AWS Cloud Cover

Jun 28 2019   6:21PM GMT

AWS month in review: Security Hub goes live at AWS security conference

Ryan Dowd Profile: Ryan Dowd

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"Amazon Web Services"
Amazon
Cloud Security

AWS this month hosted its inaugural re:Inforce conference in Boston and used the setting to make AWS Security Hub and Control Tower generally available and to introduce a VPC network security feature.

Other AWS developments of note earlier in June included AWS’ expansion of Auto Scaling to Amazon Relational Database Service (RDS), which should ease over-provisioning woes for some users, and the addition of AWS Personalize to AWS’ machine learning suite.

AWS re:Inforce, an AWS re:Invent-inspired spinoff devoted to cloud security, drew more than 8,000 attendees. The AWS security conference featured Amazon cloud service demos, training sessions and highlighted Security Hub and Control Tower, among other services, as ways to infuse more automation and visibility into cloud security processes.

Security Hub and Control Tower aim to centralize security insights and account management, respectively. Security Hub is a centralized security dashboard to monitor security and compliance posture. It collects and analyzes data from all the AWS security tools and resources you use and checks them against AWS security and compliance best practices – identifying an S3 bucket unintentionally left open to public access, for example.

AWS Control Tower was built to ease multi-account management. Control Tower automates the creation of a secure multi-account AWS environment, with AWS security best practices baked into the process. Accounts configured through Control Tower come with guardrails — high-level policies — that reject or report prohibited deployments.

Amazon Virtual Private Cloud (VPC) Traffic Mirroring is a feature for your Amazon VPC that analyzes network traffic at scale. AWS has described this capability as a “virtual fiber tap” that captures traffic flowing through your VPC. You can capture all the traffic or filter for specific network packets. VPC Traffic Mirroring should improve network visibility and help organizations check off monitoring compliance requirements.

Amazon RDS supports Auto Scaling

Auto Scaling uses Amazon CloudWatch to monitor applications and then automatically scale them according to predetermined resource needs and parameters. Users can now set up Auto Scaling for RDS in the Management Console.

Before Auto Scaling, RDS users either overprovisioned new database instances to be safe or underprovisioned them to save some money. This meant they were either stuck footing a larger bill than necessary, or had to increase capacity on the fly which typically results in application downtime. To ensure RDS performance and cost optimization, users should underprovision from expected capacity and set a maximum storage limit. Auto Scaling will boost capacity as database workloads grow.

Auto Scaling is a key feature for EC2 and Amazon Aurora, as well. Those services enable dynamic scaling — up or down — based on user recommendations for performance and cost optimization. However, RDS Auto Scaling only scales up.

Users who experience cyclical data spikes and lulls may need to use Aurora Serverless or provide additional automation on top of RDS Auto Scaling to bring their storage capacity back down. However, RDS Auto Scaling should still simplify provisioning of storage capacity in most cases.

Users pay for the database resources they use, which includes Amazon CloudWatch monitoring.

Amazon adds Personalize to ML portfolio

Like Amazon SageMaker, Amazon Personalize doesn’t require advanced ML and AI knowledge. The service stems from the machine learning models that Amazon.com uses to recommend products and offers that capability in a plug-and-play fashion to AWS users and their applications.

To get started with Amazon Personalize, users can set up an application activity stream on the Amazon Personalize API. This stream would log customer interaction on the application — along with products they’d like to recommend. Amazon Personalize will then customize a machine learning model for that data and generate real-time recommendations. AWS users can start with a two-month free trial, with data processing and storage limitations.

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