As the world becomes increasingly digitized, so does the need for cyber security. With the global market valued at £20.5bn this year and growing annually, artificial intelligence (AI) is becoming an integral part of a successful cyber security strategy. AI technologies such as natural learning and machine learning are especially beneficial in improving end-point security, allowing for better protection of sensitive data and identification of goodware/malware across networks. But while AI offers many advantages to cyber security consultants, there are also certain challenges they must overcome in order to get the most out of their AI investments. 

Benefits of AI in the Industry

Platforms like ‘Deep Instinct’ offer a real-time monitoring system that can detect threats before they become an issue. This type of proactive approach is invaluable when it comes to preventing malicious attacks from occurring.

Vulnerability management is improved drastically with AI - providing a more comprehensive view of what parts of a network are vulnerable to attack. 

AI can help improve data centre performance by automating redundant tasks and eliminating human error from processes like patching and updating systems. 

A major and significant benefit of using AI for cyber security is its ability to process large amounts of data quickly - helping businesses stay up-to-date on the latest threats faster than ever before. 

Tash Norris (head of cyber security for Moonpig) informed ITPro.com of this benefit. The example she gave was that analysts typically look for correlations that they’ve seen before, or ones expected to see. With AI, an ‘unbiased approach to the data set is taken and is able to extract correlations that would’ve otherwise been missed. 

Challenges of AI in the Industry

As always with new technologies and strategies, there are some chinks in its armour. 

In the case of AI, it can be resource intensive; organizations will need access to large amounts of data sets, as well as skilled personnel and financial resources in order to effectively implement these technologies into their existing infrastructures. 

Additionally, hackers themselves have begun using AI technologies for nefarious purposes; this means that staying ahead of them requires constant vigilance and monitoring on behalf of IT departments and consultants alike, adding to the resource costs of staying ahead. Read our blog here on the impact of AI in the Risk management consulting here for a better insight on the risk variable's businesses are exposed to in relation to AI. 

We mentioned the need for data sets above, these are essential when utilizing machine learning algorithms so security teams need access to a large range of malicious codes, malware codes & anomalies so they can train their algorithms effectively which again depends on resources available within the organization or externally sourced data sets available online which may not always be reliable or trustworthy. 

Rounding Up

For consultants considering how to implement Artificial Intelligence into strategies and day-to-day work, Tash and other panellists agreed the most sensible place to deploy AI and machine learning systems is in the broad category of detection and response functions, including tasks like SIEM, SOAR, and EDR. By automating these more manual processes, staff can be freed up to work on more dangerous threats, using AI as a force multiplier to extend the capabilities of a security team.

The approach to implementation should be methodical and risk-conscious so that crucial areas where companies will see the most ROI quickest can be experienced, and then built upon those foundations moving forward.  

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