Data Classification and Compliance Needs Analysis

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Data Classification and Compliance Needs Analysis 2016-12-05T10:57:48+00:00

netlogx believes that there are 4 major reasons the enterprises should pursue Data Classification and Compliance needs analysis:

  • To ensure that data that it can be found quickly
  • To drive de-duplication of data.  This leads to savings in storage as well as speeding search and retrieval.  It also reduces the opportunities for data breaches
  • To meet legal and regulatory requirements for retrieving specific information within a set timeframe and responding to potential or actual data breaches
  • To drive appropriate security control selection based on what needs to be protected

netlogx has developed  a practical and pragmatic approach to data classification that focuses on the information that is sensitive and covered by legal and regulatory requirements.   Data is classified and evaluated with regards its confidentiality, integrity and availability requirements.  Only data of a sensitive nature is tagged.  This significantly reduces the cost of the process.

The process is as follows:

  • Identification of critical processes and data
  • Identify data custodians and owners
  • Identify security and operational requirements
  • Establish data classification scheme
  • Identify appropriate standards and existing internal controls
  • Conduct data audit and cleansing project as required
  • Document and report on data classification and compliance requirements
  • Prepare and implement appropriate controls
  • Prepare and implement appropriate data reporting processes
  • Monitor and maintain the data classification system and adjust as required


Organizations that embrace data classification and compliance analysis are far less likely to be compromised and if they are they are able to develop plans that are proactive and drive rapid remediation of data breaches.  These organizations are also able to significantly reduce the cost of Information Management, especially as it relates to storage costs and data reuse.