Secursat and Data Analysis
"All data is important, but some data is more important than others"
In a national and international scenario in which companies are increasingly oriented, also to comply with new regulatory developments in the sector, to a conscious collection of a significant amount of data, as well as to the development of data-driven strategies, Secursat, proposes an approach to security, and more specifically to the analysis of data relating to security processes and activities, focused on the need to identify, among the vast amount of information available, those relevant to understanding and optimising processes, as well as to strategically target investments, applying Machine Learning systems to simplify Data Analytics processes.
The basis of our approach
Our approach based on a no-code system has been developed considering the need to integrate information from analogue, more traditional devices with the new IoT protocols and the various digital tools and platforms, allowing a unique key to read the data and capable of synthesising so much information into those useful for directing decisions, preventing scenarios and behaviour. Our approach therefore allows the collection of huge amounts of data, not only in an easier way, but is also capable of responding to the need to simplify the processes of processing this data.
Secursat's approach was aimed at:
- Identify, through specific security expertise, the pattern of information processing by the different technologies;
- Subsequently identify the rules of behaviour and procedures needed to help optimise the large amount of data produced by the system.
Machine learning: from security skills to data
Secursat has therefore developed a data analysis model that is not only based on the quantity of information, but also and above all on its quality level, as well as its consistency with specific customer objectives. One of the main aspects is the use of no-code processing systems capable of adapting flexibly to different data processing requirements and changing over time, also in line with changing needs and scenarios. Aggregated, aligned, clear data clusters with an unambiguous reading model and oriented by strategic objectives is the basis for the application of machine learning today and for laying the foundations for the future development of generative AI, in order to constantly continue to improve processes.
An approach to security that aims to elaborate strategies capable of producing a concrete impact on raising the levels of protection and business protection through the improvement of management and operational processes, in order to build, depending on the scenarios and contexts, global security models and make the best performing technological choices for the business starting from data.
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