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Big data analytics can help reduce cyber security risks

Big data analytics has the potential to reduce the growing number of cyber security risks and increase business agility, according to global independent information security body, Information Security Forum (ISF). The findings are revealed in a new ISF report called ‘Data Analytics for Information Security – From hindsight to insight.’

According to the report, with data volumes growing at an alarming rate – around 2.5 million terabytes every day – the importance of big data analytics has never been greater. With its ability to comprehensively 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 can lead to improved information security, greater organizational agility and better cyber resilience.

Yet despite the huge potential for big data analytics in information security, it is still immature and massively under-used. Only half of organizations surveyed by the ISF are using some form of analytics for fraud prevention, forensics and network traffic analysis, while less than 20 percent are using it to identify information related to subject matter requests, predict hardware failures, ensure data integrity or check data classification.

“Few organizations currently recognise the benefits for information security, yet many are already using data analytics to support their core business,” according to Michael de Crespigny, CEO at ISF. “With the speed and complexity of the threat landscape constantly evolving and the prevalence of combined threats, organizations need to start moving away from being retrospective and reactive to being proactive and preventative.”

Rather than drowning in data, the ISF believes that information security functions can gain a holistic, 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.

De Crespigny adds: “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.”

More details.

•Date: 31st July 2012 • World •Type: Article • Topic: ISM

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