The lack of high-quality public cyber incident data limits empirical research and predictive modeling for cyber risk assessment. This challenge persists due to the reluctance of companies to disclose incidents that could damage their reputation or investor confidence. Therefore, from an actuarial perspective, potential resolutions conclude two aspects: the enhancement of existing cyber incident datasets and the implementation of advanced modeling techniques to optimize the use of the available data. A review of existing data-driven methods highlights a significant lack of entity-specific organizational features in publicly available datasets. To address this gap, we propose a novel InsurTech framework that enriches cyber incident data with entity-specific attributes. We develop various machine learning (ML) models: a multilabel classification model to predict the occurrence of cyber incident types (e.g., Privacy Violation, Data Breach, Fraud and Extortion, IT Error, and Others) and a multioutput regression model to estimate their annual frequencies. While classifier and regressor chains are implemented to explore dependencies among cyber incident types as well, no significant correlations are observed in our datasets. Besides, we apply multiple interpretable ML techniques to identify and cross-validate potential risk factors developed by InsurTech across ML models. We find that InsurTech empowered features enhance prediction occurrence and frequency estimation robustness compared to only using conventional risk factors. The framework generates transparent, entity-specific cyber risk profiles, supporting customized underwriting and proactive cyber risk mitigation. It provides insurers and organizations with data-driven insights to support decision-making and compliance planning.

However, this extensive integration of IoT devices has also introduced significant cybersecurity risks. The Internet of Things (IoT) has attracted the attention of cybersecurity professionals after cyber-attackers started using IoT devices as botnets (Palekar and Radhika 2022). IoT devices are often vulnerable to various cyber threats, including distributed denial-of-service (DDoS) attacks, botnet exploitation, and data breaches, all of which can compromise critical systems' integrity, confidentiality, and availability. Understanding and mitigating the risks associated with IoT deployments is crucial in this evolving landscape, especially given the interdependencies between IoT components and systems.

We use a methodology based on a machine learning algorithm to quantify firms' cyber risks based on their disclosures and a dedicated cyber corpus. The model can identify paragraphs related to determined cyber-threat types and accordingly attribute several related cyber scores to the firm. The cyber scores are unrelated to other firms' characteristics. Stocks with high cyber scores significantly outperform other stocks. The long-short cyber risk factors have positive risk premia, are robust to all factors' benchmarks, and help price returns. Furthermore, we suggest the market does not distinguish between different types of cyber risks but instead views them as a single, aggregate cyber risk.

Cyber risk refers to the risk of defacing reputation, monetary losses, or disruption of an organization or individuals, and this situation usually occurs by the unconscious use of cyber systems. The cyber risk is unhurriedly increasing day by day and it is right now a global threat. Developing countries like Bangladesh face major cyber risk challenges. The growing cyber threat worldwide focuses on the need for effective modeling to predict and manage the associated risk. This paper exhibits a Machine Learning(ML) based model for predicting individuals who may be victims of cyber attacks by analyzing socioeconomic factors. We collected the dataset from victims and non-victims of cyberattacks based on socio-demographic features. The study involved the development of a questionnaire to gather data, which was then used to measure the significance of features. Through data augmentation, the dataset was expanded to encompass 3286 entries, setting the stage for our investigation and modeling. Among several ML models with 19, 20, 21, and 26 features, we proposed a novel Pertinent Features Random Forest (RF) model, which achieved maximum accuracy with 20 features (95.95\%) and also demonstrated the association among the selected features using the Apriori algorithm with Confidence (above 80\%) according to the victim. We generated 10 important association rules and presented the framework that is rigorously evaluated on real-world datasets, demonstrating its potential to predict cyberattacks and associated risk factors effectively. Looking ahead, future efforts will be directed toward refining the predictive model's precision and delving into additional risk factors, to fortify the proposed framework's efficacy in navigating the complex terrain of cybersecurity threats.

There is no surprise that AI and ML have become the key ingredients of modern technology and cyberspace. From wearables to robotics, AI is almost everywhere and in every sector. Most companies extend their hands to AI vendors to adopt AI into their workflow. They spent lots of time, money, and effort to ensure a successful project. However, Gartner estimated that more than 85 percent of AI projects fail and render errors. Another report says that around 70 percent of companies say that implementing AI has minimal or zero impact on overall workflow efficiency.

This article advances the knowledge on teaching and training new artificial intelligence algorithms, for securing, preparing, and adapting the healthcare system to cope with future pandemics. The core objective is to develop a concept healthcare system supported by autonomous artificial intelligence that can use edge health devices with real-time data. The article constructs two case scenarios for applying cybersecurity with autonomous artificial intelligence for (1) self-optimising predictive cyber risk analytics of failures in healthcare systems during a Disease X event (i.e., undefined future pandemic), and (2) self-adaptive forecasting of medical production and supply chain bottlenecks during future pandemics. To construct the two testing scenarios, the article uses the case of Covid-19 to synthesise data for the algorithms i.e., for optimising and securing digital healthcare systems in anticipation of disease X. The testing scenarios are built to tackle the logistical challenges and disruption of complex production and supply chains for vaccine distribution with optimisation algorithms.

The digital age has created several opportunities for us, and at the same time, we've been exposed to a whole new level of cyberthreats. There's no denying that cybersecurity is now an integral part of every business that wants to avoid being a victim of identity theft, data breaches, and other cyber risks. Cybercriminals are constantly searching for ways to compromise networks and steal sensitive information. These techniques are getting more advanced, and they might be challenging to detect by humans or traditional defense solutions. For this reason, organizations are looking to adopt AI techniques to strengthen their cybersecurity defense plan.

Internet and technology make it easier for businesses to develop and reach a larger range of their target market. The internet has truly become the backbone of business in the 21st century and is almost impossible to ignore. Although, as technology develops, so do cyber risks such as AI cyber-attacks. Cyber-attacks are getting more innovative and very common in the business world, from phishing emails, ransomware attacks and now deepfake AI cyber-attacks. Artificial intelligence(AI) is now programmed in almost all devices, with functions like advancing camera quality, face recognition and virtual assistants.

As cybercrime and fraud grow increasingly sophisticated, applying AI to cyber risk and to combat fraud as well as to take remedial action, is an important area of innovation. Fraudulent transactions and theft happen daily and many businesses and individuals lose revenue without knowing. Organized criminals and online scammers are increasing the sophistication and scale of their attacks. These attacks are becoming more subtle, with many scamming groups using machine learning algorithms to find new ways to target individuals and online businesses. Traditional approaches are proving inadequate to detect these attacks due to their ever-changing pattern, sequence, and structure. Traditional approaches also don't capitalize on today's technological capabilities.

The past 18 months have transformed society – and sped up the digital transformation of our world. On the plus side, digital technologies allowed business and society to continue to function even during lockdowns – helping companies survive, vulnerable people access healthcare and children continue to learn. When the worst of the pandemic is, someday, behind us, we'll be able to take many of these lessons – and technological advancements – with us to enable greater access to healthcare (especially mental healthcare), education, job training and finance. And it provided a much-needed boost to the pandemic economy. The UN's Sustainable Development Report 2021 highlighted the role of technology manufacturing as a key driver of the economic recovery, citing the rise in demand for computer electronics due to the global shift toward working from home, remote-learning and e-commerce.