Information Security Industry is changing its shape and form very rapidly with growing demand and adoption of industry changing innovations, like cloud and mobile computing.

Alongside with advancing of the tools that companies are operating with, the attacks and attackers themselves are getting more cunning and sophisticated.

Keeping up the speed and scale with evolving threats is one of the biggest challenges of the Cyber Security industry.

This forces organizations to move beyond their standard responses and advance their PRD structure:

Mostly used tools by businesses are security information and event management (SIEM) solutions that collect, store and analyze terabytes of security data across the whole organisation in real time.

The goal and challenge of using Big Data Analytics is to explore all the relevant pieces within the data, which can be hidden patterns, trends, correlations, potential risks and user behavior tendencies and preferences. By determining those company can gain competitive advantage, decrease inefficiency and increase operational productivity. The market of platforms is now getting filled by platforms that utilize the advanced power of language processing, statistics, Machine Learning, Deep Learning and Artificial Intelligence.

Big Data Analytics can and have to be introduced and heavily utilized at each stage of the PRD structure. Integration of Advanced Analytics in order to minimize cyber security challenges within the enterprise can be achieved through the Adoption of Platforms driven by Advanced Analytics tools.

The tools used are capable of triggering and alerting on events that go beyond human abilities and human eye. The gap between the events that can be analysed by a human and can be detected by complex proactive identification is easily fulfilled by Advanced platforms.

By being able to ingest enormous volumes of historical data, convert it into a smart and structured context and derive meaningful and deep insights the platform can build a baseline of what’s deemed as normal and abnormal behavior for all the users and entities.

Analytics of the constantly establishing baseline can be used to predict where the next security challenge could be coming from, thus taking the prevention stage to the next level.

Whenever users and/or assets are not following their normal or abnormal behavior the alerting could be introduced to improve the detection stage of PRD. Potential penalties and rehabilitation measures should be determined in advance for those potential challenges to assist the response phase.

Monitoring and Automation of the workflows can make the use of Big Data Analytics even more efficient and response stage even more agile, reliable and accurate within the organisation. Security automation aims to reduce risks and operational errors coming from misuse of data or time-related issues when dealing with Cyber Security Threats. When platforms are learning and building behavioral analytics from the historical and live-streaming data coming into the platform from organization’s environment, the automated processes are constantly in a refinement mode learning not only from the live data, but from the processes itself.

The advanced analytics, automation and predictive analysis together in an organisation’s environment will predict all potential cyber threats, breaches and attacks and prevent it before they even happen, and before any individual would be able to notice it.

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