during DevOps. It talks about how these technologies can improve security by enabling real-time threat detection, reducing false positives, and adapting to new vulnerabilities through continuous learning. Key AI/ML-based tools and techniques, along with their integration into DevOps workflows, are also discussed in detail. It also covers the integration challenges and the potential of AI/ ML in security testing in the coming years.