We like that Azure Sentinel does not require as much maintenance as legacy SIEMs that are on-premises. Azure Sentinel is auto-scaling - you will not have to worry about performance impact, you will always have the performance capability you need. If you have Microsoft 365, it is very easy to plug the endpoints into Azure Sentinel. With this solution, you can go on the offensive and stay proactive, continually hunting for threats. Azure Sentinel is purely cloud-based and a leading next-generation SIEM.
We have experienced a few false positives with Azure Sentinel. There is a certain level of expertise that you need to possess to appropriately utilize all of Azure Sentinel's offerings - it can be a somewhat steep learning curve to get things running at capacity. It would be an improvement if Azure Sentinel integrated better with other SaaS providers and offered more out-of-the-box connectors.
You get a huge range of powerful security tools with AWS Security Hub, including compliance scanners, vulnerability endpoint protection, and firewalls. AWS Security Hub
has very good detection and offers helpful real-time alerts. AWS Security Hub aggregates, organizes, and prioritizes security alerts or findings from other AWS services, all in one single pane.
AWS Security Hub lacks a certain level of self-sufficiency, though. We would like to see AWS Security Hub become a multi-cloud solution. AWS Security Hub has some regional restrictions that have proved problematic for us; we need visibility for all instances we have on our account. We found that AWS Security Hub is not a good global product.
Conclusion:
We felt AWS was lacking in some basic features we consider essential, like multi-region coverage. We also wanted a solution that was more intuitive.
We found Azure Sentinel to be a better fit for our team and our clients. We have a global reach and need a product that could satisfy cross-region coverage efficiently. We also feel that Azure Sentinel offers better proactive threat awareness.
Had prepared some comparison factors between AWS and Azure for one of my presales discussions, hope this will hold some insights .So depending on the requirements from the client appropriate solutions can be proposed. Widely Azure Sentinel is what has be going of matching the customer requriements.
AI and machine learning
AWS service
Azure service
Description
SageMaker
Machine Learning
A cloud service to train, deploy, automate, and manage machine learning models.
Alexa Skills Kit
Bot Framework
Build and connect intelligent bots that interact with your users using text/SMS, Skype, Teams, Slack, Microsoft 365 mail, Twitter, and other popular services.
Lex
Speech Services
API capable of converting speech to text, understanding intent, and converting text back to speech for natural responsiveness.
Lex
Language Understanding (LUIS)
Allows your applications to understand user commands contextually.
Polly, Transcribe
Speech Services
Enables both Speech to Text, and Text into Speech capabilities.
Rekognition
Cognitive Services
Computer Vision: Extract information from images to categorize and process visual data.
Face: Detect, identify, and analyze faces and facial expressions in photos.
Skills Kit
Virtual Assistant
The Virtual Assistant Template brings together a number of best practices we've identified through the building of conversational experiences and automates integration of components that we've found to be highly beneficial to Bot Framework developers.
Big data and analytics
AWS service
Azure service
Description
Redshift
Synapse Analytics
Cloud-based Enterprise Data Warehouse (EDW) that uses Massively Parallel Processing (MPP) to quickly run complex queries across petabytes of data.
Lake Formation
Data Share
A simple and safe service for sharing big data
Big data processing
AWS service
Azure service
Description
EMR
Azure Data Explorer
Fully managed, low latency, distributed big data analytics platform to run complex queries across petabytes of data.
EMR
Databricks
Apache Spark-based analytics platform.
EMR
HDInsight
Managed Hadoop service. Deploy and manage Hadoop clusters in Azure.
EMR
Data Lake Storage
Massively scalable, secure data lake functionality built on Azure Blob Storage.
Data orchestration / ETL
AWS service
Azure service
Description
Data Pipeline, Glue
Data Factory
Processes and moves data between different compute and storage services, as well as on-premises data sources at specified intervals. Create, schedule, orchestrate, and manage data pipelines.
Glue
Azure Purview
A unified data governance service that helps you manage and govern your on-premises, multicloud, and software as a service (SaaS) data.
Dynamo DB
Table Storage, Cosmos DB
NoSQL key-value store for rapid development using massive semi-structured datasets.
Analytics and visualization
AWS service
Azure service
Description
Kinesis Analytics
Stream Analytics
Storage and analysis platforms that create insights from large quantities of data, or data that originates from many sources.
Azure Data Explorer
Data Lake Analytics
Data Lake Store
QuickSight
Power BI
Business intelligence tools that build visualizations, perform ad hoc analysis, and develop business insights from data.
CloudSearch
Cognitive Search
Delivers full-text search and related search analytics and capabilities.
Athena
Data Lake Analytics
Provides a serverless interactive query service that uses standard SQL for analyzing databases.
Azure Synapse Analytics
Azure Synapse Analytics is a limitless analytics service that brings together data integration, enterprise data warehousing, and big data analytics. It gives you the freedom to query data on your terms, using either serverless or dedicated resources at scale.
Elasticsearch Service
Elastic on Azure
Use the Elastic Stack (Elastic, Logstash, and Kibana) to search, analyze, and visualize in real time.
Database
Type
AWS Service
Azure Service
Description
Relational database
RDS
SQL Database
Managed relational database services in which resiliency, scale and maintenance are primarily handled by the Azure platform.
Database for MySQL
Database for PostgreSQL
Database for MariaDB
Serverless relational database
Amazon Aurora Serverless
Azure SQL Database serverless
Database offerings that automatically scales compute based on the workload demand. You're billed per second for the actual compute used (Azure SQL)/data that's processed by your queries (Azure Synapse Analytics Serverless).
Serverless SQL pool in Azure Synapse Analytics
NoSQL/
DynamoDB
Cosmos DB
Cosmos DB is a globally distributed, multi-model database that natively supports multiple data models including key-value pairs, documents, graphs and columnar.
Document
SimpleDB
Amazon DocumentDB
Caching
ElastiCache
Cache for Redis
An in-memory–based, distributed caching service that provides a high-performance store typically used to offload nontransactional work from a database.
Database migration
Database Migration Service
Database Migration Service
A service that executes the migration of database schema and data from one database format to a specific database technology in the cloud.
We compared AWS Security Hub and Microsoft Sentinel based on our users’ reviews in four categories. After reading the collected data, you can find our conclusion below.
Ease of Deployment: AWS Security Hub's initial setup is straightforward, while Microsoft Sentinel's can be more involved. Security Hub is automatically enabled on AWS, but some configuration is still required. In contrast, Sentinel's onboarding and use case development may require ongoing attention. Integrating...
We like that Azure Sentinel does not require as much maintenance as legacy SIEMs that are on-premises. Azure Sentinel is auto-scaling - you will not have to worry about performance impact, you will always have the performance capability you need. If you have Microsoft 365, it is very easy to plug the endpoints into Azure Sentinel. With this solution, you can go on the offensive and stay proactive, continually hunting for threats. Azure Sentinel is purely cloud-based and a leading next-generation SIEM.
We have experienced a few false positives with Azure Sentinel. There is a certain level of expertise that you need to possess to appropriately utilize all of Azure Sentinel's offerings - it can be a somewhat steep learning curve to get things running at capacity. It would be an improvement if Azure Sentinel integrated better with other SaaS providers and offered more out-of-the-box connectors.
You get a huge range of powerful security tools with AWS Security Hub, including compliance scanners, vulnerability endpoint protection, and firewalls. AWS Security Hub
has very good detection and offers helpful real-time alerts. AWS Security Hub aggregates, organizes, and prioritizes security alerts or findings from other AWS services, all in one single pane.
AWS Security Hub lacks a certain level of self-sufficiency, though. We would like to see AWS Security Hub become a multi-cloud solution. AWS Security Hub has some regional restrictions that have proved problematic for us; we need visibility for all instances we have on our account. We found that AWS Security Hub is not a good global product.
Conclusion:
We felt AWS was lacking in some basic features we consider essential, like multi-region coverage. We also wanted a solution that was more intuitive.
We found Azure Sentinel to be a better fit for our team and our clients. We have a global reach and need a product that could satisfy cross-region coverage efficiently. We also feel that Azure Sentinel offers better proactive threat awareness.
Hi @Netanya Carmi ,
Had prepared some comparison factors between AWS and Azure for one of my presales discussions, hope this will hold some insights .So depending on the requirements from the client appropriate solutions can be proposed. Widely Azure Sentinel is what has be going of matching the customer requriements.
Analytics and visualization