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Key Performance Indicators for Achieving Data Security

Key Performance Indicators for Achieving Data Security

At the beginning of the year, predications are made regarding the issues of data security in the year ahead.  While certain predications come to light, other predications made by publications and articles will fall short.  Therefore, the first quarter predications must be measured as the year progresses.  The predications regarding data security performance and challenges in the year ahead, whether right or wrong, still focus on relevant issues in data security.

Predications may not offer the best sense of direction for the year ahead, in terms of improving data security.  However, a narrower focus on specific aspects of data security can help improve the performance of data security in the year ahead.  To achieve added performance in data security, the key performance indicators can be assessed.  The performance indicators reflect the environment of data security, including current legislation, industry strengths and weaknesses, as well as opportunities for data security to improve.

The three current performance indicators discussed below focus on how organizations can achieve their data security performance goals.

  • Sensitive data location and risk. Organizations deal with sensitive data, which could be compromised if not secured properly. To ensure the sensitive data is secured, organizations must have a current knowledge of what data is sensitive and where the data is located.  Though knowing the location of sensitive data is essential to data security, research has shown less than 12 percent of organizations are keeping track of the location of their sensitive data. Therefore, organizations in the upcoming year could benefit from improving the inventory of their sensitive data.  To increase their performance level for sensitive data, organizations must take more actions to set a schedule to monitor sensitive data.  However, more than half of organizations fail to acknowledge how much data they have or fail to anticipate how much data they will consume in the upcoming year.  Improve measures to monitor sensitive data will help organizations in the year ahead achieve their data security performance goals.
  • General Data Protection Regulation (GDPR) risk. Regulations are an essential element of data security to focus on in the upcoming year. Organizations should consider how they are meeting the standards of their industries and ensuring their data is within the established regulations.  Gaps in achieving data security regulations could be either an opportunity or a threat to an organization.  To utilize the gaps as an opportunity, organizations can evaluate the factors related to GDPR and assess how they can manage risks.  The threat of the gaps will come if organizations fail to meet regulations.  Failing to meet the standards of the regulations will increase the risks related to the GDPR, including protection, costs, access, and other data challenges.  Reducing the risks through GDPR policies will improve the organizations performance for data security.  Another reason to focus on regulation is because past issues, such as the HIPAA regulations, have resulted in organizations being heavily fined for failing to meet standards.  As the U.S. government continues to enforce regulations on data, organizations should be focused on efforts to implement policies regarding data security in the year ahead.
  • Identify and protect. As data security threats persist, organizations will need to determine how to identify risks and protect data. To ensure data is secured, automated data security measures could be set in place to identify threats and protect data. Part of identifying risks and protecting data links back to understanding the organization’s sensitive data. With a proper foundation of understanding sensitive data, an organization can make the most effective decisions on how to protect the data.  The third performance goal for data security therefore deals with establishing the foundation to ensure the organization can identify and protect sensitive data.  Even though the other two performance factors are considered in the third goal, more detail must be considered to understand how to implement an automated system to identify risks and protect sensitive data.
    • Understanding the organizations sensitive data: Sensitive data deals with classifications, discovery, analysis, access, and related activities. With the traits related to data established, an automated system could arrange the sensitive data to be managed more effectively.  The arrangement links back to the first data security performance goal of sensitive data and risk, which indicates the criticalness of the goal to build on other goals.
    • Monitor risk continuously: Once sensitive data is arranged in the automated system, the data must be continually monitored. By monitoring the sensitive data, risks can be identified and evaluated. Risks with more serious threats may be treated with urgency, while risks with less impact may be identified as a less serious threat.  Without a monitoring system, risks may not be identified, which would leave data vulnerable. Therefore, organizations should ensure policies and procedures are established to monitor data continuously to reduce the threat of risks.
    • Uncover the unexpected: Risks may not be identifiable, so organizations should prepare themselves for the unexpected. To detect unexcepted risks, organizations may choose to monitor access to data or establish alerts to warn against significant unexpected risks.
    • Remediate risk: Data security should be a focal point for organizations, therefore the organization can reduce the threats related to risks. Otherwise, breaches of security or unauthorized access to sensitive data may not be protected.

In summary, the three data security performance goals highlight themes of data security in organizations today.

  • Data growth. Modern data growth is expanding through the innovative services, such as the cloud and other digital data inventions. Therefore, more data is consumed by organizations for them to monitor and protect.  Other factors of data growth include the ability to share and store data in new ways.  Implementing innovative ways to expand data could help organizations realize new opportunities.
  • Compliance. As an organization’s data inventory grows, the organization will need to continually review and update their policies.  Updated policies can help organizations follow GDPR regulations.  Organizations with a focus on compliance accomplishes two major feats, including protecting their data and avoiding government fines.  Understanding and complying with the data security regulations can help an organization gain essential knowledge on how to handle data.  Furthermore, outdated measures to ensure compliance may put an organization at risk of unwanted penalties.
  • Traditional security. Evidence of data security challenges are prevalent today. To protect data from breaches and unauthorized access, organizations must consider their security measures.  The identify and protect goal could help organizations reduce the threats created by data security risks.  One way to further secure data is to understand the sensitive data and make more policies to better protect this data.  For example, regulated data, such as health and credit card data, may need more access restrictions to prevent unauthorized parties from accessing the information.  With traditional data security measures, the sensitive data may not be fully protected from data breaches.  Since more attacks occur on data, the traditional methods should be replaced by new procedures. Still, data is under continual risks, and organizations should take threats to data security seriously to prevent breaches from destroying valuable data.