Big data analytics can reduce cyber risks, says ISF

Big data analytics has the potential to reduce the growing number of cyber security risks and increase business agility, says the ISF.

Big data analytics has the potential to reduce the growing number of cyber security risks and increase business agility, says a report by the Information Security Forum (ISF). 

According to the Data Analytics for Information Security report, the importance of big data analytics has never been greater, with data volumes growing at around 2.5 million terabytes a day.

ISF researchers believe the ability to analyse large volumes of disparate and complex data, such as threats, risks and incidents, data analytics can help senior and board level executives better understand and manage their risk/reward balance in cyberspace.

Such insight, they said, can lead to improved information security, greater organisational agility and better cyber resilience.

However, the researchers found that despite the huge potential for big data analytics in information security, it is still immature and under-used.

Only half of organisations surveyed by the ISF are using some form of analytics for fraud prevention, forensics and network traffic analysis, while less than 20% are using it to identify information related to subject matter requests, predict hardware failures, ensure data integrity or check data classification.

“Few organisations currently recognise the benefits for information security, yet many are already using data analytics to support their core business,” said ISF chief Michael de Crespigny.

“With the speed and complexity of the threat landscape constantly evolving and the prevalence of combined threats, organisations need to start moving away from being retrospective and reactive to being proactive and preventative," he said.

Rather than drowning in data, the ISF believes that information security functions can gain a comprehensive, in-depth view of risks, both internal and external, and also tap into existing analytical capabilities like fraud detection and anti-money laundering in financial services and customer data analysis in retail.

“We recognise the inherent challenges of analysing big data – the huge data sets and the need for high performance computing and specialised tools – plus the really valuable insights are often buried in large volumes of results.

"But we also believe it’s manageable and that there are tools, solutions and services out there designed to help meet these challenges and enable businesses to see results very quickly,” said De Crespigny.

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