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<p>Information security (IS) is one of the most important aspects in the era of the internet and artificial intelligence (AI). As networking and AI advance, ensuring IS has become increasingly vital. Over the decades, steganography and cryptography have proven to be powerful pillars in addressing IS issues. However, with advancements in the field of AI, a different era of security concerns is emerging. Therefore, it is the need of the hour to highlight the importance of integrating AI with IS, especially in these two dominating fields: cryptography and steganography. This work presents a systematic literature review (SLR) focusing on the integration of AI with IS, particularly in cryptography and steganography techniques. Cryptography and steganography, which involve secret writing and data hiding, have long been pivotal in protecting private and sensitive data. Over the years, cryptography and steganography have seen significant advances to enhance their effectiveness in securing data. The SLR conducted in this article analyzed 116 relevant articles from the last two decades. The review aimed to provide comprehensive insights into integrating AI with cryptography and steganography, using meticulously designed research questions, inclusion–exclusion criteria, and quality assessment. The findings show how AI integrates with these techniques, specify the AI methods applied, list the datasets used, and describe the evaluation methodologies. The findings of this review can directly inform and enhance IS applications. The findings can support the development of cryptographic systems capable of generating truly random keys, automating cryptanalysis (CA) and steganalysis (SA) attacks, and designing lightweight encryption modules. In addition, the findings can guide the creation of steganographic algorithms with high payload capacities. Furthermore, the research outcomes can be applied to the development of AI-based cryptography and steganography systems tailored to specific domains. Using AI techniques, these systems can effectively improve security measures and adapt to the evolving threat landscape.</p>

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Integration of artificial intelligence with information security: a systematic literature review

  • 期刊论文

  • 发表日期：

    2026年1月5日

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  • Lokesh Negi,
  • Prakash Rao Ragiri,
  • Shobha Bhatt,
  • Geetanjali Garg,
  • Kirti Sharma,
  • Nidhi Bhatt

摘要

Information security (IS) is one of the most important aspects in the era of the internet and artificial intelligence (AI). As networking and AI advance, ensuring IS has become increasingly vital. Over the decades, steganography and cryptography have proven to be powerful pillars in addressing IS issues. However, with advancements in the field of AI, a different era of security concerns is emerging. Therefore, it is the need of the hour to highlight the importance of integrating AI with IS, especially in these two dominating fields: cryptography and steganography. This work presents a systematic literature review (SLR) focusing on the integration of AI with IS, particularly in cryptography and steganography techniques. Cryptography and steganography, which involve secret writing and data hiding, have long been pivotal in protecting private and sensitive data. Over the years, cryptography and steganography have seen significant advances to enhance their effectiveness in securing data. The SLR conducted in this article analyzed 116 relevant articles from the last two decades. The review aimed to provide comprehensive insights into integrating AI with cryptography and steganography, using meticulously designed research questions, inclusion–exclusion criteria, and quality assessment. The findings show how AI integrates with these techniques, specify the AI methods applied, list the datasets used, and describe the evaluation methodologies. The findings of this review can directly inform and enhance IS applications. The findings can support the development of cryptographic systems capable of generating truly random keys, automating cryptanalysis (CA) and steganalysis (SA) attacks, and designing lightweight encryption modules. In addition, the findings can guide the creation of steganographic algorithms with high payload capacities. Furthermore, the research outcomes can be applied to the development of AI-based cryptography and steganography systems tailored to specific domains. Using AI techniques, these systems can effectively improve security measures and adapt to the evolving threat landscape.