Predictive Analytics Are The Future For Cyber Security

Cyber security experts and analysts are constantly developing new methods in the changing IT security landscape. Recently damages from fast growing cyber crime have reached around $6 trillion every year. However, there is a lack of transparency in data breach awareness at the organisational level and if it continues, it could lead to a  significant impact on individuals. 

Today, predictive analytics is a key discipline in the field of data analytics, an umbrella term for the use of quantitative methods and expert knowledge, to derive meaning from data and answer fundamental questions about a business, the weather, healthcare, scientific research and other areas of inquiry. 

In the context of businesses, that process is often referred to as business analytics and it can be very effectively applied to cyber security.

The Data Breach Challenge

Last year, roughly 281.5 million people were affected by some kind of data breach, so it was a busy year for cyber criminals. Malicious attacks cost significantly more than data breaches resulting from glitches, human error, or negligence. Cyber security attacks are a serious threat to all businesses and charities. Their frequency isn’t going down and phishing remains the most common breach.

Organisations need a strategy to protect themselves, their customers, and their data from growing cyber security threats. 

The  EU GDPR stipulates that personal data must be processed using appropriate technical and organisational measures. It doesn’t specify a set of cyber security measures but expects the organisation to take action to manage risk. The GDPR has encouraged and, in some cases compelled, some organisations to engage with cyber security for the very first time. The threat of financial penalties and the resulting reputational damage has prompted action. A significant example is British Airways, who had £183m fine issued by the British Information Commissioner’s Office following a major breach which affected more than 400,000 customers

According to DataBreachesClaims.org.uk the amount of data breach compensation under UK laws is significant, but it depends on the nature of the breach and the impact it has had. Not only have organisations increased their prioritisation of cybersecurity, but also their spending in this area. 

• The GDPR has successfully prompted improvements in cyber risk management in the context of regulation. Curiously, the GDPR hasn’t impacted all organisations evenly. To be more precise, the finance and insurance industry was more likely to have made positive changes so as to boost cyber security. 

• The GDPR has had a more significant effect on organisations that provide public services in industries such as arts, entertainment, retail, education, health, and public administration. An individual person can sue for a GDPR breach. Additionally, supply-chain organisations were more likely to have made changes in terms of enhancing their cyber security capacity and capability as a result of the GDPR.  

There are no signs that cyber security activity will slow down any time soon. Inmnay cases, Data security experts and compliance professionals rely on legacy methods, which aren’t effective in protecting against modern-day attacks. The best way to deal with cyber criminals is to 'think out of the box'. Put simply, organisations need to understand their mindsets and tactics. Companies such as Microsoft and Meta have hired ethical hackers and specialist Pen Testing firms.  Regardless of how competent the IT department is, it can’t do everything itself. 

Predictive Analytics

Predictive analysis is gaining momentum in every industry, enabling organisations to streamline the way they do business. Predictive analytics is a key discipline in the field of data analytics, an umbrella term for the use of quantitative methods and expert knowledge to derive meaning from data and answer fundamental questions about a business, the weather, healthcare, scientific research and other areas of inquiry. In the context of businesses, the main focus here, that process is often referred to as business analytics.

Predictive analytics tells them where threat actors tried to attack in the past, so it helps to see where they’ll strike next. 

The conventional approach to fighting cybercrime is collecting data about malware, data breaches, phishing campaigns, and so on. Relevant information is extracted from those signatures. By signatures, it’s meant a one-of-a-kind arrangement of information that can be used to identify a cyber criminal’s attempt to exploit an operating system or an app’s vulnerability. 

Even the most experienced cyber professionals agree that it’s impossible to prevent every data breach. It’s not possible to stop determined attackers from getting into systems, and that’s because they’re too sophisticated. 
The sooner organisational leaders can accept this reality, the better.

It’s best to assume that data breaches are unavoidable and set up cyber defences to minimise damage. Preparing for the inevitable through a checklist can help fight the invisible enemy.

More and more companies are investing in machine learning, neural networks, deep learning, and artificial intelligence algorithms. These technologies have brought a great deal of innovation to various industries, such as healthcare, transportation, filmmaking, and many others. They are also improving cybersecurity solutions.
The need for predictive analytics is arguably more critical than it's ever been.

Predictive analytics gives businesses a leg up by looking for meaningful patterns in this cumulative data, then building models that forecast what will likely happen in the future.

Data scientists use predictive models to look for correlations between different data elements in website clickstream data, patient health records and other types of data sets. Once the data collection has occurred, a statistical model is formulated, trained and modified as needed to produce accurate results. The model is then run against the selected data to generate predictions.

A wide range of tools are presently being used in predictive modelling and analytics. AWS, Google, IBM, Microsoft, SAP, SAS Institute and many other software vendors offer predictive analytics tools and related technologies supporting machine learning and deep learning applications.

Cybercrime is more sophisticated than ever before, and organisations have to move faster - they should stop making excuses and ensure that basic cyber security goes up. 

Techtarget:    FastCompany:     Techcrunch:     FieldEffect:    CaptechU:  Business Cloud:    Analytics Insight: 

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