As organizations increase their AWS footprint, the concern about the security of sensitive data stored in AWS S3 storage and accessed from outside the organization also increases. Several of the most well-known data breaches have involved misconfigured and unsecured Amazon Simple Storage (S3) buckets that resulted in exposed millions of customer data records
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As data and analytics environments become increasingly complex, organizations can no longer afford to perform many operations manually. According to TDWI research, automation (in general) is one of the top three priorities for analytics. We see automation occurring throughout the data and analytics life cycle. Automation increasingly leverages embedded AI/ML algorithms (i.e., infused in the software) to help perform tasks such as profiling and cleansing data, identifying sensitive data, data mapping, surfacing insights, or building machine learning models.
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brighttalk
Cloud architecture, strategy, and implementation are all impacted by security and compliance guidelines and postures. What are the best practices for Cloud approach? How does it impact your security, cost, and other frameworks within IT.
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Are you currently automating your Infrastructure as Code/Terraform deployments, or are you investigating that ability now? One key piece of being successful is making sure you don't overrun the budget when you open up the ability to deploy to teams.
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