Humanity has gradually created an artificial environment around itself: buildings, engineering systems, transport infrastructure, industrial facilities, communication systems, and the entire technological world that now surrounds us. As this environment became more complex, a new category of hazards emerged — technological risks: equipment failures, fires, accidents, disruptions to technological processes, and other events generated by the very infrastructure created by human beings. First, we learned to recognise the consequences of such events. Then we learned to record them, analyse their circumstances, seek to prevent their recurrence, compare different sources of information, and use accumulated statistics to improve systems over time. Does this paradigm remain necessary? Absolutely — YES. But is it enough today???

The traditional analytical sequence looks approximately like this:
state → threshold change in state → event → reconstruction of context → analysis → conclusions → action or corrective response.
The main value emerges after the event, as facts accumulate. The main question is: What happened, and why?

However, as the artificial environment becomes more complex, denser, and more energy-intensive, both the number and power of interacting devices increase. Along with them increase the probability of complex events, the potential severity of their consequences, and the cost of subsequently reconstructing the context surrounding those events. Modern technologies already make it possible to observe changes as they develop, before their consequences become uncomfortable statistics.

This raises a broader question: Can the object of observation be not only the event itself, but also the process through which it is formed? And if so, can such observation be organised in a way that makes the formation of the context itself more verifiable and substantially reduces the scope for subsequent dispute?

Traditional observation systems are designed to record a state, the reaching of a defined threshold, or an event that has already materialised. Yet over the past few decades, these systems have begun to capture more than threshold states alone. Many now integrate and preserve additional context surrounding events. It may appear that enough information already exists — sometimes even too much. And, as always, that comfortable picture is disrupted by one familiar word: BUT.

An event never emerges from nothing. It is preceded by weak deviations, gradual degradation, changes across several interconnected parameters, failures in service procedures, or a sequence of indicators, none of which is yet perceived as an incident when considered in isolation. Modern computing systems, local observation tools, communications, and data analysis methods make it possible to change the very object of observation — and, with it, the philosophy of observing the artificial environment. The object of observation becomes not only a fact that has already occurred, but also the development of the environment that may lead to that fact.

The analytical sequence begins to look different:
state → change in state → formation of context → analysis → corrective action → change in the probability of a future event → return to an acceptable state or progression to a possible event.

The main value begins to emerge from observation and analysis before the event occurs. At the same time, there is a parallel branch that reinforces this value. If the event cannot be prevented, the context already formed retains independent value. This inevitably raises the question of trust in the very process through which that context was created:
state → change in state → formation of context → possible event → trust in the formed context → investigation → claims settlement and loss coverage.

The central question changes: What will change if we intervene now?

This is not about some magical prediction of the future. It is about identifying a meaningful change earlier, understanding the direction in which it is developing, and gaining time for considered action. Statistical analysis of past events remains necessary, but the information base available to it changes. It becomes richer in observations, temporal relationships, verified facts, and information about their provenance. A new layer is added to statistics — the systematic observation of processes that are still only forming a possible event. At this point, a further professional question arises: can the process by which observations of the environment are formed itself be trusted, and to what extent do data provenance, immutability, and continuity determine that level of trust? Can the event continue to be treated as the primary unit of observation when technology already makes it possible to observe the trajectory of its formation — and to make that trajectory dispute-resistant?

Loss prevention is not a new objective. Asset owners, engineers, operators, insurers, equipment manufacturers, and regulators are already engaged in it. The difference lies not in the objective itself, but in the stage at which observation begins, the way individual indicators are connected, how they are used for analysis, and how widely they are applied beyond their own professional domain. Some systems begin to act only after a regulatory or emergency threshold has been reached. Others attempt to identify deviations earlier. Service organisations observe the technical condition of equipment. Insurers assess risk. Operators receive warnings. Individual devices record individual parameters. Each of these approaches addresses its own specialised task.

Within its own domain, each appears logical, sufficient, and compliant with existing requirements. Yet there is rarely a common logic connecting the formation of knowledge about the state of the artificial environment. Observation is distributed across different devices, software systems, logs, organisations, and areas of responsibility. At the same time, the environment itself is rarely considered as a single structured sequence of states, changes, deviations, and possible events — particularly when cross-domain analysis becomes necessary. Fragmented observations are formed through different methodological approaches. As a result, even large volumes of information do not always combine into a coherent picture. Weak indicators exist and form local context. But this is no longer sufficient for timely analysis or for the formation of integrated knowledge about the environment itself.

Structured observation implies a different approach:
normal state → deviation → development of the deviation → conditionally acceptable state → corrective action → return to the normal state or transition to an incident.

Under this approach, value emerges not only when an alarm has already been confirmed, but also while the trajectory of the environment can still be changed. Looking deeper, that value begins even earlier. It exists before the first changes appear — in the way the environment is observed, in the provenance of those observations, and in the verifiability of the actions and processes accompanying their formation. Trust in data provenance itself gradually becomes an independent element of risk management. This approach does not guarantee the prevention of every event. But it can systematically create the conditions for earlier identification, assessment, and intervention, thereby increasing the probability of a more favourable trajectory for the environment.

This raises two interconnected questions. The first: If loss prevention is a shared objective, why has observation of risk formation still not been organised as a single structured function of the artificial environment itself? And the second: If such a function can be created, how should trust be formed in the data on which it will depend?

At this point, an important conclusion becomes apparent: Systematised observation of a multi-domain artificial environment does not simply create more data. It creates a new quality of data that cannot be obtained through the separate observation of individual domains — particularly where such observation lacks a common structure.

Even when a deviation is identified early, intervention may be impossible, delayed, or insufficient. Some events develop too quickly. In other cases, a warning may be received but fail to trigger the necessary action. Yet the new observation paradigm does not lose its value once an event occurs. On the contrary, it acquires another independent significance. Today, the circumstances of many incidents are reconstructed after the fact from available but often fragmented sources:
— readings from individual devices;

— service logs;

— operator reports;

— documents;

— witness accounts;

— subsequent expert conclusions.

A significant part of such reconstructed context may reasonably be challenged because of incompleteness, differences in data provenance, and the absence of a continuous temporal connection between sources. Most importantly, however, it may be challenged because there is insufficient trust in the provenance, immutability, and continuity of the data itself. The more complex the artificial environment becomes, the greater the scope for incompleteness, contradiction, dispute, action, and inaction. If, however, dispute-resistant context is formed simultaneously with the existence and development of the environment, what is preserved is not only the outcome, but also the sequence that preceded it:
— the state of the environment before the event;

— the emergence and development of deviations;

— data provenance;

— the technical condition of the observation tools;

— service history;

— actions taken, omitted, or delayed;

— the transition to an incident;

— the consequences.

This does not eliminate the possibility of dispute, nor does it make every record automatically true. But such a structure can improve the verifiability and completeness of the circumstances, substantially reduce the uncertainty surrounding the context of the event, and therefore significantly reduce the time required to investigate those circumstances. For insurance, asset management, and investigation, this may have independent economic value:
— lower costs of event reconstruction;

— a more precise understanding of causality;

— a reduced scope for dispute;

— more accurate differentiation between classes of risk.

This raises two further questions: How much uncertainty, investigation cost, and scope for dispute could be reduced if the context of an event were not reconstructed after a loss, but formed before it — and formed specifically as dispute-resistant context? And at what point should higher-quality context begin to influence not only investigation, but also underwriting, claims settlement, reserving, and reinsurance?

Modern analytics has access to vast volumes of information about events that have already occurred and their consequences, and can examine those events from many different perspectives. We know: — what happened;
— where it happened;
— how much damage it caused;
— what decisions were taken after the event. But far less is known about the trajectories that led to those events. Fragments of such data already exist: telemetry, service records, alerts, operator logs, and technical reports.

Yet they are rarely formed as a single, comparable, and continuous dataset linking: environment configuration → normal state → early deviation → development of the change → intervention or absence of intervention → prevented event or incident → consequences.

Particularly important are data not only about losses that occurred, but also about trajectories that were stopped in time. The awkward reality is that trust in such data often arises only through a separate expert assessment confirming that the data themselves can be trusted. As a rule, this makes such data exceptionally scarce and expensive. As a result, the available volume of trusted data remains relatively small. This makes the statistical base more vulnerable and, equally importantly, affects both the confidence in and the robustness of conclusions drawn within the wider analysis of the environment.

Without such data, it is difficult to answer questions such as:
— Why did one sequence end in an incident while another returned to a normal state?

— Which combinations of weak indicators are genuinely significant?

— Which service deviations increase the future frequency or severity of losses?

— Which interventions genuinely change the outcome?

It is impossible to guarantee the precise prediction of a specific future incident. However, the volume, comparability, and reliability of the underlying data directly influence the accuracy and robustness of predictive conclusions. Today, producing such trusted data usually requires separate expert validation and significant cost, which keeps the accessible volume limited. Yet it is now possible to form systematically a new class of data that does not currently exist in practice as a single, dispute-resistant, and analytically usable dataset. Modern technologies make this possible. And, importantly, such data can be formed entirely autonomously, without any dependence on the internet.

Once such a dataset exists, the data can be interpreted, compared, and used for:
— cohort analysis;

— hypothesis testing;

— assessment of intervention effectiveness;

— analysis of prevented events;

— development of new models of future loss frequency and severity;

— multi-domain analytics.

The issue, therefore, is no longer only the quality of analytical models. Two further questions arise: Which forms of risk analysis remain impossible today not because the models are insufficiently developed, but because the necessary data have never been systematically created within a dispute-resistant context? And can existing models be considered informationally complete if they primarily analyse the consequences of past events, but lack comparable trusted data about the processes that led to those events — or were stopped before they occurred?

The next stage in the development of technological risk management may lie not only in improving models that analyse events which have already occurred. It may require the systematic formation of data about processes that:
— lead towards an event;

— change under the influence of intervention;

— return to an acceptable state;

— continue developing into an incident;

— form verifiable context before consequences occur.

Such an approach does not replace statistics, investigation, engineering expertise, or insurance analysis. It expands their underlying information base. EGT is formed in accordance with the requirements of the method. On the basis of EGT, a connected dispute-resistant context of the trajectory is formed — DREC. The accumulation of comparable DREC creates a new analytical dataset. This dataset changes the information base for risk decision-making.

Instead of relying primarily on the reconstruction of the past, it becomes possible to observe the development of the artificial environment in a structured way, form dispute-resistant context before an event, and create data not only about losses that occurred, but also about trajectories that were prevented. It is this transition — from recording consequences to structured observation of the development of the artificial environment — that forms the basis of the broader logic of the LSMAD Master Concept.

Modern technologies have already given us new “letters” with which to describe the artificial environment. Yet most existing approaches are still trying to describe it in a language formed at a time when those “letters” did not yet exist. In my view, the time has come to ask: are we ready to begin using these new possibilities where they have already objectively emerged?