The immediacy of this study is determined by the need to fight back against the modern cyber threats that arise in the process of building a digital economy. The issues of countering various cyber threats in the activities of small and medium enterprises, firms stand to be a serious problem. Its relevance is constantly increasing. This is due to a number of objective reasons, the main of which are the following. Firstly, the globalization of economic processes, which leads to a situation where the technical, software and information component of the Information System (IS) is the same in relation to all countries developed in terms of information. Secondly, a significant change in the landscape of the IS threats themselves. It should be noted that the changes affected both quantitative and qualitative characteristics. Malware, Network Scanning, Man in the Middle, Phishing, DNS Spoofing, Trojan Horses. These are just a few examples of cyber threats carried out against small and mid-sized businesses and government information systems every day. The current condition of the information security system of governmental and commercial structures does not provide efficient resolving of up-to-date cybersecurity problems and creation of confident interaction between the critical infrastructure objects. It should be assumed that there is a need to update the theoretical and methodological base and practical developments that can protect the rights and legitimate interests of the individual, business and the state from modern security threats and increase the level of security of our economy. The article logically combines the study of the modern landscape of cybersecurity threats, the construction of an empirical model of security threats (with the allocation of a monetization block), the demonstration of the results of processing statistical data characterizing the distribution of the frequency of occurrence of specific threats. The paper aims to build an empirical model of cyber threats based on a study of huge number of relevant literature sources and statistical data.