Data intelligence for decision-making in hybrid warfare environments

Today, the operating environment has changed dramatically. It is no longer just a matter of analyzing physical scenarios or conventional conflicts, but of dealing with much more complex contexts, where the main challenge is the sheer volume of data from multiple sources, domains, and levels. However, we do have mature technologies, such as advanced analytics, Big Data, and the exploitation of open sources, which make it possible to transform this overload into useful indicators and alerts for decision-making.

One of the main challenges is the move to operations in a multi-domain environment. Traditionally, intelligence was structured across strategic, operational, and tactical levels, linked to physical domains. This approach has evolved to incorporate new domains such as cyberspace and the cognitive domain.

In this context, the multidomain can be understood as a complex sphere of action in which all domains are interconnected and interdependent. An action in one of them can generate decisive effects in the others. For example, a disinformation campaign on social media can create distrust among the population and influence decision-making at different levels. That is, a non-physical action can have direct effects in multiple areas simultaneously.

This scenario falls under the concept of hybrid warfare, also known as gray zone conflict. These are actions that do not exceed the threshold of a conventional military response, yet they seek to exploit vulnerabilities in the target system. This type of conflict focuses especially on the cognitive domain, i.e., on the perceptions and decisions of the population and policy makers. Ambiguity and difficulty of attribution complicate the answer and increase uncertainty.

The third major challenge is information saturation. In the current context, the problem is not the lack of data, but its volume. Information is generated continuously and in different formats, which makes it difficult to analyze. In this scenario, threats often manifest themselves as weak, scattered, and difficult to detect signals.

Traditional approaches, based on manual analysis or isolated indicators, do not scale to this level of complexity. The real challenge is to transform large volumes of information into useful, understandable, and actionable knowledge for the decision-maker, at the right time.

Before addressing the role of technology, it is necessary to define what capabilities enable these challenges to be met. Any decision-oriented system must be able to integrate information from multiple sources, detect anomalies and weak signals, correlate information between different domains, and transform data into clear indicators for the decision-maker. All with a common goal: to reduce the time between detection of a relevant signal and decision.

These capabilities are directly aligned with the OODA cycle (Observe, Orient, Decide, Act), the classic decision-making model. In complex environments, the advantage lies not only in deciding well, but in doing so before the adversary. Technology does not make decisions, but it does transform data into useful information for the decision-maker to act more quickly and judiciously.

Once the capabilities have been defined, the next step is to understand which technologies enable them to be realized. This is where the concept of Big Data comes into play, understood not as a specific technology, but as an approach that drives new ways of processing and analyzing information.

On this basis, platforms like the one developed by GMV in the field of homeland security make it possible to transfer these capabilities to the real operating environment.

The first step is the integration and processing of information in a single environment. In this process, data becomes structured knowledge and, through analysis, intelligence to support the decision. For this purpose, ETL/ELT and standardization processes are applied to ensure that data is prepared for use, while always complying with security and classification requirements.

Advanced analytics and artificial intelligence are at the core of these capabilities. They do not replace the analyst, but amplify his or her capabilities by enabling information processing, intelligent searches, and automatic extraction of relevant knowledge, including document analysis, transcription, or translation. In any case, artificial intelligence does not make autonomous decisions: there is always human validation and quality control.

Multidomain correlation is based on graph models that allow the representation of relationships between events and facilitate the identification of non-obvious patterns.

Finally, all this information is translated into dashboards, alerts, and indicators oriented to the decision-maker. They make it possible to synthesize complex information and support prioritization and agile decision-making.

A clear example is the deployment of a tool that provides the operator with the most relevant information within a COP (Common Operational Picture), where data, analysis, and decision-making converge in a single environment that integrates maps, indicators, and collaborative capabilities.

Here, the key is not to accumulate information, but to use it efficiently. Technology allows the analyst to transform data into useful knowledge and reduce uncertainty without replacing the decision-maker.

Ultimately, the competitive advantage lies in moving through the decision cycle more quickly and accurately. In a time-critical environment, deciding earlier can make all the difference. It is precisely here where data intelligence becomes a key element to meet the challenges of today's security.

Author: Pablo Crego Nieto