Need for predictive tools to combat complex threats is greater than ever

• Cybersecurity is undergoing massive shifts in technology and ML can play a vital role in this by capturing insights through its predictive capabilities. 
• Predictive analytics brings a new level of visibility to the security environment.
• With the increase of data from multiple sources, security teams are facing new, as yet unknown, challenges.

With thousands of touchpoints in any given network, cybercriminals are effectively exploiting weak points on an almost daily basis. 

As the Middle East continues to roll out new digital initiatives, such as the upcoming Saudi Vision 2030, cybersecurity demands an increasingly proactive approach. 

To succeed in a rapidly expanding threat landscape and protect critical infrastructure, organisations in the Middle East need a greater focus on predictive cybersecurity to stay ahead. Machine Learning (ML) can enhance efficiency in an organisation’s ability to detect and mitigate security risks. 

According to Markets and Markets, the global ML market is expected to grow to $8.81 billion by 2022, at a compound annual growth rate (CAGR) of 44.1 per cent. 

Cybersecurity is undergoing massive shifts in technology, and ML can play a vital role in this by capturing insights through its predictive capabilities. 

Predictive analytics brings a new level of visibility to an organisation’s security environment. 

They don’t just provide information on past attacks, but they also help security teams understand their network vulnerabilities and where hackers are likely to target. Predictive capabilities are designed to enable organisations to mitigate a potential attack before it’s too late. 

When it comes to cybersecurity, a rapid response is vital. ML delivers strong predictive capabilities when it comes to detecting suspicious patterns of behaviour. These tools enable a more advanced detection than manual investigations and empower an organisation to upscale its cybersecurity strategy.  

Motivations are unpredictable 

The challenge in predicting cyberattacks using current technology is that there are many areas for attackers to target. On top of this, their motivations are unpredictable, and the attack surface is growing at an accelerated rate. 

Increased adoption of Bring-Your-Own-Device (BYOD) and remote working policies are helping organisations become more flexible but this shift has also created vulnerabilities and weak spots that are harder to manage in a dispersed IT environment. 

With the increase of data from multiple sources, security teams are facing new, as yet unknown, challenges when it comes to monitoring and detecting emerging threats and keeping up with new attacks in real-time. Success lies in the ability to deploy a predictive approach to accelerate threat detection response. 

A redefined security approach 

ML is more efficient and provides far better capabilities compared to humans in recognising and predicting certain types of patterns. With ML, security technologies can move beyond rule-based approaches that require prior knowledge of known patterns.

For example, security technologies using ML can learn the typical patterns of activity within a networking environment to recognise pattern deviations, which is far superior to regular manual investigations. 

Given the sheer volume of data from activities occurring in today’s systems and applications, ML’s pattern recognition and predictive capabilities have become incredibly important. 

However, its effectiveness relies on having access to large sets of high-quality, rich, structured data capturing network activities across numerous endpoints. If ML algorithms ingest data sets that aren’t accurate, clear, well-organised, and comprehensive, they’re not going to produce the desired results. 

To effectively detect threats, security teams need to employ the correct algorithm for that threat type. The rest of an organisation’s tools provide security context and relevancy.

A security information and event management (SIEM) solution can integrate and correlate information from many tools, such as human resources (HR) systems, identity management solutions, vulnerability scanners, and asset management systems.

When used together, ML and the other tools generate the risk information needed to prioritise human actions. Without prioritisation, there are so many anomalies, that it’s impossible to examine them all and find the truly harmful ones. 

ML can enable organisations and technology in the Middle East to work better, smarter and faster by having advanced analytics at its fingertips to solve real problems. 

User and entity behaviour analytics (UEBA) is a perfect application for ML as long as the necessary security context is available for understanding the significance of each anomaly. This will enable organisations to operate with a high level of efficiency when detecting threats from the outset while giving cybersecurity a chance to be proactive instead of reactive.  

Predicting the future 

The need for predictive tools to combat the advancing, complex threats within IT environments is greater than ever. Predictive security has the potential to enable organisations in the Middle East to take their security efforts to the next level.  

Technologies powered by ML offer a great deal of promise by significantly reducing human effort and identifying suspicious patterns of behaviour. If deployed correctly, ML has the potential to play a big role in the future of the security of Middle Eastern organisations. 

• Mazen Dohaji is the Vice-President for iMETA at LogRhythm

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