A data pipeline is a streamlined and automated way to handle ingesting, processing, translating, and storing your data. When implemented intelligently on the cloud, a data pipeline architecture can help you use your data to best effect, answering many questions about your customers and launching machine learning projects that integrate into your overall business strategy.

As data comes from a wide variety of sources and includes both structured and unstructured formats, data from IoT devices, multiple formats, different APIs, and technologies, your data pipeline becomes complex, distributed with blind spots.

data pipeline security

In today’s complex data pipelines architectures, DevSecOps and DataSecOps teams are challenging to continuously discover risks and fix them before they involve into threat.

Enterprise Data Pipeline Security Challenges

1. You’re working with a growing number of data sources and data stores

According to a new Market Pulse survey, companies are drawing from an average of over 400 different data sources to feed their data ecosystem, including BI, analytics, and operations. Data sources can be generated from functions created by the organization like mobile application, web, sheet, and human-to-human interaction and from external data sources like RSS, API, or datasets

From these data sources, data ingest into also growing of datastores:

  • Structured (SQL) and unstructured (MongoDB)
  • Managed (RDS) and unmanaged (Postgres on top of EC2)
  • Elastic scale (Snowflake) and manual scale (VM)
Getting visibility, managing policies, setting security controls, and monitoring activities are challenging in growing environments. Your security team backlog is growing faster than the number of tasks they can accomplish at a certain point in time.

2. Each data consumer in your organization has its agenda

Once data is stored in your environment cloud or on-prem, consumers need to fetch internal or external data to achieve their business goals. As different data consumers have different business goals, also their usage patterns should be different:

  • Data analyst
  • Data scientist
  • Software Developer
  • DevOps
  • Application
  • Third-party
  • Security / Privacy Auditor
  • Monitoring Tools

3. The wrong security controls might impact your business goals

Everyone understands that security is not “fully seamless”; however, most of the security vendors promise “few clicks”, “5min”, “non-intrusive”, “agentless”, and other buzz words which misleading their potential customers. In reality, security experts are tired of reading those empty promises and clearly understand that there is an impact on the performance, latency, accuracy, and costs.

The impact might be minimal if the security controls you are going to implement fit your goals, but there might be a destructive effect in some cases. For example, redirecting a connection between an application and a 1M hourly transaction via a proxy, aka “universal data access” could dramatically impact your costs or slow down the roundtrip between the application and the database.

Proactively find and fix weaknesses across your SaaS platforms, and maintain continuous security for all global settings and user privileges. Deliver step-by-step remediation plans for every issue to exactly who needs them. Easily track progress end-to-end and verify everything gets fixed.

universal data access

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