Open-source intelligence: a comprehensive review of the current state, applications and future perspectives in cyber security

Yadav, Ashok; Kumar, Atul; Singh, Vrijendra

doi:10.1007/s10462-023-10454-y

Cited by 15 publications

(7 citation statements)

References 70 publications

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“…According to the literature, addressing the legal and ethical challenges associated with AI in healthcare is imperative. These include concerns over privacy, potential biases, discrimination, and the overall safety of patients [ 50 , 51 ]. Moreover, there's a pronounced need for comprehensive regulatory frameworks specifically designed to oversee the use of AI in clinical environments.…”

Section: Reviewmentioning

confidence: 99%

From Pixels to Prognosis: A Narrative Review on Artificial Intelligence’s Pioneering Role in Colorectal Carcinoma Histopathology

Naseri,

Shukla,

Hiwale

et al. 2024

Cureus

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Colorectal carcinoma, a prevalent and deadly malignancy, necessitates precise histopathological assessment for effective diagnosis and prognosis. Artificial intelligence (AI) emerges as a transformative force in this realm, offering innovative solutions to enhance traditional histopathological methods. This narrative review explores AI's pioneering role in colorectal carcinoma histopathology, encompassing its evolution, techniques, and advancements. AI algorithms, notably machine learning and deep learning, have revolutionized image analysis, facilitating accurate diagnosis and prognosis prediction. Furthermore, AI-driven histopathological analysis unveils potential biomarkers and therapeutic targets, heralding personalized treatment approaches. Despite its promise, challenges persist, including data quality, interpretability, and integration. Collaborative efforts among researchers, clinicians, and AI developers are imperative to surmount these hurdles and realize AI's full potential in colorectal carcinoma care. This review underscores AI's transformative impact and implications for future oncology research, clinical practice, and interdisciplinary collaboration.

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Section: Reviewmentioning

confidence: 99%

From Pixels to Prognosis: A Narrative Review on Artificial Intelligence’s Pioneering Role in Colorectal Carcinoma Histopathology

Naseri,

Shukla,

Hiwale

et al. 2024

Cureus

View full text Add to dashboard Cite

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“…(Govardhan et al, 2023;Iashvili and Iavich, 2023) both emphasize the role of AI in enhancing cybersecurity through OSINT, addressing challenges in data analysis and penetration testing, respectively. This focus is shared by (Ranade, 2023;Yadav et al, 2023;Yamin et al, 2022) who explore AI's capacity to organize fragmented OSINT sources and evaluate the effectiveness of various OSINT tools in cybersecurity scenarios. These works collectively underscore the crucial role of AI in synthesizing and interpreting vast quantities of open-source data for cyber defense purposes.…”

Section: Jan čErnýmentioning

confidence: 99%

Implications of Large Language Models for OSINT: Assessing the Impact on Information Acquisition and Analyst Expertise in Prompt Engineering

Černý

2024

eccws

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This paper explores the potential use of large language models (LLMs) in Open Source Intelligence (OSINT), with a focus on integrating information acquisition and the increasing importance of prompt engineering for analysts. The research includes a comprehensive literature review, which highlights the widespread use of AI in OSINT and the related challenges, such as data validity and ethical concerns. The study emphasizes the significance of prompt engineering as a crucial skill that demands a profound comprehension of LLMs to generate validated intelligence. A model of the OSINT lifecycle that incorporates LLMs is proposed. The paper further discusses updated training in critical thinking, search techniques, and prompt engineering for intelligence professionals. The findings indicate a noteworthy shift in OSINT procedures, highlighting the importance of continuous research and education to fully utilize AI in intelligence gathering.

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“…Security breaches can be concealed by AI systems' incapacity to be explained and interpreted [ 27 ]. Open-source intelligence (OSINT) utilizes publicly available data from various sources, raising implications in areas like national security, political campaigns, the cyber industry, criminal profiling, societal issues, cyber threats, and cybercrimes [ 32 ]. The COVID-19 pandemic has introduced novel cybersecurity challenges, leading to the emergence of new themes in this field.…”

Section: Reviewmentioning

confidence: 99%

Unraveling the Ethical Enigma: Artificial Intelligence in Healthcare

Jeyaraman,

Balaji,

Jeyaraman

et al. 2023

Cureus

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The integration of artificial intelligence (AI) into healthcare promises groundbreaking advancements in patient care, revolutionizing clinical diagnosis, predictive medicine, and decision-making. This transformative technology uses machine learning, natural language processing, and large language models (LLMs) to process and reason like human intelligence. OpenAI's ChatGPT, a sophisticated LLM, holds immense potential in medical practice, research, and education. However, as AI in healthcare gains momentum, it brings forth profound ethical challenges that demand careful consideration. This comprehensive review explores key ethical concerns in the domain, including privacy, transparency, trust, responsibility, bias, and data quality. Protecting patient privacy in data-driven healthcare is crucial, with potential implications for psychological well-being and data sharing. Strategies like homomorphic encryption (HE) and secure multiparty computation (SMPC) are vital to preserving confidentiality. Transparency and trustworthiness of AI systems are essential, particularly in high-risk decision-making scenarios. Explainable AI (XAI) emerges as a critical aspect, ensuring a clear understanding of AI-generated predictions. Cybersecurity becomes a pressing concern as AI's complexity creates vulnerabilities for potential breaches. Determining responsibility in AI-driven outcomes raises important questions, with debates on AI's moral agency and human accountability. Shifting from data ownership to data stewardship enables responsible data management in compliance with regulations. Addressing bias in healthcare data is crucial to avoid AI-driven inequities. Biases present in data collection and algorithm development can perpetuate healthcare disparities. A public-health approach is advocated to address inequalities and promote diversity in AI research and the workforce. Maintaining data quality is imperative in AI applications, with convolutional neural networks showing promise in multi-input/mixed data models, offering a comprehensive patient perspective. In this ever-evolving landscape, it is imperative to adopt a multidimensional approach involving policymakers, developers, healthcare practitioners, and patients to mitigate ethical concerns. By understanding and addressing these challenges, we can harness the full potential of AI in healthcare while ensuring ethical and equitable outcomes.

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Open-source intelligence: a comprehensive review of the current state, applications and future perspectives in cyber security

Cited by 15 publications

References 70 publications

From Pixels to Prognosis: A Narrative Review on Artificial Intelligence’s Pioneering Role in Colorectal Carcinoma Histopathology

From Pixels to Prognosis: A Narrative Review on Artificial Intelligence’s Pioneering Role in Colorectal Carcinoma Histopathology

Implications of Large Language Models for OSINT: Assessing the Impact on Information Acquisition and Analyst Expertise in Prompt Engineering

Unraveling the Ethical Enigma: Artificial Intelligence in Healthcare

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