Artificial intelligence and UK national security: Policy considerations

RUSI was commissioned by GCHQ to conduct an independent research study into the use of artificial intelligence (AI) for national security purposes. The aim of this project is to establish an independent evidence base to inform future policy development regarding national security uses of AI. The findings are based on in-depth consultation with stakeholders from across the UK national security community, law enforcement agencies, private sector companies, academic and legal experts, and civil society representatives. This was complemented by a targeted review of existing literature on the topic of AI and national security. The research has found that AI offers numerous opportunities for the UK national security community to improve efficiency and effectiveness of existing processes. AI methods can rapidly derive insights from large, disparate datasets and identify connections that would otherwise go unnoticed by human operators. However, in the context of national security and the powers given to UK intelligence agencies, use of AI could give rise to additional privacy and human rights considerations which would need to be assessed within the existing legal and regulatory framework. For this reason, enhanced policy and guidance is needed to ensure the privacy and human rights implications of national security uses of AI are reviewed on an ongoing basis as new analysis methods are applied to data

The Impact of IT on Insurance of the Technological Industry

The insurance industry has undergone significant transformations due to the rapid advancement of information technology (IT); This paper explores the multifaceted impact of IT on the insurance sector, covering various aspects such as customer experience, operational efficiency, risk assessment, and data security. Through a comprehensive review of existing literature and industry trends, this paper highlights the ways in which IT has revolutionized insurance processes and business models.” Additionally, this paper delves into the paradigm shift brought about by Insurtech startups, which leverage the convergence of IT and insurance to offer innovative solutions like peer-to-peer insurance and usage-based coverage. These startups are reshaping industry dynamics and compelling traditional insurers to adopt digital innovations to remain competitive. Furthermore, the regulatory landscape and compliance considerations arising from technological disruption are explored. The challenges of navigating data privacy compliance and the collaborative efforts between regulators and industry players in shaping technological policies are discussed. Ethical considerations related to IT-driven insurance are also examined, emphasizing the importance of maintaining transparency, fairness, and accountability in decision-making. Ultimately, this research paper underscores the pivotal role of IT in shaping the insurance industry's future, As technology continues to evolve, insurers that strategically integrate IT tools are better positioned to provide innovative, customer-centric solutions while enhancing operational efficiency, risk assessment accuracy, and data security, By embracing IT-driven transformations, insurers can navigate challenges, tap into opportunities, and maintain a competitive edge in the dynamic and rapidly evolving landscape of the insurance sector

National Conference on COMPUTING 4.0 EMPOWERING THE NEXT GENERATION OF TECHNOLOGY (Era of Computing 4.0 and its impact on technology and intelligent systems)

As we enter the era of Computing 4.0, the landscape of technology and intelligent systems is rapidly evolving, with groundbreaking advancements in artificial intelligence, machine learning, data science, and beyond. The theme of this conference revolves around exploring and shaping the future of these intelligent systems that will revolutionize industries and transform the way we live, work, and interact with technology. Conference Topics Quantum Computing and Quantum Information Edge Computing and Fog Computing Artificial Intelligence and Machine Learning in Computing 4.0 Internet of Things (IOT) and Smart Cities Block chain and Distributed Ledger Technologies Cybersecurity and Privacy in the Computing 4.0 Era High-Performance Computing and Parallel Processing Augmented Reality (AR) and Virtual Reality (VR) Applications Cognitive Computing and Natural Language Processing Neuromorphic Computing and Brain-Inspired Architectures Autonomous Systems and Robotics Big Data Analytics and Data Science in Computing 4.0https://www.interscience.in/conf_proc_volumes/1088/thumbnail.jp

Autonomous Threat Hunting: A Future Paradigm for AI-Driven Threat Intelligence

The evolution of cybersecurity has spurred the emergence of autonomous threat hunting as a pivotal paradigm in the realm of AI-driven threat intelligence. This review navigates through the intricate landscape of autonomous threat hunting, exploring its significance and pivotal role in fortifying cyber defense mechanisms. Delving into the amalgamation of artificial intelligence (AI) and traditional threat intelligence methodologies, this paper delineates the necessity and evolution of autonomous approaches in combating contemporary cyber threats. Through a comprehensive exploration of foundational AI-driven threat intelligence, the review accentuates the transformative influence of AI and machine learning on conventional threat intelligence practices. It elucidates the conceptual framework underpinning autonomous threat hunting, spotlighting its components, and the seamless integration of AI algorithms within threat hunting processes.. Insightful discussions on challenges encompassing scalability, interpretability, and ethical considerations in AI-driven models enrich the discourse. Moreover, through illuminating case studies and evaluations, this paper showcases real-world implementations, underscoring success stories and lessons learned by organizations adopting AI-driven threat intelligence. In conclusion, this review consolidates key insights, emphasizing the substantial implications of autonomous threat hunting for the future of cybersecurity. It underscores the significance of continual research and collaborative efforts in harnessing the potential of AI-driven approaches to fortify cyber defenses against evolving threats

Application of Artificial Intelligence in IoT Security for Crop Yield Prediction

This research explores the application of Artificial Intelligence (AI) in the Internet of Things (IoT) for crop yield prediction in agriculture. IoT devices, like sensors and drones, collect data on temperature, humidity, soil moisture, and crop health. AI algorithms process and integrate this data to provide a comprehensive view of the agricultural environment.AI-driven anomaly detection helps identify threats to crop yield, such as pests, diseases, and adverse weather conditions. Predictive analytics, based on historical and real-time data, forecast crop yield for informed decision-making in irrigation and fertilization.AI-powered image recognition detects early signs of pests and diseases, aiding timely treatment to prevent crop losses. Resource optimization allocates water and fertilizers efficiently, minimizing waste and environmental impact.AI-driven decision support systems offer personalized recommendations for ideal planting schedules and crop rotations, maximizing yield. Autonomous farming integrates AI into machinery for precision tasks like planting and monitoring.Secure communication protocols protect sensitive agricultural data from cyber threats, ensuring data integrity and privacy

Emerging Trends in Cybersecurity for Health Technologies

The paper delves into the intricate relationship between technological advancements in healthcare and the pressing need for robust cybersecurity measures. It explores the escalating vulnerability of sensitive medical data due to the sector's digital transformation and the increased susceptibility to cyber threats. The interconnectedness of healthcare systems, from wearable devices to complex electronic health record systems, exposes healthcare organizations to relentless cyberattacks. Within this context, the article meticulously examines emerging trends and innovative solutions aimed at fortifying cybersecurity infrastructure and safeguarding sensitive medical data. It scrutinizes ten cybersecurity risks prevalent within the healthcare domain, highlighting the multifaceted nature of data security challenges faced by healthcare entities. Furthermore, the paper meticulously dissects ten AI-driven security mechanisms, ranging from behavioral analytics to AI-powered compliance management, showcasing their pivotal role in ensuring data integrity and confidentiality. Collaboration emerges as a pivotal strategy, with the article outlining ten collaborative initiatives that underscore the significance of joint efforts among healthcare institutions, technology providers, and cybersecurity experts. Collectively, these insights illuminate the imperative for proactive and adaptive cybersecurity strategies within the evolving landscape of healthcare technology integration

Security issues in data analytical environments

Nowadays, data is ubiquitous and gives businesses capabilities they did not have access to before. Data analytics helps organizations transform raw data into valuable insights and is, therefore, a critical asset to any organization as a baseline for any important tactical, operational, and strategic decisions. However, although data analytics provides many benefits, new security challenges have emerged that hamper the effectiveness of organizational analytics efforts. New approaches to security are required to address these challenges. This research in progress paper provides an overview of security-related challenges surrounding data analytical solutions. In addition, the paper discusses shortcomings of current governance and security frameworks in addressing data analytics-specific security challenges and presents avenues for future research