Big data implementations are complex, multi-level stacks, encapsulating some of an organization’s most important and sensitive data. As such, when these deployments go into production, they create a high-risk asset. And herein lies the challenge for IT organizations: securing access to big data while still providing end user access for extracting valuable business insights.
Here are three big data security risks and a simple approach to mitigating them.
Unfettered access to big data puts sensitive and valuable data at risk of loss and theft. IT organizations need centralized control over who can access big data, how, and when. Only users with a business need should have access to big data. Least privilege access, or giving users only the privileges they need to perform their jobs, should be standard.
Over-privileged accounts increase the risk of insider threats, and big data is no different. Administrators should not have full access to Hadoop clusters and all their data. Instead, as with least privilege access, administrator access should be limited to the specific actions and commands required to do the job. This means enforcing a narrower set of access and privilege rights than the local root account allows.
Lack of visibility into what’s happening across the Hadoop cluster creates a number of challenges for IT organizations. Without session recording, it becomes nearly impossible to identify, mitigate, and remediate potential security issues.
And without auditing capabilities, IT organizations have a difficult time proving compliance with regulatory and standards requirements. Given that 83% of big data implementations must meet some level of compliance, it’s imperative that IT organizations implement auditing capabilities.