Home Articles Cyber resilience and machine learning – The perfect partnership

Cyber resilience and machine learning – The perfect partnership

by Ramsés Gallego

The business of the future will need to take the journey from cybersecurity to cyber resilience if it wishes to protect its valuable data.

Malicious parties are on the hunt for valuable data, and your organization is their target. This has long been the fundamental reason behind cybersecurity, but the truth is that cybersecurity alone is no longer sufficient. Organizations can no longer afford to react to incidents after they have already happened with a “What now?” attitude.

Instead, modern organizations should be embarking on the journey to achieving true cyber resilience – the Holy Grail when it comes to protecting your data.

Let us use the metaphor of the military. It is not enough for a country simply to have armies defending their borders and important buildings. They require active intelligence that operates proactively to deal with threats to national security before they manifest in full.

The analogy extends to cyber resilience – you need to be setting up your systems to anticipate threats and deal with them before they become a problem. It is important to note in this analogy that cyber resilience does not replace cybersecurity; just as military intelligence improves upon the work of armed forces, cyber resilience operates on top of your cybersecurity solutions and complements them to provide proactive, superior protection.

There are four key areas of cyber resilience which go above and beyond traditional cybersecurity functionality:

  • Anticipating – Rather than identifying issues once they have happened, cyber resilience solutions anticipate issues before they take place.
  • Withstanding – Your infrastructure should be designed so that, even if you are breached, important resources still cannot be accessed and stolen.
  • Recovering – If you are attacked, your system should be equipped to recover.
  • Evolving – Your system should automatically learn from incidents to be better prepared in the future.

As is evident from the above explanations, cyber resilience is focused on preparing your organization’s systems ahead of time so that when you are attacked (because it is a matter of when not if), your IT systems are proactively equipped to deal with the threat with minimal effect on your organization.


The importance of machine learning

Key to the proactivity of cyber resilience solutions is their use of machine learning. This is particularly evident in the Anticipating and Evolving areas of cyber resilience.

Machine learning enables your organization to evolve by learning from incidents that have already happened, as well as from the insight gleamed from the continuous stream of data it processes. The identification of behavioral patterns then enables the cyber resilience solution to identify suspicious deviations from these patterns and anticipate these threats before they become a problem.

Additionally, the best machine learning solutions can also learn from their own mistakes – such as false positives – ensuring that it is easier to narrow down true suspicious activity and ignore deviations that are not malicious.


Supervised versus unsupervised machine learning

It is also important to understand that there are four types of machine learning: Supervised, Unsupervised, Reinforcement, and Deep Learning. The difference between supervised and unsupervised is particularly pertinent in the context of cyber resilience.

Supervised machine learning needs a data scientist to continually feed the right data and variables to the system so that it can digest this data and turn it into valuable insights. While supervised machine learning is valuable, unsupervised machine learning improves upon this by removing the middle man. Unsupervised machine learning technologies consume the data themselves and determine the right variables to prove or disprove hypotheses.

It is therefore the preferred form of machine learning for cyber resilience solutions due to the autonomy of the technology – it does not require you to invest resources such as data scientists.

Not only does this reduce the load on your staff; since technology does not need to sleep, and does not make human errors, unsupervised machine learning enables a more rigorous, accurate, and continuous cyber resilience solution.


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