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Deep learning watchdog - natural language processing in risk management and cybersecurity.

Efficient and effective risk management together with a proper cybersecurity framework is the main pillar of a stable organization. Regulated (e.g. financial services or energy) or not every company has to put in place tools and organizational solutions that will be aimed at protecting the organization. Such protection is inevitable for stable growth and customers satisfaction.


Nowadays more and more businesses are moving to the digital world where data is the main source of income but also a risk for the company. At the same time, it is also a source of information about potential gaps, frauds, and operational issues. Based on the information produced within the organization, not only internal policies and procedures but also communication between business lines and customers.


But how the natural language processing can help with issues with risk management and cybersecurity? There are many ways. NLP is a way computers is processing information (including large datasets) in our human language (scope is to be determined by the relevant business owner) and translates it in a manner and ‘shape’ that is desirable. NLP may search for certain phrases or even context-specific pieces of text and put them into a more appropriate (from the human perspective) form, including executive summary or certain recommendations. It may also put in place self-decisions – if allowed by human(s).


A good example is where an NLP tool is engaged to search for compliance gaps within the internal governance framework. Such a tool can screen all the procedures, policies, and by-laws to find out whether such documents (and in fact – processes) are in line with relevant legal and regulatory requirements and if not – what should be done to ensure a sufficient level of compliance. It may also propose a level of risk that certain gaps may create for the organization that is a part of the risk management.


In addition, such solutions can find other important information such as potential risks in the cybersecurity framework, including assessing the potential risk of breach and/or risk of fraud. It is possible to screen incoming and outcoming messages (e-mails, social media messages) to find if it contains vulnerable information and/or phishing or similar attempt. Within seconds it may block the potential attack and report it to responsible persons that will be able to react and swiftly patch software or act in another manner.


Natural language processing may be also helpful in extracting data from technical documents and putting it in a more understandable format. It may save not only time and money but also make a difficult situation less stressful for all engaged persons. Less stress – better work – better results.


There are only a few examples of the application of NLP tools. Such solutions may be used almost everywhere where data is present (structured or unstructured). Of course, it will take some time to teach the AI-based tool that the NLP is, but in the end you will save much more.




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