System and Organization Controls 3 (SOC 3) Report
Total Page：16
File Type：pdf, Size：1020Kb
Load more
Recommended publications
-
Vsrx Deployment Guide for Google Cloud Platform
vSRX Deployment Guide for Google Cloud Platform Published 2020-09-22 ii Juniper Networks, Inc. 1133 Innovation Way Sunnyvale, California 94089 USA 408-745-2000 www.juniper.net Juniper Networks, the Juniper Networks logo, Juniper, and Junos are registered trademarks of Juniper Networks, Inc. in the United States and other countries. All other trademarks, service marks, registered marks, or registered service marks are the property of their respective owners. Juniper Networks assumes no responsibility for any inaccuracies in this document. Juniper Networks reserves the right to change, modify, transfer, or otherwise revise this publication without notice. vSRX Deployment Guide for Google Cloud Platform Copyright © 2020 Juniper Networks, Inc. All rights reserved. The information in this document is current as of the date on the title page. YEAR 2000 NOTICE Juniper Networks hardware and software products are Year 2000 compliant. Junos OS has no known time-related limitations through the year 2038. However, the NTP application is known to have some difficulty in the year 2036. END USER LICENSE AGREEMENT The Juniper Networks product that is the subject of this technical documentation consists of (or is intended for use with) Juniper Networks software. Use of such software is subject to the terms and conditions of the End User License Agreement (“EULA”) posted at https://support.juniper.net/support/eula/. By downloading, installing or using such software, you agree to the terms and conditions of that EULA. iii Table of Contents About the Documentation -
(AWS) Security Workshop - Pre-Read Material
Amazon Web Services (AWS) Security Workshop - Pre-read material It is highly recommended to go through the pre-read before attending the AWS Security workshop. Shared Responsibility Model Security and Compliance is a shared responsibility between AWS and the customer. This shared model can help relieve the customer’s operational burden as AWS operates, manages and controls the components from the host operating system and virtualization layer down to the physical security of the facilities in which the service operates. The customer assumes responsibility and management of the guest operating system (including updates and security patches), other associated application software as well as the configuration of the AWS provided security group firewall. Customers should carefully consider the services they choose as their responsibilities vary depending on the services used, the integration of those services into their IT environment, and applicable laws and regulations. The nature of this shared responsibility also provides the flexibility and customer control that permits the deployment. As shown in the chart below, this differentiation of responsibility is commonly referred to as Security “of” the Cloud versus Security “in” the Cloud. AWS responsibility “Security of the Cloud” - AWS is responsible for protecting the infrastructure that runs all of the services offered in the AWS Cloud. This infrastructure is composed of the hardware, software, networking, and facilities that run AWS Cloud services. Customer responsibility “Security in the Cloud” – Customer responsibility will be determined by the AWS Cloud services that a customer selects. This determines the amount of configuration work the customer must perform as part of their security responsibilities. -
Commvault on AWS Comprehensive Cloud Data Management for Hybrid IT Table of Contents
Commvault on AWS Comprehensive Cloud Data Management for Hybrid IT Table of Contents The Data Management Challenge 3 Commvault and Amazon Web Services 4 Benefits 7 Case Study: Dow Jones 8 Getting Started 9 2 The Data Management Challenge Today, more data is being generated than ever before. Keeping up with the rapid pace of data growth presents a series of challenges. Enterprise organizations are collecting petabytes of customer and application data that must be backed up and accessible to meet compliance requirements. Increased regulation around data retention policies make it more difficult to manage backup and archive storage, as critical data may be required to be kept for years and maybe difficult to find. Further complicating matters, point solutions often overlap and leave gaps where data is unprotected and require additional staff to manage them. These issues lead to increased costs, yet many IT departments are facing budget cuts and cannot expand upon their capital to meet increased performance demands. Today’s hybrid IT organizations realize cloud can help solve many data storage issues. With cloud storage comes the need for a comprehensive data management platform to manage data both on-premises and in the cloud. Savvy organizations streamline IT operations and reduce cloud waste with flexible orchestration to automate resource provisioning, policies and routine tasks. This eBook will demonstrate how Commvault and Amazon Web Services (AWS) deliver a cost-effective data management solution that addresses all of these challenges with a single, scalable solution. 3 Commvault and Amazon Web Services Unlike most backup and recovery solutions, Commvault offers a single data platform solution for all backup and recovery needs; as opposed to having to several point solutions for each use case (i.e. -
AWS Risk and Compliance Whitepaper for Additional Details - Policy Available At
Amazon Web Services: Risk and Compliance January 2017 (Consult http://aws.amazon.com/compliance/resources for the latest version of this paper) Amazon Web Services Risk and Compliance January 2017 This document is intended to provide information to assist AWS customers with integrating AWS into their existing control framework supporting their IT environment. This document includes a basic approach to evaluating AWS controls and provides information to assist customers with integrating control environments. This document also addresses AWS-specific information around general cloud computing compliance questions. Table of Contents Risk and Compliance Overview .......................................................................................................................3 Shared Responsibility Environment ............................................................................................................................................... 3 Strong Compliance Governance ...................................................................................................................................................... 4 Evaluating and Integrating AWS Controls ...................................................................................................4 AWS IT Control Information ........................................................................................................................................................... 5 AWS Global Regions ......................................................................................................................................................................... -
AWS Managed Services (AMS)
AWS Managed Services (AMS) Application Developer's Guide AMS Advanced Operations Plan Version September 16, 2020 AWS Managed Services (AMS) Application Developer's Guide AMS Advanced Operations Plan AWS Managed Services (AMS) Application Developer's Guide: AMS Advanced Operations Plan Copyright © Amazon Web Services, Inc. and/or its affiliates. All rights reserved. Amazon's trademarks and trade dress may not be used in connection with any product or service that is not Amazon's, in any manner that is likely to cause confusion among customers, or in any manner that disparages or discredits Amazon. All other trademarks not owned by Amazon are the property of their respective owners, who may or may not be affiliated with, connected to, or sponsored by Amazon. AWS Managed Services (AMS) Application Developer's Guide AMS Advanced Operations Plan Table of Contents Application Onboarding to AMS Introduction ........................................................................................ 1 What is Application Onboarding? ................................................................................................. 1 What we do, what we do not do .................................................................................................. 1 AMS Amazon Machine Images (AMIs) ............................................................................................ 2 Security enhanced AMIs ...................................................................................................... 4 Key terms ................................................................................................................................. -
Building Alexa Skills Using Amazon Sagemaker and AWS Lambda
Bootcamp: Building Alexa Skills Using Amazon SageMaker and AWS Lambda Description In this full-day, intermediate-level bootcamp, you will learn the essentials of building an Alexa skill, the AWS Lambda function that supports it, and how to enrich it with Amazon SageMaker. This course, which is intended for students with a basic understanding of Alexa, machine learning, and Python, teaches you to develop new tools to improve interactions with your customers. Intended Audience This course is intended for those who: • Are interested in developing Alexa skills • Want to enhance their Alexa skills using machine learning • Work in a customer-focused industry and want to improve their customer interactions Course Objectives In this course, you will learn: • The basics of the Alexa developer console • How utterances and intents work, and how skills interact with AWS Lambda • How to use Amazon SageMaker for the machine learning workflow and for enhancing Alexa skills Prerequisites We recommend that attendees of this course have the following prerequisites: • Basic familiarity with Alexa and Alexa skills • Basic familiarity with machine learning • Basic familiarity with Python • An account on the Amazon Developer Portal Delivery Method This course is delivered through a mix of: • Classroom training • Hands-on Labs Hands-on Activity • This course allows you to test new skills and apply knowledge to your working environment through a variety of practical exercises • This course requires a laptop to complete lab exercises; tablets are not appropriate Duration One Day Course Outline This course covers the following concepts: • Getting started with Alexa • Lab: Building an Alexa skill: Hello Alexa © 2018, Amazon Web Services, Inc. -
Performance Efficiency Pillar
Performance Efficiency Pillar AWS Well-Architected Framework Performance Efficiency Pillar AWS Well-Architected Framework Performance Efficiency Pillar: AWS Well-Architected Framework Copyright © Amazon Web Services, Inc. and/or its affiliates. All rights reserved. Amazon's trademarks and trade dress may not be used in connection with any product or service that is not Amazon's, in any manner that is likely to cause confusion among customers, or in any manner that disparages or discredits Amazon. All other trademarks not owned by Amazon are the property of their respective owners, who may or may not be affiliated with, connected to, or sponsored by Amazon. Performance Efficiency Pillar AWS Well-Architected Framework Table of Contents Abstract and Introduction ................................................................................................................... 1 Abstract .................................................................................................................................... 1 Introduction .............................................................................................................................. 1 Performance Efficiency ....................................................................................................................... 2 Design Principles ........................................................................................................................ 2 Definition ................................................................................................................................. -
Innovation on Every Front
INNOVATION ON EVERY FRONT AWS DeepComposer uses AI to compose music. AI AWS DeepComposer The new service gives developers a creative way to get started and become familiar with machine learning capabilities. Amazon Transcribe Medical is a machine AI Amazon Transcribe Medical learning service that makes it easy to quickly create accurate transcriptions from medical consultations between patients and physician dictated notes, practitioner/patient consultations, and tele-medicine are automatically converted from speech to text for use in clinical documen- tation applications. Amazon Rekognition Custom Labels helps users AI Amazon Rekognition Custom identify the objects and scenes in images that are specific to business needs. For example, Labels users can find corporate logos in social media posts, identify products on store shelves, classify machine parts in an assembly line, distinguish healthy and infected plants, or detect animated characters in videos. Amazon SageMaker Model Monitor is a new AI Amazon SageMaker Model capability of Amazon SageMaker that automatically monitors machine learning (ML) Monitor models in production, and alerts users when data quality issues appear. Amazon SageMaker is a fully managed service AI Amazon SageMaker that provides every developer and data scientist with the ability to build, train, and deploy ma- Experiments chine learning (ML) models quickly. SageMaker removes the heavy lifting from each step of the machine learning process to make it easier to develop high quality models. Amazon SageMaker Debugger is a new capa- AI Amazon SageMaker Debugger bility of Amazon SageMaker that automatically identifies complex issues developing in machine learning (ML) training jobs. Amazon SageMaker Autopilot automatically AI Amazon SageMaker Autopilot trains and tunes the best machine learning models for classification or regression, based on your data while allowing to maintain full control and visibility. -
Amazon Documentdb Deep Dive
DAT326 Amazon DocumentDB deep dive Joseph Idziorek Antra Grover Principal Product Manager Software Development Engineer Amazon Web Services Fulfillment By Amazon © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Agenda What is the purpose of a document database? What customer problems does Amazon DocumentDB (with MongoDB compatibility) solve and how? Customer use case and learnings: Fulfillment by Amazon What did we deliver for customers this year? What’s next? © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Purpose-built databases Relational Key value Document In-memory Graph Search Time series Ledger Why document databases? Denormalized data Normalized data model model { 'name': 'Bat City Gelato', 'price': '$', 'rating': 5.0, 'review_count': 46, 'categories': ['gelato', 'ice cream'], 'location': { 'address': '6301 W Parmer Ln', 'city': 'Austin', 'country': 'US', 'state': 'TX', 'zip_code': '78729'} } Why document databases? GET https://api.yelp.com/v3/businesses/{id} { 'name': 'Bat City Gelato', 'price': '$', 'rating': 5.0, 'review_count': 46, 'categories': ['gelato', 'ice cream'], 'location': { 'address': '6301 W Parmer Ln', 'city': 'Austin', 'country': 'US', 'state': 'TX', 'zip_code': '78729'} } Why document databases? response = yelp_api.search_query(term='ice cream', location='austin, tx', sort_by='rating', limit=5) Why document databases? for i in response['businesses']: col.insert_one(i) db.businesses.aggregate([ { $group: { _id: "$price", ratingAvg: { $avg: "$rating"}} } ]) db.businesses.find({ -
Consistency in Cloud-Based Database Systems
https://doi.org/10.31449/inf.v43i1.2650 Informatica 43 (2019) 313–319 313 Consistency in Cloud-based Database Systems Zohra Mahfoud USTHB University, Algeria E-mail: [email protected] Nadia Nouali-Taboudjemat CERIST Research Center, Algeria E-mail: [email protected] Keywords: cloud computing, consistency, distributed databases, relational databases, No-SQL, CAP Received: July 15, 2019 Cloud computing covers the large spectrum of services available on the internet. Cloud services use replication to ensure high availability. Within database replication, various copies of the same data item are stored in different sites, this situation requires managing the consistency of the multiple copies. In fact, the requirement for consistency level can be different according to application natures and other metrics; a delay of some minutes in visualizing latest posts in social networks can be tolerated, while some seconds can make a loss of a bid in an auction system. Wide variety of database management systems are used actually by cloud services, they support different levels of consistency to meet the diversity of needs. This paper draws a presentation of the main characteristics of cloud computing and data management systems and describes different consistency models. Then it discusses the most famous cloud-based database management systems from the point of view of their data and consistency models. Povzetek: Prispevek analizira podatkovna skladišča v oblakih predvsem s stališča konsistentnosti. 1 Introduction Cloud computing refers to the large spectrum of services cloud systems and describes the implemented models of available on the internet. These services manage big data and consistency. Section 6 concludes the paper. -
Build Your Own Anomaly Detection ML Pipeline
Device telemetry data is ingested from the field Build your own Anomaly Detection ML Pipeline 1 devices on a near real-time basis by calls to the This end-to-end ML pipeline detects anomalies by ingesting real-time, streaming data from various network edge field devices, API via Amazon API Gateway. The requests get performing transformation jobs to continuously run daily predictions/inferences, and retraining the ML models based on the authenticated/authorized using Amazon Cognito. incoming newer time series data on a daily basis. Note that Random Cut Forest (RCF) is one of the machine learning algorithms Amazon Kinesis Data Firehose ingests the data in for detecting anomalous data points within a data set and is designed to work with arbitrary-dimensional input. 2 real time, and invokes AWS Lambda to transform the data into parquet format. Kinesis Data AWS Cloud Firehose will automatically scale to match the throughput of the data being ingested. 1 Real-Time Device Telemetry Data Ingestion Pipeline 2 Data Engineering Machine Learning Devops Pipeline Pipeline The telemetry data is aggregated on an hourly 3 3 basis and re-partitioned based on the year, month, date, and hour using AWS Glue jobs. The Amazon Cognito AWS Glue Data Catalog additional steps like transformations and feature Device #1 engineering are performed for training the SageMaker AWS CodeBuild Anomaly Detection ML Model using AWS Glue Notebook Create ML jobs. The training data set is stored on Amazon Container S3 Data Lake. Amazon Kinesis AWS Lambda S3 Data Lake AWS Glue Device #2 Amazon API The training code is checked in an AWS Gateway Data Firehose 4 AWS SageMaker CodeCommit repo which triggers a Machine Training CodeCommit Containers Learning DevOps (MLOps) pipeline using AWS Dataset for Anomaly CodePipeline. -
Introduction to Amazon EC2 Running IBM
Introduction to Amazon EC2 Running IBM Featuring Mike Culver, Technical Evangelist for Amazon Web Services Melody Ng, Manager, Data Management Emerging Partnerships & Technologies for IBM Jason Chan, Linux and Virtualization Lead, Data Management Emerging Partnerships & Technologies for IBM Majed Itani, Chief Software Architect for SugarCRM Webinar — Introduction to Amazon EC2 Running IBM Introducon IBM SugarCRM Q&A Q&A Amazon Has Three Parts 1 2 3 What You Want… Develop Test Operate What You Get… Undifferenated heavy liing • Hardware costs • Soware costs • Maintenance • Load balancing • Scaling Develop Test Operate • Ulizaon • Idle machines • Bandwidth management • Server hosng • Storage Management • High availability Continuous Process Improvement Makes it Worse Undifferenated heavy liing • Hardware costs • Soware costs • Maintenance • Load balancing • Scaling Develop Test Operate • Ulizaon • Idle machines • Bandwidth management • Server hosng • Storage Management • High availability The 70/30 Switch Differenated Value Undifferenated Heavy Liing Creaon Undifferenated Differenated Value Creaon Heavy Liing We Think of the Cloud as a Set of Building Block Services Infrastructure As a Service Payments As a Service Amazon Simple Storage Service Amazon Flexible Payments Amazon Elastic Compute Cloud Service Amazon Simple Queue Service Amazon DevPay Amazon SimpleDB Amazon CloudFront Fulfillment and Associates Amazon Elastic MapReduce Amazon Fulfillment Web Service Amazon Associates Web Service People As a Service Amazon Mechanical Turk What is Amazon