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6 Tips for Big Data Security Strategies

The necessary IT business transformation is a challenge for the IT department. From the virtualization of the workstation to the storage of the Big Data, the problems arise, and are grouped around the same concern – the data protection and data security.

Often compared to the “new black gold”, the data have become a major economic asset. In 2011, it took two days to generate five exabytes; whereas in 2013, this will take only 10 minutes. Faced with such an explosion of information, how does Big Data changes corporate security systems?

The major impact of Big Data on the security industry and the advent of intelligent security models

Raising shields to protect the company’s computer borders is only possible if these boundaries are known and under control. With the advent of mobility, organizations evolve, and have entered the era of hyper-connectivity, making traditional perimeter defense strategies obsolete.

To address increasingly dynamic threats, security must be agile. This is where the Big Data technologies in a safe angle come in, thanks to the real-time analysis of large volumes of data and security operations. Based on internal and external security information, CIOs can have a complete mapping of the risks and threats to their network in order to implement appropriate solutions. This context-based approach helps companies better assess risks and prevent, and even anticipate, future attacks and threats.

In the longer term, the security association and Big Data will change the nature of traditional defense mechanisms. Within three to five years, the evolution of data analysis tools will take the form of advanced predictive functions and automated real-time controls.

6 tracks to support the safe transformation towards a Big Data Analysis

Companies need to plan for migration to an intelligent, Big Data-oriented security model of their defense activities and mechanisms. Here are some tips to help them:

  1. Define a global cyber security strategy and sensitize all teams to implement the agreed program, specifically adapted to the risks, threats and expectations of the company.
  2. Establishing a common repository for all safety data: Since Big Data analysis involves gathering information from multiple sources and a large number of formats, it seems appropriate to adopt a single reference framework for the acquisition of data. Information, indexing, standardization, analysis and sharing.
  3. Leave the isolated products to unified security architecture: Identify the security products you plan to use over the next few years; In fact, each of these products has its own data structure that must be integrated into the same analytical framework dedicated to security.
  4. Focus on open, scalable, ready-to-use security tools: Invest predominantly in security solutions based on agile analytic technologies, not static solutions (signature recognition) that are limited to the perimeter of the network. Look for tools that are ready for Big Data, architecturally flexible enough to adapt to changing business, IT or threats.
  5. Strengthen the skills of security analysts: If the new enterprise protection solutions are ready for Big Data, the security teams are not necessarily so. Specialist security analysts are rare and will remain so for a long time. Many companies will therefore have to solicit external partners in support of their internal analytical capabilities.
  6. Provide services from external sources of intelligence and threat information: In addition to internal security analysis programs, solicit external services and obtain information from sources of trust.

Integrating Big Data with security practices promises greater visibility into IT environments, the ability to distinguish between normal activities and suspicious activities to maintain confidence in IT systems, and significantly more effective defense and response capabilities.

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