What is Big Data Security?

Big data security includes the tools, policies, and procedures necessary to protect sensitive data—such as personally identifiable information (PII) and intellectual property (IP)—at scale for security and privacy compliance that helps reduce the risk of a security breach.

What do I need to know about big data security?

Successful digital transformation depends on agile, trustworthy, reliable data—and today’s data-driven organizations demand that sensitive data be protected against improper exposure that could result in theft or misuse. The consequences of failing to meet big data security challenges range from losing customer trust to racking up hefty penalties for compliance violations.

What are some big data security challenges?

With increasingly vast amounts of data to secure, companies often aren't sure where to begin when addressing big data security issues. Top concerns include:

Without a comprehensive, integrated data management strategy, organizations may try to resolve these challenges piece by piece. However, the resulting patchwork of big data security tools and technologies leaves potential gaps in controls with a low trust assurance. The piecemeal approach also increases IT complexity when it requires disruptive, costly customization to integrate consistently across today’s wide variety of data stores, applications, and multi-cloud environments.

How should big data security be implemented?

Organizations need a big data security solution that enable them to discover, classify, and map data across a wide range of data repositories and data types. This will give them the necessary insight to analyze security risks and prioritize the remediation efforts that will have the greatest business impact on data security and privacy compliance throughout the organization.

5 key considerations for implementing big data security

  • Make ease of scalability a priority. Big data is only getting bigger. An effective, future-proof big data security solution must be able to scale both for data growth and for new types of sensitive data in need of protection.
  • Don't try to force-fit legacy security techniques. Big data platforms are complex and often designed to meet modern needs, such as data-intensive analytics. Traditional data-at-rest security tools, even those that claim to protect big data, may not offer the flexibility you need. Minimizing security gaps demands a big data security solution that is data-centric and platform-agnostic and deployed consistently across diverse IT architecture, DevOps workloads, and legacy infrastructure to meet security and privacy compliance requirements.
  • Incorporate artificial intelligence. AI can automate the cumbersome manual process of discovering, classifying, and analyzing the risk exposure of sensitive data. As the algorithm learns, it will become faster and more effective at suggesting remediation for compliance issues with appropriate controls that align to security policies. IT teams are often asked to do more with less—AI can effectively accelerate your efforts with less cost and resources required.
  • Design for the cloud. The cloud is an ideal platform for big data, as cloud services are uniquely elastic for scaling to process and store large amounts of streaming data from the Internet of Things (IoT) and other sources. A big data security solution must therefore be able to easily ingest and protect big data in the cloud, both in storage and in transit to and from the cloud and on-premise systems.
  • Adopt an integrated solution to simplify management. Your data governance strategy demands an approach that streamlines controls from data discovery to long-term data retention, and everything in between. As your organization grows, an integrated solution provides consistency and reliability for big data security and privacy controls—helping to lower the cost of ownership and simplify security audits.

How do big data security and privacy practices tie into broader business objectives around governance, risk, and compliance?

Technologies, policies, and procedures that help companies manage and control their data can also help define big data security best practices, such as which data protection methods to use and how to apply access controls more effectively. Data governance solutions are a natural fit with big data security objectives: they help with tasks such as automating business rules and validating data quality, and they provide tools to support complex use cases for securing personal data. They also help address data privacy issues by delivering actionable intelligence about risk and privacy compliance—a task that’s accelerated when you can leverage AI and machine learning to streamline and automate the process. Cloud migration initiatives for critical business applications also require big data security tools that cover systems and data sources both on-premises and in the cloud.