AWS Blog

Our customers use Cost Explorer to better understand and manage their AWS spending, making heavy use of the reporting, analytics, and visualization tools that it provides. We launched Cost Explorer in 2014 with a focus on simplicity – single click signup, preconfigured default views, and a clean user interface (take a look back at The New AWS Cost Explorer to see where we started). The Cost Explorer has been very popular and we’ve received a lot of great feedback from our customers.

Last week we launched a major upgrade to Cost Explorer. We’ve redesigned the user interface to optimize many common workflows including filtering, report management, selection of date ranges, and grouping of data. We have also included some default reports to make it easier for you to explore the costs related to your use of Reserved Instances.

Looking at Cost Explorer
Since pictures are reportedly worth 1000 words, let’s take a closer look! Cost Explorer is part of the Billing Dashboard so I can start there:

Here’s the Billing Dashboard. I click on Cost Explorer to move ahead:

I can open up Cost Explorer or access one of three preconfigured views. I’ll go for the first option:

The default report shows my EC2 costs and usage (running hours) for the past 3 months:

I can use the Group By menu to break the costs down by EC2 instance type:

I have many other grouping options:

The filtering options are now easier to access and to edit. Here’s the full set:

I can explore my EC2 costs in any set of desired regions:

I can filter and then group by instance type to see how my spending breaks down:

I can click on Download CSV and then process the data locally:

I can also exclude certain instance types from the report. Here’s how I exclude my m4.xlarge, t2.micro, and t2.nano usage:

Report Management
Cost Explorer allows me to customize my existing reports and to create new reports from scratch. I can click on Save As to save my customized report with a new name:

I can see and manage all of my reports on the Saved Reports page (The padlock denotes a default report that cannot be edited and then overwritten):

When I click on New report I can start from a template:

After I click on Create Report, I set up my date range and filters as desired, and click on Save As. I created a report that displays my year-to-date usage of several AWS database services (Amazon Redshift, DynamoDB Accelerator (DAX), Amazon Relational Database Service (RDS), and AWS Database Migration Service):

All of my reports are accessible from the Reports menu so I can check on my costs with a click:

We also simplified the process of selecting a range of dates for a report, including options to select common date ranges:

Reserved Instance Reports
Cost Explorer also includes a pair of reports that will help you to understand and optimize your usage of Reserved Instances. I don’t own an RI’s so I used screen shots supplied by the team.

The RI Utilization report allows you to see how much of your purchased RI capacity is being put to use (the dashed red line represents a utilization target that you can specify):

The RI Coverage report tells you how much of your EC2 usage is being handled by Reserved Instances (this time, the dashed red line represents the desired amount of coverage):

I hope you have enjoyed this tour of the updated Cost Explorer. It is available now and you can start using it today!

— Jeff;

I’ve been earning a living in the technology industry since 1977, when I worked in one of the first computer stores in the country as a teenager. Looking back over the past 40 years, and realizing that the Altair, IMSAI, Sol-20, and North Star Horizon machines that I learned about, built, debugged, programmed, sold, and supported can now be seen in museums (Seattle’s own Living Computer Museum is one of the best), helps me to appreciate that the world I live in changes quickly, and to understand that I need to do the same. This applies to technology, to people, and to attitudes.

I lived in a suburb of Boston in my early teens. At that time, diversity meant that one person in my public school had come all the way from (gasp) England a few years earlier. When I went to college I began to meet people from other countries and continents and to appreciate the fresh vantage points and approaches that they brought to the workplace and to the problems that we tackled together.

Back in those days, there were virtually no women working as software engineers, managers, or entrepreneurs. Although the computer store was owned by a couple and the wife did all of the management, this was the exception rather than the rule at that time, and for too many years after that. Today, I am happy to be part of a team that brings together the most capable people, regardless of their gender, race, background, or anything other than their ability to do a kick-ass job (Ana, Tara, Randall, Tina, Devin, and Sara, I’m talking about all of you).

We want to do all that we can to encourage young women to prepare to become the next generation of engineers, managers, and entrepreneurs. AWS is proud to support Girls Who Code (including the Summer Immersion Program), Girls in Tech, and other organizations supporting women and underrepresented communities in tech. I sincerely believe that these organizations will be able to move the needle in the right direction. However, like any large-scale social change, this is going to take some time with results visible in years and decades, and only with support & participation from those of us already in the industry.

In conjunction with me&Eve, we were able to speak with some of the attendees at the most recent Girls in Tech Catalyst conference (that’s our booth in the picture). Click through to see what the attendees had to say:

• Sandy Carter – Amazon VP and President of the Board for Girls in Tech.
• Adriana Gascoigne – CEO of Girls in Tech.
• Kym McNicholas – Competition Director for the Extreme Tech Challenge.
• Christine Adair – Security Manager, AWS.
• Matt White – Senior Human Resources Leader, AWS.
• Aaron Levie – Co-founder and CEO, Box.

I’m happy to be part of an organization that supports such a worthwhile cause, and that challenges us to make our organization ever-more diverse. While reviewing this post with my colleagues I learned about We Power Tech, an AWS program designed to build skills and foster community and to provide access to Amazon executives who are qualified to speak about the program and about diversity. In conjunction with our friends at Accenture, we have assembled a strong Diversity at re:Invent program.

— Jeff;

A couple of months ago on the blog, I announced the AWS Chatbot Challenge in conjunction with Slack. The AWS Chatbot Challenge was an opportunity to build a unique chatbot that helped to solve a problem or that would add value for its prospective users. The mission was to build a conversational, natural language chatbot using Amazon Lex and leverage Lex’s integration with AWS Lambda to execute logic or data processing on the backend.

I know that you all have been anxiously waiting to hear announcements of who were the winners of the AWS Chatbot Challenge as much as I was. Well wait no longer, the winners of the AWS Chatbot Challenge have been decided.

May I have the Envelope Please? (The Trumpets sound)

The winners of the AWS Chatbot Challenge are:

• First Place: BuildFax Counts by Joe Emison
• Second Place: Hubsy by Andrew Riess, Andrew Puch, and John Wetzel
• Third Place: PFMBot by Benny Leong and his team from MoneyLion.
• Large Organization Winner: ADP Payroll Innovation Bot by Eric Liu, Jiaxing Yan, and Fan Yang

Diving into the Winning Chatbot Projects

Let’s take a walkthrough of the details for each of the winning projects to get a view of what made these chatbots distinctive, as well as, learn more about the technologies used to implement the chatbot solution.

BuildFax Counts by Joe Emison

The BuildFax Counts bot was created as a real solution for the BuildFax company to decrease the amount the time that sales and marketing teams can get answers on permits or properties with permits meet certain criteria.

BuildFax, a company co-founded by bot developer Joe Emison, has the only national database of building permits, which updates data from approximately half of the United States on a monthly basis. In order to accommodate the many requests that come in from the sales and marketing team regarding permit information, BuildFax has a technical sales support team that fulfills these requests sent to a ticketing system by manually writing SQL queries that run across the shards of the BuildFax databases. Since there are a large number of requests received by the internal sales support team and due to the manual nature of setting up the queries, it may take several days for getting the sales and marketing teams to receive an answer.

The BuildFax Counts chatbot solves this problem by taking the permit inquiry that would normally be sent into a ticket from the sales and marketing team, as input from Slack to the chatbot. Once the inquiry is submitted into Slack, a query executes and the inquiry results are returned immediately.

Joe built this solution by first creating a nightly export of the data in their BuildFax MySQL RDS database to CSV files that are stored in Amazon S3. From the exported CSV files, an Amazon Athena table was created in order to run quick and efficient queries on the data. He then used Amazon Lex to create a bot to handle the common questions and criteria that may be asked by the sales and marketing teams when seeking data from the BuildFax database by modeling the language used from the BuildFax ticketing system. He added several different sample utterances and slot types; both custom and Lex provided, in order to correctly parse every question and criteria combination that could be received from an inquiry.  Using Lambda, Joe created a Javascript Lambda function that receives information from the Lex intent and used it to build a SQL statement that runs against the aforementioned Athena database using the AWS SDK for JavaScript in Node.js library to return inquiry count result and SQL statement used.

The BuildFax Counts bot is used today for the BuildFax sales and marketing team to get back data on inquiries immediately that previously took up to a week to receive results.

Not only is BuildFax Counts bot our 1st place winner and wonderful solution, but its creator, Joe Emison, is a great guy.  Joe has opted to donate his prize; the $5,000 cash, the $2,500 in AWS Credits, and one re:Invent ticket to the Black Girls Code organization. I must say, you rock Joe for helping these kids get access and exposure to technology.

Hubsy by Andrew Riess, Andrew Puch, and John Wetzel

Hubsy bot was created to redefine and personalize the way users traditionally manage their HubSpot account. HubSpot is a SaaS system providing marketing, sales, and CRM software. Hubsy allows users of HubSpot to create engagements and log engagements with customers, provide sales teams with deals status, and retrieves client contact information quickly. Hubsy uses Amazon Lex’s conversational interface to execute commands from the HubSpot API so that users can gain insights, store and retrieve data, and manage tasks directly from Facebook, Slack, or Alexa.

In order to implement the Hubsy chatbot, Andrew and the team members used AWS Lambda to create a Lambda function with Node.js to parse the users request and call the HubSpot API, which will fulfill the initial request or return back to the user asking for more information. Terraform was used to automatically setup and update Lambda, CloudWatch logs, as well as, IAM profiles. Amazon Lex was used to build the conversational piece of the bot, which creates the utterances that a person on a sales team would likely say when seeking information from HubSpot. To integrate with Alexa, the Amazon Alexa skill builder was used to create an Alexa skill which was tested on an Echo Dot. Cloudwatch Logs are used to log the Lambda function information to CloudWatch in order to debug different parts of the Lex intents. In order to validate the code before the Terraform deployment, ESLint was additionally used to ensure the code was linted and proper development standards were followed.

PFMBot by Benny Leong and his team from MoneyLion

PFMBot, Personal Finance Management Bot,  is a bot to be used with the MoneyLion finance group which offers customers online financial products; loans, credit monitoring, and free credit score service to improve the financial health of their customers. Once a user signs up an account on the MoneyLion app or website, the user has the option to link their bank accounts with the MoneyLion APIs. Once the bank account is linked to the APIs, the user will be able to login to their MoneyLion account and start having a conversation with the PFMBot based on their bank account information.

The PFMBot UI has a web interface built with using Javascript integration. The chatbot was created using Amazon Lex to build utterances based on the possible inquiries about the user’s MoneyLion bank account. PFMBot uses the Lex built-in AMAZON slots and parsed and converted the values from the built-in slots to pass to AWS Lambda. The AWS Lambda functions interacting with Amazon Lex are Java-based Lambda functions which call the MoneyLion Java-based internal APIs running on Spring Boot. These APIs obtain account data and related bank account information from the MoneyLion MySQL Database.

ADP Payroll Innovation Bot by Eric Liu, Jiaxing Yan, and Fan Yang

ADP PI (Payroll Innovation) bot is designed to help employees of ADP customers easily review their own payroll details and compare different payroll data by just asking the bot for results. The ADP PI Bot additionally offers issue reporting functionality for employees to report payroll issues and aids HR managers in quickly receiving and organizing any reported payroll issues.

The ADP Payroll Innovation bot is an ecosystem for the ADP payroll consisting of two chatbots, which includes ADP PI Bot for external clients (employees and HR managers), and ADP PI DevOps Bot for internal ADP DevOps team.

The architecture for the ADP PI DevOps bot is different architecture from the ADP PI bot shown above as it is deployed internally to ADP. The ADP PI DevOps bot allows input from both Slack and Alexa. When input comes into Slack, Slack sends the request to Lex for it to process the utterance. Lex then calls the Lambda backend, which obtains ADP data sitting in the ADP VPC running within an Amazon VPC. When input comes in from Alexa, a Lambda function is called that also obtains data from the ADP VPC running on AWS.

The architecture for the ADP PI bot consists of users entering in requests and/or entering issues via Slack. When requests/issues are entered via Slack, the Slack APIs communicate via Amazon API Gateway to AWS Lambda. The Lambda function either writes data into one of the Amazon DynamoDB databases for recording issues and/or sending issues or it sends the request to Lex. When sending issues, DynamoDB integrates with Trello to keep HR Managers abreast of the escalated issues. Once the request data is sent from Lambda to Lex, Lex processes the utterance and calls another Lambda function that integrates with the ADP API and it calls ADP data from within the ADP VPC, which runs on Amazon Virtual Private Cloud (VPC).

Python and Node.js were the chosen languages for the development of the bots.

The ADP PI bot ecosystem has the following functional groupings:

Employee Functionality

• Summarize Payrolls
• Compare Payrolls
• Escalate Issues
• Evolve PI Bot

HR Manager Functionality

• Bot Management
• Audit and Feedback

DevOps Functionality

• Reduce call volume in service centers (ADP PI Bot).
• Track issues and generate reports (ADP PI Bot).
• Monitor jobs for various environment (ADP PI DevOps Bot)
• View job dashboards (ADP PI DevOps Bot)
• Query job details (ADP PI DevOps Bot)

Summary

Let’s all wish all the winners of the AWS Chatbot Challenge hearty congratulations on their excellent projects.

You can review more details on the winning projects, as well as, all of the submissions to the AWS Chatbot Challenge at: https://awschatbot2017.devpost.com/submissions. If you are curious on the details of Chatbot challenge contest including resources, rules, prizes, and judges, you can review the original challenge website here:  https://awschatbot2017.devpost.com/.

Hopefully, you are just as inspired as I am to build your own chatbot using Lex and Lambda. For more information, take a look at the Amazon Lex developer guide or the AWS AI blog on Building Better Bots Using Amazon Lex (Part 1)

Chat with you soon!

– Tara

Today we released Dedicated IP Pools for Amazon Simple Email Service (SES). With dedicated IP pools, you can specify which dedicated IP addresses to use for sending different types of email. Dedicated IP pools let you use your SES for different tasks. For instance, you can send transactional emails from one set of IPs and you can send marketing emails from another set of IPs.

If you’re not familiar with Amazon SES these concepts may not make much sense. We haven’t had the chance to cover SES on this blog since 2016, which is a shame, so I want to take a few steps back and talk about the service as a whole and some of the enhancements the team has made over the past year. If you just want the details on this new feature I strongly recommend reading the Amazon Simple Email Service Blog.

What is SES?

So, what is SES? If you’re a customer of Amazon.com you know that we send a lot of emails. Bought something? You get an email. Order shipped? You get an email. Over time, as both email volumes and types increased Amazon.com needed to build an email platform that was flexible, scalable, reliable, and cost-effective. SES is the result of years of Amazon’s own work in dealing with email and maximizing deliverability.

In short: SES gives you the ability to send and receive many types of email with the monitoring and tools to ensure high deliverability.

Sending an email is easy; one simple API call:

import boto3
ses = boto3.client('ses')
ses.send_email(
    Source='randhunt@amazon.com',
    Destination={'ToAddresses': ['randhunt@amazon.com']},
    Message={
        'Subject': {'Data': 'Hello, World!'},
        'Body': {'Text': {'Data': 'Hello, World!'}}
    }
)

Receiving and reacting to emails is easy too. You can set up rulesets that forward received emails to Amazon Simple Storage Service (S3), Amazon Simple Notification Service (SNS), or AWS Lambda – you could even trigger a Amazon Lex bot through Lambda to communicate with your customers over email. SES is a powerful tool for building applications. The image below shows just a fraction of the capabilities:

Deliverability 101

Deliverability is the percentage of your emails that arrive in your recipients’ inboxes. Maintaining deliverability is a shared responsibility between AWS and the customer. AWS takes the fight against spam very seriously and works hard to make sure services aren’t abused. To learn more about deliverability I recommend the deliverability docs. For now, understand that deliverability is an important aspect of email campaigns and SES has many tools that enable a customer to manage their deliverability.

Dedicated IPs and Dedicated IP pools

When you’re starting out with SES your emails are sent through a shared IP. That IP is responsible for sending mail on behalf of many customers and AWS works to maintain appropriate volume and deliverability on each of those IPs. However, when you reach a sufficient volume shared IPs may not be the right solution.

By creating a dedicated IP you’re able to fully control the reputations of those IPs. This makes it vastly easier to troubleshoot any deliverability or reputation issues. It’s also useful for many email certification programs which require a dedicated IP as a commitment to maintaining your email reputation. Using the shared IPs of the Amazon SES service is still the right move for many customers but if you have sustained daily sending volume greater than hundreds of thousands of emails per day you might want to consider a dedicated IP. One caveat to be aware of: if you’re not sending a sufficient volume of email with a consistent pattern a dedicated IP can actually hurt your reputation. Dedicated IPs are $24.95 per address per month at the time of this writing – but you can find out more at the pricing page.

Before you can use a Dedicated IP you need to “warm” it. You do this by gradually increasing the volume of emails you send through a new address. Each IP needs time to build a positive reputation. In March of this year SES released the ability to automatically warm your IPs over the course of 45 days. This feature is on by default for all new dedicated IPs.

Customers who send high volumes of email will typically have multiple dedicated IPs. Today the SES team released dedicated IP pools to make managing those IPs easier. Now when you send email you can specify a configuration set which will route your email to an IP in a pool based on the pool’s association with that configuration set.

One of the other major benefits of this feature is that it allows customers who previously split their email sending across several AWS accounts (to manage their reputation for different types of email) to consolidate into a single account.

You can read the documentation and blog for more info.

Welcome to mid-August, everyone–the season of beach days, family road trips, and an inbox full of “out of office” emails from your coworkers. Just in case spending time indoors has you feeling a bit blue, we’ve got a piping hot batch of AWS Online Tech Talks for you to check out. Kick up your feet, grab a glass of ice cold lemonade, and dive into our latest Tech Talks on Compute and DevOps.

August 2017 – Schedule

Noted below are the upcoming scheduled live, online technical sessions being held during the month of August. Make sure to register ahead of time so you won’t miss out on these free talks conducted by AWS subject matter experts.

Webinars featured this month are:

Thursday, August 17 – Compute

9:00 – 9:40 AM PDT: Deep Dive on Lambda@Edge.

Monday, August 28 – DevOps

10:30 – 11:10 AM PDT: Building a Python Serverless Applications with AWS Chalice.

12:00 – 12:40 PM PDT: How to Deploy .NET Code to AWS from Within Visual Studio.

The AWS Online Tech Talks series covers a broad range of topics at varying technical levels. These sessions feature live demonstrations & customer examples led by AWS engineers and Solution Architects. Check out the AWS YouTube channel for more on-demand webinars on AWS technologies.

– Sara (Hello everyone, I’m a co-op from Northeastern University joining the team until December.)

Starting today Amazon Virtual Private Cloud (VPC) Endpoints for Amazon DynamoDB are available in all public AWS regions. You can provision an endpoint right away using the AWS Management Console or the AWS Command Line Interface (CLI). There are no additional costs for a VPC Endpoint for DynamoDB.

Many AWS customers run their applications within a Amazon Virtual Private Cloud (VPC) for security or isolation reasons. Previously, if you wanted your EC2 instances in your VPC to be able to access DynamoDB, you had two options. You could use an Internet Gateway (with a NAT Gateway or assigning your instances public IPs) or you could route all of your traffic to your local infrastructure via VPN or AWS Direct Connect and then back to DynamoDB. Both of these solutions had security and throughput implications and it could be difficult to configure NACLs or security groups to restrict access to just DynamoDB. Here is a picture of the old infrastructure.

Creating an Endpoint

Let’s create a VPC Endpoint for DynamoDB. We can make sure our region supports the endpoint with the DescribeVpcEndpointServices API call.

aws ec2 describe-vpc-endpoint-services --region us-east-1
{
    "ServiceNames": [
        "com.amazonaws.us-east-1.dynamodb",
        "com.amazonaws.us-east-1.s3"
    ]
}

Great, so I know my region supports these endpoints and I know what my regional endpoint is. I can grab one of my VPCs and provision an endpoint with a quick call to the CLI or through the console. Let me show you how to use the console.

First I’ll navigate to the VPC console and select “Endpoints” in the sidebar. From there I’ll click “Create Endpoint” which brings me to this handy console.

You’ll notice the AWS Identity and Access Management (IAM) policy section for the endpoint. This supports all of the fine grained access control that DynamoDB supports in regular IAM policies and you can restrict access based on IAM policy conditions.

For now I’ll give full access to my instances within this VPC and click “Next Step”.

This brings me to a list of route tables in my VPC and asks me which of these route tables I want to assign my endpoint to. I’ll select one of them and click “Create Endpoint”.

Keep in mind the note of warning in the console: if you have source restrictions to DynamoDB based on public IP addresses the source IP of your instances accessing DynamoDB will now be their private IP addresses.

After adding the VPC Endpoint for DynamoDB to our VPC our infrastructure looks like this.

That’s it folks! It’s that easy. It’s provided at no cost. Go ahead and start using it today. If you need more details you can read the docs here.

We love bringing our customers helpful information and we have another cool series we are excited to tell you about. The AWS Partner Webinar Series is a selection of live and recorded presentations covering a broad range of topics at varying technical levels and scale. A little different from our AWS Online TechTalks, each AWS Partner Webinar is hosted by an AWS solutions architect and an AWS Competency Partner who has successfully helped customers evaluate and implement the tools, techniques, and technologies of AWS.

Check out this month’s webinars and let us know which ones you found the most helpful! All schedule times are shown in the Pacific Time (PDT) time zone.

Security Webinars

Sophos
Seeing More Clearly: ATLO Software Secures Online Training Solutions for Correctional Facilities with SophosUTM on AWS Link.
August 17th, 2017 | 10:00 AM PDT

F5
F5 on AWS: How MailControl Improved their Application Visibility and Security
August 23, 2017 | 10:00 AM PDT

Big Data Webinars

Tableau, Matillion, 47Lining, NorthBay
Unlock Insights and Reduce Costs by Modernizing Your Data Warehouse on AWS
August 22, 2017 | 10:00 AM PDT

Storage Webinars

StorReduce
How Globe Telecom does Primary Backups via StorReduce to the AWS Cloud
August 29, 2017 | 8:00 AM PDT

Commvault
Moving Forward Faster: How Monash University Automated Data Movement for 3500 Virtual Machines to AWS with Commvault
August 29, 2017 | 1:00 PM PDT

Dell EMC
Moving Forward Faster: Protect Your Workloads on AWS With Increased Scale and Performance
August 30, 2017 | 11:00 AM PDT

Druva
How Hatco Protects Against Ransomware with Druva on AWS
September 13, 2017 | 10:00 AM PDT

Whew – what a week! Tara, Randall, Ana, and I have been working around the clock to create blog posts for the announcements that we made at the AWS Summit in New York. Here’s a summary to help you to get started:

Amazon Macie – This new service helps you to discover, classify, and secure content at scale. Powered by machine learning and making use of Natural Language Processing (NLP), Macie looks for patterns and alerts you to suspicious behavior, and can help you with governance, compliance, and auditing. You can read Tara’s post to see how to put Macie to work; you select the buckets of interest, customize the classification settings, and review the results in the Macie Dashboard.

AWS Glue – Randall’s post (with deluxe animated GIFs) introduces you to this new extract, transform, and load (ETL) service. Glue is serverless and fully managed, As you can see from the post, Glue crawls your data, infers schemas, and generates ETL scripts in Python. You define jobs that move data from place to place, with a wide selection of transforms, each expressed as code and stored in human-readable form. Glue uses Development Endpoints and notebooks to provide you with a testing environment for the scripts you build. We also announced that Amazon Athena now integrates with Amazon Glue, as does Apache Spark and Hive on Amazon EMR.

AWS Migration Hub – This new service will help you to migrate your application portfolio to AWS. My post outlines the major steps and shows you how the Migration Hub accelerates, tracks,and simplifies your migration effort. You can begin with a discovery step, or you can jump right in and migrate directly. Migration Hub integrates with tools from our migration partners and builds upon the Server Migration Service and the Database Migration Service.

CloudHSM Update – We made a major upgrade to AWS CloudHSM, making the benefits of hardware-based key management available to a wider audience. The service is offered on a pay-as-you-go basis, and is fully managed. It is open and standards compliant, with support for multiple APIs, programming languages, and cryptography extensions. CloudHSM is an integral part of AWS and can be accessed from the AWS Management Console, AWS Command Line Interface (CLI), and through API calls. Read my post to learn more and to see how to set up a CloudHSM cluster.

Managed Rules to Secure S3 Buckets – We added two new rules to AWS Config that will help you to secure your S3 buckets. The s3-bucket-public-write-prohibited rule identifies buckets that have public write access and the s3-bucket-public-read-prohibited rule identifies buckets that have global read access. As I noted in my post, you can run these rules in response to configuration changes or on a schedule. The rules make use of some leading-edge constraint solving techniques, as part of a larger effort to use automated formal reasoning about AWS.

CloudTrail for All Customers – Tara’s post revealed that AWS CloudTrail is now available and enabled by default for all AWS customers. As a bonus, Tara reviewed the principal benefits of CloudTrail and showed you how to review your event history and to deep-dive on a single event. She also showed you how to create a second trail, for use with CloudWatch CloudWatch Events.

Encryption of Data at Rest for EFS – When you create a new file system, you now have the option to select a key that will be used to encrypt the contents of the files on the file system. The encryption is done using an industry-standard AES-256 algorithm. My post shows you how to select a key and to verify that it is being used.

Watch the Keynote
My colleagues Adrian Cockcroft and Matt Wood talked about these services and others on the stage, and also invited some AWS customers to share their stories. Here’s the video:

Watch the Twitch Stram
My colleagues Ian Massingham, Tara Walker, and Abby Fuller hosted a live stream on the AWS Twitch channel:

— Jeff;

When Jeff and I heard about this service, we both were curious on the meaning of the name Macie. Of course, Jeff being a great researcher looked up the name Macie and found that the name Macie has two meanings. It has both French and English (UK) based origin, it is typically a girl name, has various meanings. The first meaning of Macie that was found, said that that name meant “weapon”.  The second meaning noted the name was representative of a person that is bold, sporty, and sweet. In a way, these definitions are appropriate, as today I am happy to announce that we are launching  Amazon Macie, a new security service that uses machine learning to help identify and protect sensitive data stored in AWS from breaches, data leaks, and unauthorized access with Amazon Simple Storage Service (S3) being the initial data store. Therefore, I can imagine that Amazon Macie could be described as a bold, weapon for AWS customers providing a sweet service with a sporty user interface that helps to protects against malicious access of your data at rest. Whew, that was a mouthful, but I unbelievably got all the Macie descriptions out in a single sentence! Nevertheless, I am a thrilled to share with you the power of the new Amazon Macie service.

Amazon Macie is a service powered by machine learning that can automatically discover and classify your data stored in Amazon S3. But Macie doesn’t stop there, once your data has been classified by Macie, it assigns each data item a business value, and then continuously monitors the data in order to detect any suspicious activity based upon access patterns. Key features of the Macie service include:

• Data Security Automation: analyzes, classifies, and processes data to understand the historical patterns, user authentications to data, data access locations, and times of access.
• Data Security & Monitoring: actively monitors usage log data for anomaly detected along with automatic resolution of reported issues through CloudWatch Events and Lambda
• Data Visibility for Proactive Loss prevention: Provides management visibility into details of storage data while providing immediate protection without the need for manual customer input
• Data Research and Reporting: allows administrative configuration for reporting and alert management requirements

How does Amazon Macie accomplish this you ask? 

Using machine learning algorithms for natural language processing (NLP), Macie can automate the classification of data in your S3 buckets. In addition, Amazon Macie takes advantage of predictive analytics algorithms enabling data access patterns to be dynamically analyzed. Learnings are then used to inform and to alert you on possible suspicious behavior. Macie also runs an engine specifically to detect common sources of personally identifiable information (PII), or sensitive personal information (SP).  Macie takes advantage of AWS CloudTrail and continuously checks Cloudtrail events for PUT requests in S3 buckets and automatically classify new objects in almost real time.

While Macie is a powerful tool to use for security and data protection in the AWS cloud, it also can aid you with governance, compliance requirements, and/or audit standards.  Many of you may already be aware of the EU’s most stringent privacy regulation to date – The General Protection Data Regulation (GDPR), which becomes enforceable on May 25, 2018. As Amazon Macie recognizes personally identifiable information (PII) and provides customers with dashboards and alerts, it will enable customers to comply with GDPR regulations around encryption and pseudonymization of data. When combined with Lambda queries, Macie becomes a powerful tool to help remediate GDPR concerns.

Tour of the Amazon Macie Service

Let’s look a tour of the service and look at Amazon Macie up close and personal.

First, I will log onto the Macie console and start the process of setting up Macie so that I can start to my data classification and protection by clicking the Get Started button.

As you can see, to enable the Amazon Macie service, I must have the appropriate IAM roles created for the service, and additionally I will need to have AWS CloudTrail enabled in my account.

I will create these roles and turn on the AWS CloudTrail service in my account. To make things easier for you to setup Macie, you can take advantage of sample template for CloudFormation provided in the Macie User Guide that will set up required IAM roles and policies for you, you then would only need to setup a trail as noted in the CloudTrail documentation.

If you have multiple AWS accounts, you should note that the account you use to enable the Macie service will be noted as the master account, you can integrate other accounts with the Macie service but they will have the member account designation. Users from member accounts will need to use an IAM role to federate access to the master account in order access the Macie console.

Now that my IAM roles are created and CloudTrail is enabled, I will click the Enable Macie button to start Macie’s data monitoring and protection.

Once Macie is finished starting the service in your account, you will be brought to the service main screen and any existing alerts in your account will be presented to you. Since I have just started the service, I currently have no existing alerts at this time.

Considering we are doing a tour of the Macie service, I will now integrate some of my S3 buckets with Macie. However, you do not have to specify any S3 buckets for Macie to start monitoring since the service already uses the AWS CloudTrail Management API analyze and process information. With this tour of Macie, I have decided to monitor some object level API events in from certain buckets in CloudTrail.

In order to integrate with S3, I will go to the Integrations tab of the Macie console.  Once on the Integrations tab, I will see two options: Accounts and Services. The Account option is used to integrate member accounts with Macie and to set your data retention policy. Since I want to integrate specific S3 buckets with Macie, I’ll click the Services option go to the Services tab.

When I integrate Macie with the S3 service, a trail and a S3 bucket will be created to store logs about S3 data events. To get started, I will use the Select an account drop down to choose an account.  Once my account is selected, the services available for integration are presented. I’ll select the Amazon S3 service by clicking the Add button.

Now I can select the buckets that I want Macie to analyze, selecting the Review and Save button takes me to a screen which I confirm that I desire object level logging by clicking Save button.

4
Next, on our Macie tour, let’s look at how we can customize data classification with Macie.

As we discussed, Macie will automatically monitor and classify your data. Once Macie identifies your data it will classify your data objects by file and content type. Macie will also use a support vector machine (SVM) classifier to classify the content within S3 objects in addition to the metadata of the file. In deep learning/machine learning fields of study, support vector machines are supervised learning models, which have learning algorithms used for classification and regression analysis of data. Macie trained the SVM classifier by using a data of varying content types optimized to support accurate detection of data content even including the source code you may write.

Macie will assign only one content type per data object or file, however, you have the ability to enable or disable content type and file extensions in order to include or exclude them from the Macie service classifying these objects. Once Macie classifies the data, it will assign risk level of the object between 1 and 10 with 10 being the highest risk and 1 being the lowest data risk level.

To customize the classification of our data with Macie, I’ll go to the Settings Tab. I am now presented with the choices available to enable or disable the Macie classifications settings.

For an example during our tour of Macie, I will choose File extension. When presented with the list of file extensions that Macie tracks and uses for classifications.

As a test, I’ll edit the apk file extension for Android application install file, and disable monitoring of this file by selecting No – disabled from the dropdown and clicking the Save button. Of course, later I will turn this back on since I want to keep my entire collection of data files safe including my Android development binaries.

One last thing I want to note about data classification using Macie is that the service provides visibility in how you data object are being classified and highlights data assets that you have stored regarding how critical or important the information for compliance, for your personal data and for your business.

Now that we have explored the data that Macie classifies and monitors, the last stop on our service tour is the Macie dashboard.

The Macie Dashboard provides us with a complete picture of all of the data and activity that has been gathered as Macie monitors and classifies our data. The dashboard displays Metrics and Views grouped by categories to provide different visual perspectives of your data. Within these dashboard screens, you also you can go from a metric perspective directly to the Research tab to build and run queries based on the metric. These queries can be used to set up customized alerts for notification of any possible security issues or problems. We won’t have an opportunity to tour the Research or Alerts tab, but you can find out more information about these features in the Macie user guide.

Turning back to the Dashboard, there are so many great resources in the Macie Dashboard that we will not be able to stop at each view, metric, and feature during our tour, so let me give you an overview of all the features of the dashboard that you can take advantage of using.

Dashboard Metrics – monitored data grouped by the following categories:

• High-risk S3 objects: data objects with risk levels of 8 through 10.
• Total event occurrences: – total count of all event occurrences since Macie was enabled
• Total user sessions – 5-minute snapshot of CloudTrail data

Dashboard Views – views to display various points of the monitored data and activity:

• S3 objects for a selected time range
• S3 objects
• S3 objects by personally identifiable information (PII)
• S3 objects by ACL
• CloudTrail events and associated users
• CloudTrail errors and associated users
• Activity location
• AWS CLoudTrail events
• Activity ISPs
• AWS CloudTrail user identity types

Summary

Well, that concludes our tour of the new and exciting Amazon Macie service. Amazon Macie is a sensational new service that uses the power of machine learning and deep learning to aid you in securing, identifying, and protecting your data stored in Amazon S3. Using natural language processing (NLP) to automate data classification, Amazon Macie enables you to easily get started with high accuracy classification and immediate protection of your data by simply enabling the service.  The interactive dashboards give visibility to the where, what, who, and when of your information allowing you to proactively analyze massive streams of data, data accesses, and API calls in your environment. Learn more about Amazon Macie by visiting the product page or the documentation in the Amazon Macie user guide.

– Tara

We launched Amazon Elastic File System in production form a little over a year ago (see Amazon Elastic File System – Production Ready in Three Regions for more information). Later in the year we added On-Premises access via Direct Connect and made EFS available in the US East (Ohio) Region, following up this year with availability in the EU (Frankfurt) and Asia Pacific (Sydney) Regions.

Encryption at Rest
Today we are adding support for encryption of data at rest. When you create a new file system, you can select a key that will be used to encrypt the contents of the files that you store on the file system. The key can be a built-in key that is managed by AWS or a key that you created yourself using AWS Key Management Service (KMS). File metadata (file names, directory names, and directory contents) will be encrypted using a key managed by AWS. Both forms of encryption are implemented using an industry-standard AES-256 algorithm.

You can set this up in seconds when you create a new file system. You simply choose the built-in key (aws/elasticfilesystem) or one of your own:

EFS will take care of the rest! You can select the filesystem in the console to verify that it is encrypted as desired:

A cryptographic algorithm that meets the approval of FIPS 140-2 is used to encrypt data and metadata. The encryption is transparent and has a minimal effect on overall performance.

You can use AWS Identity and Access Management (IAM) to control access to the Customer Master Key (CMK). The CMK must be enabled in order to grant access to the file system; disabling the key prevents it from being used to create new file systems and blocks access (after a period of time) to existing file systems that it protects. To learn more about your options, read Managing Access to Encrypted File Systems.

Available Now
Encryption of data at rest is available now in all regions where EFS is supported, at no additional charge.

— Jeff;