security middleware

It is common to look at many deep, persistent problems in the world and frame them in macro terms—climate change is a global issue, war is an issue between nation states, unemployment is an issue of the financial system. In the 1990’s, an effort was made to re-cast these challenges at a more micro-scale by defining them as matters of “human security.” As the term implies, human security was cast at an individual level, “It means, first, safety from such chronic threats as hunger, disease and repression. And second, it means protection from sudden and hurtful disruptions in the patterns of daily life—whether in homes, in jobs or in communities.”⁴

Throughout the rise and fall of intellectual fashions, though, one thing has remained consistent: macro people gripe that micro is too detailed and contingent and doesn’t scale up; and micro people complain that macro analysis can’t understand decision-making at the level it actually takes place.⁵

In between the micro and macro, though, there are in fact crucial intermediating “mesoscales”⁶ that are not well understood or exploited. In the context of analyzing issues related to human security, “mesoscale” can be used to describe the dense tangle of structures and systems that make up the functioning of our everyday world. Supply chains, for example, connect macro inter-state issues such as the Russian invasion of Ukraine to the micro issues of on the ground food insecurity in import-dependent countries like Egypt as the flow of commodities such as wheat are disrupted by conflict thousands of miles away. Similarly, the home mortgage lending system in the United States tied macro level decisions such as interest-rating setting by the Federal Reserve to micro-level individual decisions to invest in home ownership thereby creating a tight coupling that in a time of crisis proved to be essentially buffer-less.⁷

In software terms, the mesoscale is where middleware sits “act[ing] like the connective tissue between applications, data, and users.”⁸ Operating systems function as the foundational software layer that directly interfaces with hardware resources, providing essential services including memory management, process scheduling, device driver abstraction, and security enforcement through privileged execution modes. Middleware, conversely, operates at a higher abstraction level, positioned between the operating system and application software to facilitate standardized communication protocols, distributed processing capabilities, and service-oriented architectures across heterogeneous computing environments. While operating systems manage hardware-specific implementations and provide isolation between concurrent processes, middleware abstracts these platform-specific details, offering application developers consistent APIs and interoperability mechanisms that mask underlying system complexities, thereby enabling cross-platform development without requiring intimate knowledge of the hardware or operating system variations that middleware effectively encapsulates through its intermediary translation services.

One can think of the legal system of a country as another type of operating system —in this case the operating system for that society. It determines what actions are allowed and what the consequences are when actions take place outside the sanctioned boundaries. In the United States, documents such as the Constitution and the body of federal and state statutes provide the broad brush strokes for the operating system. Regulations and case law fill in the middleware layer dictating how these high-level ideas connect to everyday life. And administrative agencies and the court system apply the regulatory and case law middleware to a myriad of everyday decisions at the micro level.⁹

These mesoscale system elements today are often made up of complicated sets of inter-related technologies¹⁰ —be they layers of software protocols allowing information to flow across the Internet, supply chain handoffs flowing less processed inputs into finished products, or policy interpretations at the level of individual FDA drug reviewers impacting the clinical trial decisions of large multi-national pharmaceutical companies. The complicated and distributed nature of these networks has made understanding and influencing them complicated, and the increasing inclusion of AI elements into these systems is only adding another measure of “black-boxedness” into the mix.