28 Nov The power of predictive security
Mazen Dohaji, Vice President (iMETA), LogRhythm looks at how companies can stay ahead of future threats.
To prepare for future digital transformation across the globe, organisations need to adopt a predictive cybersecurity strategy. Machine Learning (ML) and Artificial Intelligence (AI) can enable new levels of visibility and efficiency to create a stronger, future-ready security foundation.
Digital transformation efforts are helping workplaces across the region become increasingly flexible and efficient, but they are also creating new blind spots and vulnerabilities that are hard for security operations centres (SOCs) to identify. Combined with the vast amount of data that organisations process on a daily basis, monitoring threats has become increasingly challenging.
To overcome this, organisations need to be prepared to adapt their cybersecurity strategies to stay ahead of evolving threat tactics. Security teams now have an urgent need for more intelligent methods of detecting threats and malicious user behaviour. AI and ML can provide security teams with the ability to rapidly analyse security events and identify the types of threats that are putting their organisations at risk. Predictive analytics builds data driven profiles on users, networks, and assets to alert organisations to any suspicious behaviour.
A predictive future
Predictive security is transforming how organisations go about protecting their networks. It allows them to gain the capabilities to forecast future threats before they occur, and in turn, take the right actions to mitigate them.
Organisations that deploy a predictive approach benefit from powerful insights to protect the future of their businesses. They gain the tools to safely secure their network through smart and automated processes.
Understanding user behaviour: Predictive analytics provides an intelligent way of identifying users to capture patterns of behaviour. Security teams can overcome threats with ML-powered user and entity behaviour analytics (UEBA).
UEBA can help organisations monitor for known threats and behavioural changes in user data, providing critical visibility to uncover user-based threats that might otherwise go undetected.
With ML, security technologies can move beyond rule-based approaches that require prior knowledge of attack patterns. For example, security technologies using machine learning can learn the typical patterns of activity within a networking environment to recognise pattern deviations.
When an account behaves differently than the AI predicted, SOC teams can investigate further. If it’s an attacker, they can combat the threat, and if it’s a false alarm, they can reinstate permissions.
Preventing future attacks: ML predicts attacks by identifying malicious activity across networks. It then determines who might attack them, which vector an attacker will use and when this is likely to occur.
With highly accurate prediction tools at their fingertips, organisations can anticipate the future and prevent a potential attack from happening. SOC analysts can reduce their reliance on threat-hunting activities and avoid attacks going undetected within their networks for months. Rather than waiting until the damage has already been done, organisations can predict and therefore prevent the attack from happening.
Identifying weaknesses: Cyberattacks have the potential to impact any business, so organisations need to be aware of the areas of their network that cybercriminals are most likely to target.
Predictive analytics can build an understanding of an organisation’s vulnerabilities based on the security measures it uses and cybercrime trends across organisations. Threat monitoring and tracking activities to establish a baseline and detect anomalies enables predictive analytics to indicate potential risks. This can then identify how threat actors may attack them and where the weaknesses in their defences lie.
Removing workforce pressure: Automation is vital for overwhelmed security teams. Predictive analytics can help security teams identify threat patterns while taking the pressure off having to run manual processes. It also makes incident responses much faster, effective, efficient, and accurate.
With the right predictive analytics in place, IT teams can focus their efforts on more complex threat detection tasks, rather than getting caught up in mundane day-to-day activities.
Better, smarter, and faster security
In the next few years, predictive security will continue to be an essential part of any Chief Information Security Officer (CISO) and SOC plan. ML and AI can enable security teams and technology to be better, smarter, and faster by having advanced analytics readily available to predict threats.
By focusing on predictive measures and ensuring the right resources are in place to support this, organisations in the Middle East can gain intuitive threat detection by learning the patterns of normal activities and recognising anomalies such as the introduction or prediction of a new pattern, a change in an existing pattern, or the removal of a pattern.
Predictive security can prevent damaging attacks and reduce their impact through rapid detection. Security teams should take control of their digital futures with greater speed, agility and accuracy.
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