Tag Archives: news

AWS Weekly Roundup: Passkey MFA, Malware Protection on Amazon S3, and more (June 17, 2024)

Post Syndicated from Veliswa Boya original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-passkey-mfa-malware-protection-on-amazon-s3-and-more-june-17-2024/

Last week, my alma mater Standard Bank Group (SBG) hosted a Software Engineering Conference and invited me to be one of the keynote speakers. SBG has presence throughout Africa and this hybrid conference was attended by almost 2,000 engineers from across the continent. It was amazing to reconnect with long-time friends and former colleagues, and to make new friends.

Last week’s launches
Here are some launches that got my attention during the previous week.

Passkey multi-factor authentication (MFA) for root and IAM users – We’ve added passkeys to the list of supported multi-factor authentication (MFA) for your root and AWS Identity and Access Management (IAM) users, to give you the convenience of use and easy recoverability. A passkey is a pair of cryptographic keys generated on your client device when you register for a service or a website. Passkeys can be used to replace passwords. However, for this initial release, we choose to use passkeys as a second factor authentication, in addition to your password.

Amazon GuardDuty Malware Protection for Amazon S3 – At AWS re:Inforce 2024 this past week, we announced general availability of Amazon GuardDuty Malware Protection for Amazon Simple Storage Service (Amazon S3). This is an expansion of GuardDuty Malware Protection to detect malicious file uploads to selected S3 buckets. Benefits include the ability to fully manage malware detection without managing compute infrastructure, and coverage summary for all protected buckets in your organization, to name a few. Read more in the post published last week detailing Amazon GuardDuty Malware Protection for Amazon S3.

IAM Access Analyzer Update – More goodness out of AWS re:Inforce 2024 last week! We announced an IAM Access Analyzer Update, which allows you to extend custom policy checks and also includes a guided revocation. This gives you guidance that you can share with your developers so that they can revoke unneeded permissions. My colleague Jeff Barr writes about it in more detail in this post.

Other AWS news
AWS open source news and updates – My colleague Ricardo writes this weekly open source newsletter in which he highlights new open source projects, tools, and demos from the AWS Community.

The .Net developer community remains close to our hearts here at AWS. I’m inspired by my colleagues Brandon Minnick and Francois Bouteruche who support this community, resulting in the AWS and the Azure engineering teams working together to create delightful .NET developer experiences. At the recently held NDC Oslo, which is part of the NDC Conferences hosted around the world, VP of Azure Developer Experience, Scott Hunter, talked about this collaboration during his keynote. Make sure to catch the keynote on the NDC Conferences YouTube Channel as soon as it’s published.

Upcoming AWS events
AWS Summits – These are free online and in-person events that bring the cloud computing community together to connect, collaborate, and learn about AWS. Whether you’re in the Americas, Asia Pacific & Japan, or EMEA region, learn here about future AWS Summit events happening in your area.

AWS Community Days – Join an AWS Community Day event just like the one I mentioned at the beginning of this post to participate in technical discussions, workshops, and hands-on labs led by expert AWS users and industry leaders from your area. If you’re in Sri Lanka, there’s an event happening in your area next week.

You can browse all upcoming in-person and virtual events here.

That’s all for this week. Check back next Monday for another Weekly Roundup!

– Veliswa

This post is part of our Weekly Roundup series. Check back each week for a quick roundup of interesting news and announcements from AWS.

In the Works – AWS Region in Taiwan

Post Syndicated from Channy Yun original https://aws.amazon.com/blogs/aws/in-the-works-aws-region-in-taiwan/

Today, we’re announcing that a new AWS Region will be coming to Taiwan by early 2025. The new AWS Asia Pacific (Taipei) Region will consist of three Availability Zones at launch, and will give AWS customers in Taiwan the ability to run workloads and store data that must remain in Taiwan.

Each of the Availability Zones will be physically independent of the others in the Region – close enough to support applications that need low latency, yet sufficiently distant to significantly reduce the risk that an event at an Availability Zone level might impact business continuity.

The Availability Zones in this Region will be connected together through high-bandwidth, low-latency network connections over dedicated, fully redundant fiber. This connectivity supports applications that need synchronous replication between Availability Zones for availability or redundancy. You can take a peek at the AWS Global Infrastructure page to learn more about how we design and build Regions and Availability Zones.

We are currently working on Regions in Malaysia, Mexico, New Zealand, the Kingdom of Saudi Arabia, Thailand, and the AWS European Sovereign Cloud. The AWS Cloud operates 105 Availability Zones within 33 AWS Regions around the world, with announced plans for 21 more Availability Zones and seven more Regions, including Taiwan.

AWS in Taiwan
AWS has been investing and supporting customers and partners in Taiwan for more than 10 years. To support our customers in Taiwan, we have business development teams, solutions architects, partner managers, professional services consultants, support staff, and various other roles working in our Taipei office.

Other AWS infrastructure includes two Amazon CloudFront edge locations along with access to the AWS global backbone through multiple redundant submarine cables. You can access any other AWS Region (except Beijing and Ningxia) from AWS Direct Connect locations in Taipei, operated by Chief Telecom and Chunghwa Telecom. With AWS Direct Connect, your data that would have previously been transported over the internet is delivered through a private network connection between your facilities and AWS.

You can also use AWS Outposts in Taiwan, a family of fully managed solutions delivering AWS infrastructure and services to virtually any on-premises or edge location for a truly consistent hybrid experience. With AWS Local Zones in Taipei, you can deliver applications that require single-digit millisecond latency to end users.

AWS continues to invest in upskilling students, local developers and technical professionals, nontechnical professionals, and the next generation of IT leaders in Taiwan through offerings like AWS Academy, AWS Educate, and AWS Skill Builder. Since 2017, AWS has trained more than eight million people across the Asia Pacific-Japan region on cloud skills, including more than 100,000 people in Taiwan.

To learn more, join AWS Summit 2024 Taiwan in July; in-person events that bring the cloud computing community together to connect, collaborate, and learn about AWS.

AWS customers in Taiwan
AWS customers in Taiwan have been increasingly moving their applications to AWS and running their technology infrastructure in other AWS Regions around the world. With the addition of this new AWS Region, customers will be able to provide even lower latency to end users and use advanced technologies such as generative artificial intelligence (generative AI), Internet of Things (IoT), mobile services, and more, to drive innovation. This Region will give AWS customers the ability to run their workloads and store their content in Taiwan.

Here are some examples of customers using AWS to drive innovation:

Chunghwa Telecom is the largest integrated telecom provider in Taiwan. To improve AI data security and governance, they use Amazon Bedrock for a variety of generative AI applications, including automatically generating specifications documents for the software development lifecycle and crafting custom marketing campaigns. With Amazon Bedrock, Chunghwa Telecom is saving developer hours and has also developed an immersive, interactive virtual English teacher for the first time.

Gamania Group is a leader in the development and publication of online games in Taiwan. To maximize the value of running on AWS, they worked with AWS Training and Certification to enhance AWS skills across all of its departments, such as AWS Classroom training, AWS Well-Architected Framework, and AWS GameDay events. As a result, they reduced the time needed to make critical operational decisions by 50 percent, lowered its time-to-market by up to 70 percent, and accelerated the launch of new games.

KKCompany Technologies is a multimedia technology group building a music streaming platform, AI-powered streaming solution, and cloud intelligence service in Taiwan. The company specializes in generative AI, multimedia technology, and digital transformation consulting services for enterprises in Taiwan and Japan. You can find BlendVision, a cloud-based streaming solution in AWS Marketplace.

To learn more about Taiwan customer cases, visit AWS Customer Success Stories in English or our Traditional Chinese page.

Stay Tuned
We’ll announce the opening of this and the other Regions in future blog posts, so be sure to stay tuned! To learn more, visit the AWS Region in Taiwan page in Traditional Chinese.

— Channy

AWS Audit Manager extends generative AI best practices framework to Amazon SageMaker

Post Syndicated from Matheus Guimaraes original https://aws.amazon.com/blogs/aws/aws-audit-manager-extends-generative-ai-best-practices-framework-to-amazon-sagemaker/

Sometimes I hear from tech leads that they would like to improve visibility and governance over their generative artificial intelligence applications. How do you monitor and govern the usage and generation of data to address issues regarding security, resilience, privacy, and accuracy or to validate against best practices of responsible AI, among other things? Beyond simply taking these into account during the implementation phase, how do you maintain long-term observability and carry out compliance checks throughout the software’s lifecycle?

Today, we are launching an update to the AWS Audit Manager generative AI best practice framework on AWS Audit Manager. This framework simplifies evidence collection and enables you to continually audit and monitor the compliance posture of your generative AI workloads through 110 standard controls which are pre-configured to implement best practice requirements. Some examples include gaining visibility into potential personally identifiable information (PII) data that may not have been anonymized before being used for training models, validating that multi-factor authentication (MFA) is enforced to gain access to any datasets used, and periodically testing backup versions of customized models to ensure they are reliable before a system outage, among many others. These controls perform their tasks by fetching compliance checks from AWS Config and AWS Security Hub, gathering user activity logs from AWS CloudTrail and capturing configuration data by making application programming interface (API) calls to relevant AWS services. You can also create your own custom controls if you need that level of flexibility.

Previously, the standard controls included with v1 were pre-configured to work with Amazon Bedrock and now, with this new version, Amazon SageMaker is also included as a data source so you may gain tighter control and visibility of your generative AI workloads on both Amazon Bedrock and Amazon SageMaker with less effort.

Enforcing best practices for generative AI workloads
The standard controls included in the “AWS generative AI best practices framework v2” are organized under domains named accuracy, fair, privacy, resilience, responsible, safe, secure and sustainable.

Controls may perform automated or manual checks or a mix of both. For example, there is a control which covers the enforcement of periodic reviews of a model’s accuracy over time. It automatically retrieves a list of relevant models by calling the Amazon Bedrock and SageMaker APIs, but then it requires manual evidence to be uploaded at certain times showing that a review has been conducted for each of them.

You can also customize the framework by including or excluding controls or customizing the pre-defined ones. This can be really helpful when you need to tailor the framework to meet regulations in different countries or update them as they change over time. You can even create your own controls from scratch though I would recommend you search the Audit Manager control library first for something that may be suitable or close enough to be used as a starting point as it could save you some time.

The Control library interface featuring a search box and three tabs: Common, Standard and Custom.

The control library where you can browse and search for common, standard and custom controls.

To get started you first need to create an assessment. Let’s walk through this process.

Step 1 – Assessment Details
Start by navigating to Audit Manager in the AWS Management Console and choose “Assessments”. Choose “Create assessment”; this takes you to the set up process.

Give your assessment a name. You can also add a description if you desire.

Step 1 screen of the assessment creation process. It has a textbox where you must enter a name for your assessment and a description text box where you can optionally enter a description.

Choose a name for this assessment and optionally add a description.

Next, pick an Amazon Simple Storage Service (S3) bucket where Audit Manager stores the assessment reports it generates. Note that you don’t have to select a bucket in the same AWS Region as the assessment, however, it is recommended since your assessment can collect up to 22,000 evidence items if you do so, whereas if you use a cross-Region bucket then that quota is significantly reduced to 3,500 items.

Interface with a textbox where you can type or search for your S3 buckets as well as buttons for browsing and creating a new bucket.

Choose the S3 bucket where AWS Audit Manager can store reports.

Next, we need to pick the framework we want to use. A framework effectively works as a template enabling all of its controls for use in your assessment.

In this case, we want to use the “AWS generative AI best practices framework v2” framework. Use the search box and click on the matched result that pops up to activate the filter.

The Framework searchbox where we typed "gene" which is enough to bring a few results with the top one being "AWS Generative AI Best Practices Framework v2"

Use the search box to find the “AWS generative AI best practices framework V2”

You then should see the framework’s card appear .You can choose the framework’s title, if you wish, to learn more about it and browse through all the included controls.

Select it by choosing the radio button in the card.

A widget containing the framework's title and summary with a radio button that has been checked.

Check the radio button to select the framework.

You now have an opportunity to tag your assessment. Like any other resources, I recommend you tag this with meaningful metadata so review Best Practices for Tagging AWS Resources if you need some guidance.

Step 2 – Specify AWS accounts in scope
This screen is quite straight-forward. Just pick the AWS accounts that you want to be continuously evaluated by the controls in your assessment. It displays the AWS account that you are currently using, by default. Audit Manager does support running assessments against multiple accounts and consolidating the report into one AWS account, however, you must explicitly enable integration with AWS Organizations first, if you would like to use that feature.

Screen displaying all the AWS accounts available for you to select that you want to include in your assessment.

Select the AWS accounts that you want to include in your assessment.

I select my own account as listed and choose “Next”

Step 3 – Specify audit owners
Now we just need to select IAM users who should have full permissions to use and manage this assessment. It’s as simple as it sounds. Pick from a list of identity and access management (IAM) users or roles available or search using the box. It’s recommended that you use the AWSAuditManagerAdministratorAccess policy.

You must select at least one, even if it’s yourself which is what I do here.

Interface for searching and selecting IAM users or roles.

Select IAM users or roles who will have full permissions over this assessment and act as owners.

Step 4 – Review and create
All that is left to do now is review your choices and click on “Create assessment” to complete the process.

Once the assessment is created, Audit Manager starts collecting evidence in the selected AWS accounts and you start generating reports as well as surfacing any non-compliant resources in the summary screen. Keep in mind that it may take up to 24 hours for the first evaluation to show up.

The summary screen for the assessment showing details such as how many controls are available, the status of each control displaying whether they "under review" or their compliance status plus tabs where you can revisit the assessment configuration.

You can visit the assessment details screen at any time to inspect the status for any of the controls.

Conclusion
The “AWS generative AI best practices framework v2” is available today in the AWS Audit Manager framework library in all AWS Regions where Amazon Bedrock and Amazon SageMaker are available.

You can check whether Audit Manager is available in your preferred Region by visiting AWS Services by Region.

If you want to dive deeper, check out a step-by-step guide on how to get started.

Simplify AWS CloudTrail log analysis with natural language query generation in CloudTrail Lake (preview)

Post Syndicated from Esra Kayabali original https://aws.amazon.com/blogs/aws/simplify-aws-cloudtrail-log-analysis-with-natural-language-query-generation-in-cloudtrail-lake-preview/

Today, I am happy to announce in preview the generative artificial intelligence (generative AI)–powered natural language query generation in AWS CloudTrail Lake, which is a managed data lake for capturing, storing, accessing, and analyzing AWS CloudTrail activity logs to meet compliance, security, and operational needs. You can ask a question using natural language about these activity logs (management and data events) stored in CloudTrail Lake without having the technical expertise to write a SQL query or spend time to decode the exact structure of activity events. For example, you might ask, “Tell me how many database instances are deleted without a snapshot”, and the feature will convert that question to a CloudTrail Lake query, which you can run as-is or modify to get the requested event information. Natural language query generation makes the process of exploration of AWS activity logs simpler.

Now, let me show you how to start using natural language query generation.

Getting started with natural language query generation
The natural language query generator uses generative AI to produce a ready-to-use SQL query from your prompt, which you can then choose to run in the query editor of CloudTrail Lake.

In the AWS CloudTrail console, I choose Query under Lake. The query generator can only generate queries for event data stores that collect CloudTrail management and data events. I choose an event data store for my CloudTrail Lake query from the dropdown list in Event data store. In the Query generator, I enter the following prompt in the Prompt field using natural language:

How many errors were logged during the past month?

Then, I choose Generate query. The following SQL query is automatically generated:

SELECT COUNT(*) AS error_count
FROM 8a6***
WHERE eventtime >= '2024-04-21 00:00:00'
    AND eventtime <= '2024-05-21 23:59:59'
    AND (
        errorcode IS NOT NULL
        OR errormessage IS NOT NULL
    )

I choose Run to see the results.

This is interesting, but I want to know more details. I want to see which services had the most errors and why these actions were erroring out. So I enter the following prompt to request additional details:

How many errors were logged during the past month for each service and what was the cause of each error?

I choose Generate query, and the following SQL query is generated:

SELECT eventsource,
    errorcode,
    errormessage,
    COUNT(*) AS errorCount
FROM 8a6***
WHERE eventtime >= '2024-04-21 00:00:00'
    AND eventtime <= '2024-05-21 23:59:59'
    AND (
        errorcode IS NOT NULL
        OR errormessage IS NOT NULL
    )
GROUP BY 1,
    2,
    3
ORDER BY 4 DESC;

I choose Run to see the results.

In the results, I see that my account experiences most number of errors related to Amazon S3, and top errors are related to CORS and object level configuration. I can continue to dig deeper to see more details by asking further questions. But now let me give natural language query generator another instruction. I enter the following prompt in the Prompt field:

What are the top 10 AWS services that I used in the past month? Include event name as well.

I choose Generate query, and the following SQL query is generated. This SQL statement retrieves the field names (eventSource,
eventName, COUNT(*) AS event_count), restricts the rows with the date interval of the past month in the WHERE clause, groups the rows by eventSource and eventName, sorts them by the usage count, and limit the result to 10 rows as I requested in a natural language.

SELECT eventSource,
    eventName,
    COUNT(*) AS event_count
FROM 8a6***
WHERE eventTime >= timestamp '2024-04-21 00:00:00'
    AND eventTime <= timestamp '2024-05-21 23:59:59'
GROUP BY 1,
    2
ORDER BY 3 DESC
LIMIT 10;

Again, I choose Run to see the results.

I now have a better understanding of how many errors were logged during the past month, what service the error was for, and what caused the error. You can try asking questions in plain language and run the generated queries over your logs to see how this feature works with your data.

Join the preview
Natural language query generation is available in preview in the US East (N. Virginia) Region as part of CloudTrail Lake.

You can use natural language query generation in preview for no additional cost. CloudTrail Lake query charges apply when running the query to generate results. For more information, visit AWS CloudTrail Pricing.

To learn more and get started using natural language query generation, visit AWS CloudTrail Lake User Guide.

— Esra

Introducing Amazon GuardDuty Malware Protection for Amazon S3

Post Syndicated from Channy Yun original https://aws.amazon.com/blogs/aws/introducing-amazon-guardduty-malware-protection-for-amazon-s3/

Today we are announcing the general availability of Amazon GuardDuty Malware Protection for Amazon Simple Storage Service (Amazon S3), an expansion of GuardDuty Malware Protection to detect malicious file uploads to selected S3 buckets. Previously, GuardDuty Malware Protection provided agentless scanning capabilities to identify malicious files on Amazon Elastic Block Store (Amazon EBS) volumes attached to Amazon Elastic Compute Cloud (Amazon EC2) and container workloads.

Now, you can continuously evaluate new objects uploaded to S3 buckets for malware and take action to isolate or eliminate any malware found. Amazon GuardDuty Malware Protection uses multiple Amazon Web Services (AWS) developed and industry-leading third-party malware scanning engines to provide malware detection without degrading the scale, latency, and resiliency profile of Amazon S3.

With GuardDuty Malware Protection for Amazon S3, you can use built-in malware and antivirus protection on your designated S3 buckets to help you remove the operational complexity and cost overhead associated with automating malicious file evaluation at scale. Unlike many existing tools used for malware analysis, this managed solution from GuardDuty does not require you to manage your own isolated data pipelines or compute infrastructure in each AWS account and AWS Region where you want to perform malware analysis.

Your development and security teams can work together to configure and oversee malware protection throughout your organization for select buckets where new uploaded data from untrusted entities is required to be scanned for malware. You can configure post-scan action in GuardDuty, such as object tagging, to inform downstream processing, or consume the scan status information provided through Amazon EventBridge to implement isolation of malicious uploaded objects.

Getting started with GuardDuty Malware Protection for your S3 bucket
To get started, in the GuardDuty console, select Malware Protection for S3 and choose Enable.

Enter the S3 bucket name or choose Browse S3 to select an S3 bucket name from a list of buckets that belong to the currently selected Region. You can select All the objects in the S3 bucket when you want GuardDuty to scan all the newly uploaded objects in the selected bucket. Or you can also select Objects beginning with a specific prefix when you want to scan the newly uploaded objects that belong to a specific prefix.

After scanning a newly uploaded S3 object, GuardDuty can add a predefined tag with the key as GuardDutyMalwareScanStatus and the value as the scan status:

  • NO_THREATS_FOUND – No threat found in the scanned object.
  • THREATS_FOUND – Potential threat detected during scan.
  • UNSUPPORTED – GuardDuty cannot scan this object because of size.
  • ACCESS_DENIED – GuardDuty cannot access object. Check permissions.
  • FAILED – GuardDuty could not scan the object.

When you want GuardDuty to add tags to your scanned S3 objects, select Tag objects. If you use tags, you can create policies to prevent objects from being accessed before the malware scan completes and prevent your application from accessing malicious objects.

Now, you must first create and attach an AWS Identity and Access Management (IAM) role that includes the required permissions:

  • EventBridge actions to create and manage the EventBridge managed rule so that Malware Protection for S3 can listen to your S3 Event Notifications.
  • Amazon S3 and EventBridge actions to send S3 Event Notifications to EventBridge for all events in this bucket.
  • Amazon S3 actions to access the uploaded S3 object and add a predefined tag to the scanned S3 object.
  • AWS Key Management Service (AWS KMS) key actions to access the object before scanning and putting a test object on buckets with the supported DSSE-KMS and SSE-KMS

To add these permissions, choose View permissions and copy the policy template and trust relationship template. These templates include placeholder values that you should replace with the appropriate values associated with your bucket and AWS account. You should also replace the placeholder value for the AWS KMS key ID.

Now, choose Attach permissions, which opens the IAM console in a new tab. You can choose to create a new IAM role or update an existing IAM role with the permissions from the copied templates. If you want to create or update your IAM role in advance, visit Prerequisite – Create or update IAM PassRole policy in the AWS documentation.

Finally, go back to the GuardDuty browser tab that has the IAM console open, choose your created or updated IAM role, and choose Enable.

Now, you will see Active in the protection Status column for this protected bucket.

Choose View all S3 malware findings to see the generated GuardDuty findings associated with your scanned S3 bucket. If you see the finding type S3Object:S3/MaliciousFile, GuardDuty has detected the listed S3 object as malicious. Choose the Threats detected section in the Findings details panel and follow the recommended remediation steps. To learn more, visit Remediating a potentially malicious S3 object in the AWS documentation.

Things to know
You can set up GuardDuty Malware Protection for your S3 buckets even without GuardDuty enabled for your AWS account. However, if you enable GuardDuty in your account, you can use the full monitoring of foundational sources, such as AWS CloudTrail management events, Amazon Virtual Private Cloud (Amazon VPC) Flow Logs, and DNS query logs, as well as malware protection features. You can also have security findings sent to AWS Security Hub and Amazon Detective for further investigation.

GuardDuty can scan files belonging to the following synchronous Amazon S3 storage classes: S3 Standard, S3 Intelligent-Tiering, S3 Standard-IA, S3 One Zone-IA, and Amazon S3 Glacier Instant Retrieval. It will scan the file formats known to be used to spread or contain malware. At the launch, the feature supports file sizes up to 5 GB, including archive files with up to five levels and 1,000 files per level after it is decompressed.

As I said, GuardDuty will send scan metrics to your EventBridge for each protected S3 bucket. You can set up alarms and define post-scan actions, such as tagging the object or moving the malicious object to a quarantine bucket. To learn more about other monitoring options, such as Amazon CloudWatch metrics and S3 object tagging, visit Monitoring S3 object scan status in the AWS documentation.

Now available
Amazon GuardDuty Malware Protection for Amazon S3 is generally available today in all AWS Regions where GuardDuty is available, excluding China Regions and GovCloud (US) Regions.

The pricing is based on the GB volume of the objects scanned and number of objects evaluated per month. This feature comes with a limited AWS Free Tier, which includes 1,000 requests and 1 GB each month, pursuant to conditions for the first 12 months of account creation for new AWS accounts, or until June 11, 2025, for existing AWS accounts. To learn more, visit the Amazon GuardDuty pricing page.

Give GuardDuty Malware Protection for Amazon S3 a try in the GuardDuty console. For more information, visit the Amazon GuardDuty User Guide and send feedback to AWS re:Post for Amazon GuardDuty or through your usual AWS support contacts.

— Channy

IAM Access Analyzer Update: Extending custom policy checks & guided revocation

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/iam-access-analyzer-update-extending-custom-policy-checks-guided-revocation/

We are making IAM Access Analyzer even more powerful, extending custom policy checks and adding easy access to guidance that will help you to fine-tune your IAM policies. Both of these new features build on the Custom Policy Checks and the Unused Access analysis that were launched at re:Invent 2023. Here’s what we are launching:

New Custom Policy Checks – Using the power of automated reasoning, the new checks help you to detect policies that grant access to specific, critical AWS resources, or that grant any type of public access. Both of the checks are designed to be used ahead of deployment, possibly as part of your CI/CD pipeline, and will help you proactively detect updates that do not conform to your organization’s security practices and policies.

Guided Revocation – IAM Access Analyzer now gives you guidance that you can share with your developers so that they can revoke permissions that grant access that is not actually needed. This includes unused roles, roles with unused permissions, unused access keys for IAM users, and unused passwords for IAM users. The guidance includes the steps needed to either remove the extra items or to replace them with more restrictive ones.

New Custom Policy Checks
The new policy checks can be invoked from the command line or by calling an API function. The checks examine a policy document that is supplied as part of the request and return a PASS or FAIL value. In both cases, PASS indicates that the policy document properly disallows the given access, and FAIL indicates that the policy might allow some or all of the permissions. Here are the new checks:

Check No Public Access – This check operates on a resource policy, and checks to see if the policy grants public access to a specified resource type. For example, you can check a policy to see if it allows public access to an S3 bucket by specifying the AWS::S3::Bucket resource type. Valid resource types include DynamoDB tables and streams, EFS file systems, OpenSearch domains, Kinesis streams and stream consumers, KMS keys, Lambda functions, S3 buckets and access points, S3 Express directory buckets, S3 Outposts buckets and access points, Glacier, Secrets Manager secrets, SNS topics and queues, and IAM policy documents that assume roles. The list of valid resource types will expand over time and can be found in the CheckNoPublicAccess documentation,

Let’s say that I have a policy which accidentally grants public access to an Amazon Simple Queue Service (Amazon SQS) queue. Here’s how I check it:

$ aws accessanalyzer check-no-public-access --policy-document file://resource.json \
  --resource-type AWS::SQS::Queue --output json

And here is the result:

{
    "result": "FAIL",
    "message": "The resource policy grants public access for the given resource type.",
    "reasons": [
        {
            "description": "Public access granted in the following statement with sid: SqsResourcePolicy.",
            "statementIndex": 0,
            "statementId": "SqsResourcePolicy"
        }
    ]
}

I edit the policy to remove the access grant and try again, and this time the check passes:

{
    "result": "PASS",
    "message": "The resource policy does not grant public access for the given resource type."
}

Check Access Not Granted – This check operates on a single resource policy or identity policy at a time. It also accepts an list of actions and resources, both in the form that are acceptable as part of an IAM policy. The check sees if the policy grants unintended access to any of the resources in the list by way of the listed actions. For example, this check could be used to make sure that a policy does not allow a critical CloudTrail trail to be deleted:

$ aws accessanalyzer check-access-not-granted --policy-document file://ct.json \
  --access resources="arn:aws:cloudtrail:us-east-1:123456789012:trail/MySensitiveTrail" \
  --policy-type IDENTITY_POLICY --output json

IAM Access Analyzer indicates that the check fails:

{
    "result": "FAIL",
    "message": "The policy document grants access to perform one or more of the listed actions or resources.",
    "reasons": [
        {
            "description": "One or more of the listed actions or resources in the statement with index: 0.",
            "statementIndex": 0
        }
    ]
}

I fix the policy and try again, and this time the check passes, indicating that the policy does not grant access to the listed resources:

{
    "result": "PASS",
    "message": "The policy document does not grant access to perform the listed actions or resources."
}

Guided Revocation
In my earlier post I showed you how IAM Access Analyzer discovers and lists IAM items that grant access which is not actually needed. With today’s launch, you now get guidance to help you (or your developer team) to resolve these findings. Here are the latest findings from my AWS account:

Some of these are leftovers from times when I was given early access to a service so that I could use and then blog about it; others are due to my general ineptness as a cloud admin! Either way, I need to clean these up. Let’s start with the second one, Unused access key. I click on the item and can see the new Recommendations section at the bottom:

I can follow the steps and delete the access key or I can click Archive to remove the finding from the list of active findings and add it to the list of archived ones. I can also create an archive rule that will do the same for similar findings in the future. Similar recommendations are provided for unused IAM users, IAM roles, and passwords.

Now let’s take a look at a finding of Unused permissions:

The recommendation is to replace the existing policy with a new one. I can preview the new policy side-by-side with the existing one:

As in the first example I can follow the steps or I can archive the finding.

The findings and the recommendations are also available from the command line. I generate the recommendation by specifying an analyzer and a finding from it:

$ aws accessanalyzer generate-finding-recommendation \
  --analyzer-arn arn:aws:access-analyzer-beta:us-west-2:123456789012:analyzer/MyAnalyzer \
  --id 67110f3e-05a1-4562-b6c2-4b009e67c38e

Then I retrieve the recommendation. In this example, I am filtering the output to only show the steps since the entire JSON output is fairly rich:

$ aws accessanalyzer get-finding-recommendation \
  --analyzer-arn arn:aws:access-analyzer-beta:us-west-2:123456789012:analyzer/MyAnalyzer \
  --id 67110f3e-05a1-4562-b6c2-4b009e67c38e --output json | \
  jq .recommendedSteps[].unusedPermissionsRecommendedStep.recommendedAction
"CREATE_POLICY"
"DETACH_POLICY"

You can use these commands (or the equivalent API calls) to integrate the recommendations into your own tools and systems.

Available Now
The new checks and the resolution steps are available now and you can start using them today in all public AWS regions!

— Jeff;

AWS adds passkey multi-factor authentication (MFA) for root and IAM users

Post Syndicated from Sébastien Stormacq original https://aws.amazon.com/blogs/aws/aws-adds-passkey-multi-factor-authentication-mfa-for-root-and-iam-users/

Security is our top priority at Amazon Web Services (AWS), and today, we’re launching two capabilities to help you strengthen the security posture of your AWS accounts:

MFA is one of the simplest and most effective ways to enhance account security, offering an additional layer of protection to help prevent unauthorized individuals from gaining access to systems or data.

MFA with passkey for your root and IAM users
Passkey is a general term used for the credentials created for FIDO2 authentication.

A passkey is a pair of cryptographic keys generated on your client device when you register for a service or a website. The key pair is bound to the web service domain and unique for each one.

The public part of the key is sent to the service and stored on their end. The private part of the key is either stored in a secured device, such as a security key, or securely shared across your devices connected to your user account when you use cloud services, such as iCloud Keychain, Google accounts, or a password manager such as 1Password.

Typically, the access to the private part of the key is protected by a PIN code or a biometric authentication, such as Apple Face ID or Touch ID or Microsoft Hello, depending on your devices.

When I try to authenticate on a service protected with passkeys, the service sends a challenge to my browser. The browser then requests my device sign the challenge with my private key. This triggers a PIN or biometric authentication to access the secured storage where the private key is stored. The browser returns the signature to the service. When the signature is valid, it confirms I own the private key that matches the public key stored on the service, and the authentication succeeds.

You can read more about this process and the various standards at work (FIDO2, CTAP, WebAuthn) in the post I wrote when AWS launched support for passkeys in AWS IAM Identity Center back in November 2020.

Passkeys can be used to replace passwords. However, for this initial release, we choose to use passkeys as a second factor authentication, in addition to your password. The password is something you know, and the passkey is something you have.

Passkeys are more resistant to phishing attacks than passwords. First, it’s much harder to gain access to a private key protected by your fingerprint, face, or a PIN code. Second, passkeys are bound to a specific web domain, reducing the scope in case of unintentional disclosure.

As an end user, you will benefit from the convenience of use and easy recoverability. You can use the built-in authenticators in your phones and laptops to unlock a cryptographically secured credential to your AWS sign-in experience. And when using a cloud service to store the passkey (such as iCloud keychain, Google accounts, or 1Password), the passkey can be accessed from any of your devices connected to your passkey provider account. This helps you to recover your passkey in the unfortunate case of losing a device.

How to enable passkey MFA for an IAM user
To enable passkey MFA, I navigate to the AWS Identity and Access Management (IAM) section of the console. I select a user, and I scroll down the page to the Multi-factor authentication (MFA) section. Then, I select Assign MFA device.

Note that to help you increase resilience and account recovery, you can have multiple MFA devices enabled for a user.

Enable MFA in AM console

On the next page, I enter an MFA device name, and I select Passkey or security key. Then, I select next.

enable MFA : select passkey

When using a password manager application that supports passkeys, it will pop up and ask if you want to generate and store a passkey using that application. Otherwise, your browser will present you with a couple of options. The exact layout of the screen depends on the operating system (macOS or Windows) and the browser you use. Here is the screen I see on macOS with a Chromium-based browser.

Enable passkey : choose method

The rest of the experience depends on your selection. iCloud Keychain will prompt you for a Touch ID to generate and store the passkey.

In the context of this demo, I want to show you how to bootstrap the passkey on another device, such as a phone. I therefore select Use a phone, tablet, or security key instead. The browser presents me with a QR code. Then, I use my phone to scan the QR code. The phone authenticates me with Face ID and generates and stores the passkey.

Passkey : scan a QR code

This QR code-based flow allows a passkey from one device to be used to sign in on another device (a phone and my laptop in my demo). It is defined by the FIDO specification and known as cross device authentication (CDA).

When everything goes well, the passkey is now registered with the IAM user.

Enable passkey : success

Note that we don’t recommend using IAM users to authenticate human beings to the AWS console. We recommend configuring single sign-on (SSO) with AWS IAM Identity Center instead.

What’s the sign-in experience?
Once MFA is enabled and configured with a passkey, I try to sign in to my account.

The user experience differs based on the operating system, browser, and device you use.

For example, on macOS with iCloud Keychain enabled, the system prompts me for a touch on the Touch ID key. For this demo, I registered the passkey on my phone using CDA. Therefore, the system asks me to scan a QR code with my phone. Once scanned, the phone authenticates me with Face ID to unlock the passkey, and the AWS console terminates the sign-in procedure.

Authenticate with MFA and passkey

Enforcing MFA for root users
The second announcement today is that we have started to enforce the use of MFA for the root user on some AWS accounts. This change was announced last year in a blog post from Stephen Schmidt, Chief Security Officer at Amazon.

To quote Stephen:

Verifying that the most privileged users in AWS are protected with MFA is just the latest step in our commitment to continuously enhance the security posture of AWS customers.

We started with your most sensitive account: your management account for AWS Organizations. The deployment of the policy is progressive, with just a few thousand accounts at a time. Over the coming months, we will progressively deploy the MFA enforcement policy on root users for the majority of the AWS accounts.

When you don’t have MFA enabled on your root user account, and your account is updated, a new message will pop up when you sign in, asking you to enable MFA. You will have a grace period, after which the MFA becomes mandatory.

Enable MFA on root account

You can start to use passkeys for multi-factor authentication today in all AWS Regions, except in China.

We’re enforcing the use of multi-factor authentication in all AWS Regions, except for the two regions in China (Beijing, Ningxia) and for AWS GovCloud (US), because the AWS accounts in these Regions have no root user.

Now go activate passkey MFA for your root user in your accounts.

— seb

AWS Weekly Roundup: New AWS Heroes, Amazon API Gateway, Amazon Q and more (June 10, 2024)

Post Syndicated from Donnie Prakoso original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-new-aws-heroes-amazon-api-gateway-amazon-q-and-more-june-10-2024/

In the last AWS Weekly Roundup, Channy reminded us on how life has ups and downs. It’s just how life is. But, that doesn’t mean that we should do it alone. Farouq Mousa, AWS Community Builder, is fighting brain cancer and Allen Helton, AWS Serverless Hero, his daughter is fighting leukemia.

If you have a moment, please visit their campaign pages and give your support.

Meanwhile, we’ve just finished a few AWS Summits in India, Korea and also Thailand. As always, I had so much fun working together at Developer Lounge with AWS Heroes, AWS Community Builders, and AWS User Group leaders. Here’s a photo from everyone here.

Last Week’s Launches
Here are some launches that caught my attention last week:

Welcome, new AWS Heroes! — Last week, we just announced new cohort for AWS Heroes, worldwide group of AWS experts who go above and beyond to share knowledge and empower their communities.

Amazon API Gateway increased integration timeout limit — If you’re using Regional REST APIs and private REST APIs in Amazon API Gateway, now you can increase the integration timeout limit greater than 29 seconds. This allows you to run various workloads requiring longer timeouts.

Amazon Q offers inline completion in the command line — Now, Amazon Q Developer provides real-time AI-generated code suggestions as you type in your command line. As a regular command line interface (CLI) user, I’m really excited about this.

New common control library in AWS Audit Manager — This announcement helps you to save time when mapping enterprise controls into AWS Audit Manager. Check out Danilo’s post where he elaborated how that you can simplify risk and complicance assessment with the new common control library.

Amazon Inspector container image scanning for Amazon CodeCatalyst and GitHub actions — If you need to integrate your CI/CD with software vulnerabilities checking, you can use Amazon Inspector. Now, with this native integration in GitHub actions and Amazon CodeCatalyst, it streamlines your development pipeline process.

Ingest streaming data with Amazon OpenSearch Ingestion and Amazon Managed Streaming for Apache Kafka — With this new capability, now you can build more efficient data pipelines for your complex analytics use cases. Now, you can seamlessly index the data from your Amazon MSK Serverless clusters in Amazon OpenSearch service.

Amazon Titan Text Embeddings V2 now available in Amazon Bedrock Knowledge Base — You now can embed your data into a vector database using Amazon Titan Text Embeddings V2. This will be helpful for you to retrieve relevant information for various tasks.

Max tokens 8,192
Languages 100+ in pre-training
Fine-tuning supported No
Normalization supported Yes
Vector size 256, 512, 1,024 (default)

From Community.aws
Here’s my 3 personal favorites posts from community.aws:

Upcoming AWS events
Check your calendars and sign up for these AWS and AWS Community events:

  • AWS Summits — Join free online and in-person events that bring the cloud computing community together to connect, collaborate, and learn about AWS. Register in your nearest city: Japan (June 20), Washington, DC (June 26–27), and New York (July 10).

  • AWS re:Inforce — Join us for AWS re:Inforce (June 10–12) in Philadelphia, PA. AWS re:Inforce is a learning conference focused on AWS security solutions, cloud security, compliance, and identity. Connect with the AWS teams that build the security tools and meet AWS customers to learn about their security journeys.

  • AWS Community Days — Join community-led conferences that feature technical discussions, workshops, and hands-on labs led by expert AWS users and industry leaders from around the world: Midwest | Columbus (June 13), Sri Lanka (June 27), Cameroon (July 13), New Zealand (August 15), Nigeria (August 24), and New York (August 28).

You can browse all upcoming in-person and virtual events.

That’s all for this week. Check back next Monday for another Weekly Roundup!

— Donnie

This post is part of our Weekly Roundup series. Check back each week for a quick roundup of interesting news and announcements from AWS!

Simplify risk and compliance assessments with the new common control library in AWS Audit Manager

Post Syndicated from Danilo Poccia original https://aws.amazon.com/blogs/aws/simplify-risk-and-compliance-assessments-with-the-new-common-control-library-in-aws-audit-manager/

With AWS Audit Manager, you can map your compliance requirements to AWS usage data and continually audit your AWS usage as part of your risk and compliance assessment. Today, Audit Manager introduces a common control library that provides common controls with predefined and pre-mapped AWS data sources.

The common control library is based on extensive mapping and reviews conducted by AWS certified auditors, verifying that the appropriate data sources are identified for evidence collection. Governance, Risk and Compliance (GRC) teams can use the common control library to save time time when mapping enterprise controls into Audit Manager for evidence collection, reducing their dependence on information technology (IT) teams.

Using the common control library, you can view the compliance requirements for multiple frameworks (such as PCI or HIPAA) associated with the same common control in one place, making it easier to understand your audit readiness across multiple frameworks simultaneously. In this way, you don’t need to implement different compliance standard requirements individually and then review the resulting data multiple times for different compliance regimes.

Additionally, by using controls from this library, you automatically inherit improvements as Audit Manager updates or adds new data sources, such as additional AWS CloudTrail events, AWS API calls, AWS Config rules, or maps additional compliance frameworks to common controls. This eliminates the efforts required by GRC and IT teams to constantly update and manage evidence sources and makes it easier to benefit from additional compliance frameworks that Audit Manager adds to its library.

Let’s see how this works in practice with an example.

Using AWS Audit Manager common control library
A common scenario for an airline is to implement a policy so that their customer payments, including in-flight meals and internet access, can only be taken via credit card. To implement this policy, the airline develops an enterprise control for IT operations that says that “customer transactions data is always available.” How can they monitor whether their applications on AWS meet this new control?

Acting as their compliance officer, I open the Audit Manager console and choose Control library from the navigation bar. The control library now includes the new Common category. Each common control maps to a group of core controls that collect evidence from AWS managed data sources and makes it easier to demonstrate compliance with a range of overlapping regulations and standards. I look through the common control library and search for “availability.” Here, I realize the airline’s expected requirements map to common control High availability architecture in the library.

Console screenshot.

I expand the High availability architecture common control to see the underlying core controls. There, I notice this control doesn’t adequately meet all the company’s needs because Amazon DynamoDB is not in this list. DynamoDB is a fully managed database, but given extensive usage of DynamoDB in their application architecture, they definitely want their DynamoDB tables to be available when their workload grows or shrinks. This might not be the case if they configured a fixed throughput for a DynamoDB table.

I look again through the common control library and search for “redundancy.” I expand the Fault tolerance and redundancy common control to see how it maps to core controls. There, I see the Enable Auto Scaling for Amazon DynamoDB tables core control. This core control is relevant for the architecture that the airline has implemented but the whole common control is not needed.

Console screenshot.

Additionally, common control High availability architecture already includes a couple of core controls that check that Multi-AZ replication on Amazon Relational Database Service (RDS) is enabled, but these core controls rely on an AWS Config rule. This rule doesn’t work for this use case because the airline does not use AWS Config. One of these two core controls also uses a CloudTrail event, but that event does not cover all scenarios.

Console screenshot.

As the compliance officer, I would like to collect the actual resource configuration. To collect this evidence, I briefly consult with an IT partner and create a custom control using a Customer managed source. I select the api-rds_describedbinstances API call and set a weekly collection frequency to optimize costs.

Console screenshot.

Implementing the custom control can be handled by the compliance team with minimal interaction needed from the IT team. If the compliance team has to reduce their reliance on IT, they can implement the entire second common control (Fault tolerance and redundancy) instead of only selecting the core control related to DynamoDB. It might be more than what they need based on their architecture, but the acceleration of velocity and reduction of time and effort for both the compliance and IT teams is often a bigger benefit than optimizing the controls in place.

I now choose Framework library in the navigation pane and create a custom framework that includes these controls. Then, I choose Assessments in the navigation pane and create an assessment that includes the custom framework. After I create the assessment, Audit Manager starts collecting evidence about the selected AWS accounts and their AWS usage.

By following these steps, a compliance team can precisely report on the enterprise control “customer transactions data is always available” using an implementation in line with their system design and their existing AWS services.

Things to know
The common control library is available today in all AWS Regions where AWS Audit Manager is offered. There is no additional cost for using the common control library. For more information, see AWS Audit Manager pricing.

This new capability streamlines the compliance and risk assessment process, reducing the workload for GRC teams and simplifying the way they can map enterprise controls into Audit Manager for evidence collection. To learn more, see the AWS Audit Manager User Guide.

— Danilo

Zabbix 7.0 – Everything You Need to Know

Post Syndicated from Michael Kammer original https://blog.zabbix.com/zabbix-7-0-everything-you-need-to-know/28210/

After plenty of breathless anticipation, we’re proud to announce the release of the latest major Zabbix version – the new and improved Zabbix 7.0 LTS. This release is the direct result of user feedback and delivers a variety of improvements, including cloud-native Zabbix proxy scalability, website transaction monitoring, improved data collection speed and scalability, new dashboard widgets, major network discovery speed improvements, new templates and integrations, and more!

Without further ado, let’s take a whistle-stop tour of what you need to know:

Synthetic end-user web monitoring

Busy enterprises can now monitor multiple websites and applications by defining flexible multi-step browser-based scenarios. 7.0 LTS also makes it easy to capture screenshots of the current website state, collect and visualize website performance and availability metrics, extract, monitor, and analyze web application data, and get alerts when issues are discovered.

Zabbix proxy high availability and load balancing

When it’s time to expand, Zabbix 7.0 LTS makes it easy to scale a Zabbix environment, guaranteeing 100% availability with automatic proxy load balancing and high availability features, including the ability to assign hosts to load-balanced proxy groups and seamlessly scale a Zabbix environment by deploying additional proxies.

Faster, more efficient Zabbix proxies

Zabbix proxy now fully supports in-memory data storage for collected metrics. Users can choose from Disk, Memory, and Hybrid proxy buffer modes, all of which are ideal for embedded hardware. In addition, memory mode enables the support of edge computing use cases. Users can expect 10-100x better proxy performance by switching to memory or hybrid modes, depending on allocated hardware.

Centralized control of data collection timeouts

Centralizing control of data collection timeouts enables better support for metrics and custom checks, taking longer data collection time intervals. Data collection timeouts can be defined per item-type and overridden per proxy or on an individual item level. In addition, timeouts are now fully configurable in the Zabbix GUI or via Zabbix API.

Faster and more scalable data collection

Synchronous poller processes have been replaced with asynchronous pollers, which improves the speed and scalability of metric polling, particularly for agent, SNMP, and HTTP checks. The next metric can now be polled before waiting for a response from a previously requested metric, and up to 1,000 concurrent checks can now be supported per poller process.

New ways to visualize data

A variety of new dashboard widgets have been introduced, with the goal of giving users detailed information about their monitored metrics and infrastructure at a glance.

Dynamic dashboard widget navigation

Speaking of dashboard widgets, a new communication framework has also been introduced for dashboard widgets, enabling communication between widgets, allowing a widget to serve as a data source for other widgets, and dynamically updating information displayed in a dashboard widget based on the data source.

Faster network discovery

Discovering services and hosts has never been easier, thanks to support of parallelization while performing network discovery. Concurrency support allows for massive improvements in network discovery speed and simplifies host and service discovery while scanning large network segments.

Better security via enterprise-grade multi-factor authentication

Out-of-the box support of multi-factor authentication enables enterprise-grade security and added flexibility for configuring user authentication methods. Support MFA providers include time-based one-time Password (TOTP) and Duo Universal Prompt authentication.

More flexible resource discovery and management

Low-level discovery has received a variety of improvements, which enable enhanced host configuration and management flexibility when discovering hosts in complex environments, such as VMware or Kubernetes.

New templates and integrations

In response to user demand, Zabbix 7.0 LTS comes pre-packaged with a range of new templates for the most popular vendors and cloud providers.

Zabbix 7.0 training updates

All Zabbix training materials have been updated based on the new functionalities that have been added to the product since Zabbix 6.0.

Everyone is welcome to sharpen their skills, but if you’re a Zabbix 6.0 Certified Specialist or Certified Professional you can master Zabbix 7.0 LTS in just one day with our Upgrade Courses. As a 7.0 Specialist, you’ll be able to automate user provisioning with the Just-in-time (JIT) feature, monitor websites with new synthetic end-user monitoring, leverage new visualization features, and enhance the speed and performance of your data collection.

The 7.0 Certified Professional course covers proxy group configuration with high availability and load balancing, improved proxy data collection, new SNMP bulk monitoring, and enhanced host discovery for VMware, Kubernetes, and Cloud infrastructures.

We’re also happy to organize private trainings for organizations of any size, so don’t hesitate to get in touch!

Upcoming 7.0 events

If you’re looking for more information regarding Zabbix 7.0, you’re in luck! You can tune in to the “What’s new in Zabbix 7.0” webinar on June 11 at 12 PM CST or June 12 at 10 AM EEST. If you’d prefer a more hands-on approach, the following workshops are also available:

• “Zabbix Proxy High-availability and Load Balancing” (June 18, 6 PM EEST)
• “New Web Monitoring Features in Zabbix 7.0” (June 20, 6 PM EEST)

While you’re at it, feel free to explore Zabbix 7.0 LTS webinars and workshops in other languages. You can also check out worldwide events related to Zabbix 7.0 LTS, including our free in-person meetup in Riga on June 19 and Zabbix Summit 2024 this fall. 

Ready to upgrade or migrate?

With a brand-new version out, there’s never been a better time to take advantage of our upgrade or migration services. Let our team take the risk out of migrating or upgrading to 7.0, giving you the latest version at a lower cost and with minimal disruption to your organization.

Need a consultation about the latest version?

Not sure about how to get the most out of Zabbix 7.0? Our expert consultants can answer any questions related to the architecture of your infrastructure, the implementation of a back-up strategy, and your capacity planning, while providing strategic advice on which 7.0 services are right for you.

Make your contribution as a translator

The Documentation 7.0 translation project is now live, which means that you can help localize Zabbix 7.0 documentation in multiple languages. Your efforts will help make Zabbix accessible to users around the globe, and you’ll also receive a reward for your contributions. The guidelines, which contain essential information about the project, are available here.

Useful links

To see what else is in store for the future, have a look at the Zabbix roadmap.

You can find the instructions and download the new version on the Download page.

Detailed, step-by-step upgrade instructions are available on our Upgrade procedure page.

Learn about new features and changes introduced in Zabbix 7.0 LTS by visiting the What’s new in Zabbix 7.0 page.

The What’s new documentation section provides a detailed description of the new features.

Take a look at the release notes to see the full list of new features and improvements.

 

The post Zabbix 7.0 – Everything You Need to Know appeared first on Zabbix Blog.

Our brand-new cohort of AWS Heroes has arrived – June 2024

Post Syndicated from Taylor Jacobsen original https://aws.amazon.com/blogs/aws/our-brand-new-cohort-of-aws-heroes-has-arrived-june-2024/

The vibrant AWS community is made up of millions of builders worldwide. Within this global audience, there are technical enthusiasts who are going above and beyond to solve problems and generously share their learnings and best practices to empower others—the AWS Heroes. These inspirational leaders make significant contributions, and the AWS Heroes program is our way of recognizing and highlighting their impactful efforts.

Please join us in celebrating our newest group of AWS Heroes!

Arshad Zackeriya – Wellington, New Zealand

Community Hero Arshad Zackeriya is a Senior Engineer at Xero, specializing in empowering organizations to deliver software at high velocity. He is well-known in the community as “Zack,” and his expertise primarily centers around Amazon EKS and developer tools. Zack is also a public speaker, and serves as one of the co-organizers and leaders for the Wellington Chapter in the AWS User Group Aotearoa New Zealand. Additionally, he was an AWS New Voices Coach and was an AWS Community Builder for five consecutive years, earning recognition as a nominee for the AWS Community Builder of the Year for 2022 and 2023 in the APJ region.

Julia Furst Morgado – New York, USA

Container Hero Julia Furst Morgado is a Global Technologist on the Product Strategy team in the Office of the CTO at Veeam Software. She is committed to diversity and inclusion, and her passion lies in making Cloud Native technologies and DevOps best practices easier to understand by sharing her knowledge. Julia excels in evangelizing and creating engaging content focused on Amazon EKS, and presenting at major events about Amazon EKS Blueprints and Amazon EKS security. Additionally, she co-organizes the AWS Community Day New York, Kubernetes Community Days, and the AWS User Group Lisbon – Women in Tech chapter, fostering vibrant collaboration and learning opportunities.

Paloma Lataliza – Belo Horizonte, Brazil

Community Hero Paloma Lataliza is a Cloud Engineer with over six years of experience. She has a bachelor’s degree in Computer Science, specialized in Cloud Computing, is an enthusiast of container technologies and passionate about technology and sharing knowledge. Paloma is a leader of the AWS User Group Minas Gerais, and she is dedicated to mentoring women by providing a supportive network and offering them free classes to make tech more accessible. This is further demonstrated as the organizer of the AWSome Women Community Summit Brazil, and founder of the Mulheres na Nuvem Minas Gerais (Women in the Cloud Minas Gerais) project. Previously, she was an AWS Community Builder, producing technical content, speaking at Cloud and DevOps events, and mentoring those eager to deepen their technical skills.

Shaoyi Li – Shenzhen, China

Community Hero Shaoyi Li is a Lead Cloud Engineer focusing on cybersecurity and generative AI, advocating for cloud generative AI security and governance solutions to help the community build secure, compliant, and responsible generative AI applications. He is a regular speaker at AWS events, such as AWS Summits, AWS Community Days, and AWS User Group Meetups. Shaoyi also shares his insights into AWS technologies through various channels, including AWS case studies, AWS blogs, AWS WeChat channels, community.aws, and on his social networks.

Vishal Alhat – Pune, India

Community Hero Vishal Alhat is a Senior Software Engineer at Forcepoint, a leading cybersecurity company, where he leverages his 9+ years of experience to play a key role in cloud-based deployments. He focuses on cloud security and DevOps using AWS, implementing DevOps tools, AWS services, and best practices to automate deployments and ensure consistency across Forcepoint’s cloud infrastructure. Vishal is passionate about sharing his knowledge and was selected as the AWS Community Builder of the Year for the APJ region, which is a testament to his dedication. Furthermore, he is the AWS User Group Pune leader, and regularly speaks at conferences, meetups, AWS Community Days, and AWS Summits worldwide.

Learn More

Please visit the AWS Heroes website if you’d like to learn more about the AWS Heroes program or to connect with a Hero near you.

— Taylor

AWS Weekly Roundup: Amazon EC2 U7i Instances, Bedrock Converse API, AWS World IPv6 Day and more (June 3, 2024)

Post Syndicated from Channy Yun original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-amazon-ec2-u7i-instances-bedrock-converse-api-aws-world-ipv6-day-and-more-june-3-2024/

Life is not always happy, there are difficult times. However, we can share our joys and sufferings with those we work with. The AWS Community is no exception.

Jeff Barr introduced two members of the AWS community who are dealing with health issues. Farouq Mousa is an AWS Community Builder and fighting brain cancer. Allen Helton is an AWS Serverless Hero and his young daughter is fighting leukemia.

Please donate to support Farauq and Olivia, Allen’s daughter to overcome their disease.

Last week’s launches
Here are some launches that got my attention:

Amazon EC2 high memory U7i Instances – These instances with up to 32 TiB of DDR5 memory and 896 vCPUs are powered by custom fourth generation Intel Xeon Scalable Processors (Sapphire Rapids). These high memory instances are designed to support large, in-memory databases including SAP HANA, Oracle, and SQL Server. To learn more, visit Jeff’s blog post.

New Amazon Connect analytics data lake – You can use a single source for contact center data including contact records, agent performance, Contact Lens insights, and more — eliminating the need to build and maintain complex data pipelines. Your organization can create your own custom reports using Amazon Connect data or combine data queried from third-party sources. To learn more, visit Donnie’s blog post.

Amazon Bedrock Converse API – This API provides developers a consistent way to invoke Amazon Bedrock models removing the complexity to adjust for model-specific differences such as inference parameters. With this API, you can write a code once and use it seamlessly with different models in Amazon Bedrock. To learn more, visit Dennis’s blog post to get started.

New Document widget for PartyRock – You can build, use, and share generative AI-powered apps for fun and for boosting personal productivity, using PartyRock. Its widgets display content, accept input, connect with other widgets, and generate outputs like text, images, and chats using foundation models. You can now use new document widget to integrate text content from files and documents directly into a PartyRock app.

30 days of alarm history in Amazon CloudWatch – You can view the history of your alarm state changes for up to 30 days prior. Previously, CloudWatch provided 2 weeks of alarm history. This extended history makes it easier to observe past behavior and review incidents over a longer period of time. To learn more, visit the CloudWatch alarms documentation section.

10x faster startup time in Amazon SageMaker Canvas – You can launch SageMaker Canvas in less than a minute and get started with your visual, no-code interface for machine learning 10x faster than before. Now, all new user profiles created in existing or new SageMaker domains can experience this accelerated startup time.

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

Other AWS news
Here are some additional news items and a Twitch show that you might find interesting:

Let us manage your relational database! – Jeff Barr ran a poll to better understand why some AWS customers still choose to host their own databases in the cloud. Working backwards, he highlights four issues that AWS managed database services address. Consider these before hosting your own database.

Amazon Bedrock Serverless Prompt Chaining – This repository provides examples of using AWS Step Functions and Amazon Bedrock to build complex, serverless, and highly scalable generative AI applications with prompt chaining.

AWS Merch Store Spring Sale – Do you want to buy AWS branded t-shirts, hats, bags, and so on? Get 15% off on all items now through June 7th.

Upcoming AWS events
Check your calendars and sign up for these AWS events:

AWS World IPv6 Day — Join us a free in-person celebration event on June 6, for technical presentations from AWS experts plus a workshop and whiteboarding session. You will learn how to get started with IPv6 and hear from customers who have started on the journey of IPv6 adoption. Check out your near city: San Francisco, Seattle, New York,  London, Mumbai, Bangkok, Singapore, Kuala Lumpur, Beijing, Manila, and Sydney.

AWS Summits — Join free online and in-person events that bring the cloud computing community together to connect, collaborate, and learn about AWS. Register in your nearest city: Stockholm (June 4), Madrid (June 5), and Washington, DC (June 26–27).

AWS re:Inforce — Join us for AWS re:Inforce (June 10–12) in Philadelphia, PA. AWS re:Inforce is a learning conference focused on AWS security solutions, cloud security, compliance, and identity. Connect with the AWS teams that build the security tools and meet AWS customers to learn about their security journeys.

AWS Community Days — Join community-led conferences that feature technical discussions, workshops, and hands-on labs led by expert AWS users and industry leaders from around the world: Midwest | Columbus (June 13), Sri Lanka (June 27), Cameroon (July 13), New Zealand (August 15), Nigeria (August 24), and New York (August 28).

You can browse all upcoming in-person and virtual events.

That’s all for this week. Check back next Monday for another Weekly Roundup!

— Channy

This post is part of our Weekly Roundup series. Check back each week for a quick roundup of interesting news and announcements from AWS!

Simplify custom contact center insights with Amazon Connect analytics data lake

Post Syndicated from Donnie Prakoso original https://aws.amazon.com/blogs/aws/simplify-custom-contact-center-insights-with-amazon-connect-analytics-data-lake/

Analytics are vital to the success of a contact center. Having insights into each touchpoint of the customer experience allows you to accurately measure performance and adapt to shifting business demands. While you can find common metrics in the Amazon Connect console, sometimes you need to have more details and custom requirements for reporting based on the unique needs of your business. 

Starting today, the Amazon Connect analytics data lake is generally available. As announced last year as preview, this new capability helps you to eliminate the need to build and maintain complex data pipelines. Amazon Connect data lake is zero-ETL capable, so no extract, transform, or load (ETL) is needed.

Here’s a quick look at the Amazon Connect analytics data lake:

Improving your customer experience with Amazon Connect
Amazon Connect analytics data lake helps you to unify disparate data sources, including customer contact records and agent activity, into a single location. By having your data in a centralized location, you now have access to analyze contact center performance and gain insights while reducing the costs associated with implementing complex data pipelines.

With Amazon Connect analytics data lake, you can access and analyze contact center data, such as contact trace records and Amazon Connect Contact Lens data. This provides you the flexibility to prepare and analyze data with Amazon Athena and use the business intelligence (BI) tools of your choice, such as, Amazon QuickSight and Tableau. 

Get started with the Amazon Connect analytics data lake
To get started with the Amazon Connect analytics data lake, you’ll first need to have an Amazon Connect instance setup. You can follow the steps in the Create an Amazon Connect instance page to create a new Amazon Connect instance. Because I’ve already created my Amazon Connect instance, I will go straight to showing you how you can get started with Amazon Connect analytics data lake.

First, I navigate to the Amazon Connect console and select my instance.

Then, on the next page, I can set up my analytics data lake by navigating to Analytics tools and selecting Add data share.

This brings up a pop-up dialog, and I first need to define the target AWS account ID. With this option, I can set up a centralized account to receive all data from Amazon Connect instances running in multiple accounts. Then, under Data types, I can select the types I need to share with the target AWS account. To learn more about the data types that you can share in the Amazon Connect analytics data lake, please visit Associate tables for Analytics data lake.

Once it’s done, I can see the list of all the target AWS account IDs with which I have shared all the data types.

Besides using the AWS Management Console, I can also use the AWS Command Line Interface (AWS CLI) to associate my tables with the analytics data lake. The following is a sample command:

$> aws connect batch-associate-analytics-data-set --cli-input-json file:///input_batch_association.json

Where input_batch_association.json is a JSON file that contains association details. Here’s a sample:

{
	"InstanceId": YOUR_INSTANCE_ID,
	"DataSetIds": [
		"<DATA_SET_ID>"
		],
	"TargetAccountId": YOUR_ACCOUNT_ID
} 

Next, I need to approve (or reject) the request in the AWS Resource Access Manager (RAM) console in the target account. RAM is a service to help you securely share resources across AWS accounts. I navigate to AWS RAM and select Resource shares in the Shared with me section.

Then, I select the resource and select Accept resource share. 

At this stage, I can access shared resources from Amazon Connect. Now, I can start creating linked tables from shared tables in AWS Lake Formation. In the Lake Formation console, I navigate to the Tables page and select Create table.

I need to create a Resource link to a shared table. Then, I fill in the details and select the available Database and the Shared table’s region.

Then, when I select Shared table, it will list all the available shared tables that I can access.

Once I select the shared table, it will automatically populate Shared table’s database and Shared table’s owner ID. Once I’m happy with the configuration, I select Create.

To run some queries for the data, I go to the Amazon Athena console.The following is an example of a query that I ran:

With this configuration, I have access to certain Amazon Connect data types. I can even visualize the data by integrating with Amazon QuickSight. The following screenshot show some visuals in the Amazon QuickSight dashboard with data from Amazon Connect.

Customer voice
During the preview period, we heard lots of feedback from our customers about Amazon Connect analytics data lake. Here’s what our customer say:

Joulica is an analytics platform supporting insights for software like Amazon Connect and Salesforce. Tony McCormack, founder and CEO of Joulica, said, “Our core business is providing real-time and historical contact center analytics to Amazon Connect customers of all sizes. In the past, we frequently had to set up complex data pipelines, and so we are excited about using Amazon Connect analytics data lake to simplify the process of delivering actionable intelligence to our shared customers.”

Things you need to know

  • Pricing — Amazon Connect analytics data lake is available for you to use up to 2 years of data without any additional charges in Amazon Connect. You only need to pay for any services you use to interact with the data.
  • Availability — Amazon Connect analytics data lake is generally available in the following AWS Regions: US East (N. Virginia), US West (Oregon), Africa (Cape Town), Asia Pacific (Mumbai, Seoul, Singapore, Sydney, Tokyo), Canada (Central), and Europe (Frankfurt, London)
  • Learn more — For more information, please visit Analytics data lake documentation page.

Happy building,
— Donnie

AWS analytics services streamline user access to data, permissions setting, and auditing

Post Syndicated from Sébastien Stormacq original https://aws.amazon.com/blogs/aws/aws-analytics-services-streamline-user-access-to-data-permissions-setting-and-auditing/

I am pleased to announce a new use case based on trusted identity propagation, a recently introduced capability of AWS IAM Identity Center.

Tableau, a commonly used business intelligence (BI) application, can now propagate end-user identity down to Amazon Redshift. This has a triple benefit. It simplifies the sign-in experience for end users. It allows data owners to define access based on real end-user identity. It allows auditors to verify data access by users.

Trusted identity propagation allows applications that consume data (such as Tableau, Amazon QuickSight, Amazon Redshift Query Editor, Amazon EMR Studio, and others) to propagate the user’s identity and group memberships to the services that store and manage access to the data, such as Amazon Redshift, Amazon Athena, Amazon Simple Storage Service (Amazon S3), Amazon EMR, and others. Trusted identity propagation is a capability of IAM Identity Center that improves the sign-in experience across multiple analytics applications, simplifies data access management, and simplifies audit. End users benefit from single sign-on and do not have to specify the IAM roles they want to assume to connect to the system.

Before diving into more details, let’s agree on terminology.

I use the term “identity providers” to refer to the systems that hold user identities and group memberships. These are the systems that prompt the user for credentials and perform the authentication. For example, Azure Directory, Okta, Ping Identity, and more. Check the full list of identity providers we support.

I use the term “user-facing applications” to designate the applications that consume data, such as Tableau, Microsoft PowerBI, QuickSight, Amazon Redshift Query Editor, and others.

And finally, when I write “downstream services”, I refer to the analytics engines and storage services that process, store, or manage access to your data: Amazon Redshift, Athena, S3, EMR, and others.

Trusted Identity Propagation - high-level diagram

To understand the benefit of trusted identity propagation, let’s briefly talk about how data access was granted until today. When a user-facing application accesses data from a downstream service, either the upstream service uses generic credentials (such as “tableau_user“) or assumes an IAM role to authenticate against the downstream service. This is the source of two challenges.

First, it makes it difficult for the downstream service administrator to define access policies that are fine-tuned for the actual user making the request. As seen from the downstream service, all requests originate from that common user or IAM role. If Jeff and Jane are both mapped to the BusinessAnalytics IAM role, then it is not possible to give them different levels of access, for example, readonly and read-write. Furthermore, if Jeff is also in the Finance group, he needs to choose a role in which to operate; he cannot access data from both groups in the same session.

Secondly, the task of associating a data-access event to an end user involves some undifferentiated heavy lifting. If the request originates from an IAM role called BusinessAnalytics, then additional work is required to figure out which user was behind that action.

Well, this particular example might look very simple, but in real life, organizations have hundreds of users and thousands of groups to match to hundreds of datasets. There was an opportunity for us to Invent and Simplify.

Once configured, the new trusted identity propagation provides a technical mechanism for user-facing applications to access data on behalf of the actual user behind the keyboard. Knowing the actual user identity offers three main advantages.

First, it allows downstream service administrators to create and manage access policies based on actual user identities, the groups they belong to, or a combination of the two. Downstream service administrators can now assign access in terms of users, groups, and datasets. This is the way most of our customers naturally think about access to data—intermediate mappings to IAM roles are no longer necessary to achieve these patterns.

Second, auditors now have access to the original user identity in system logs and can verify that policies are implemented correctly and follow all requirements of the company or industry-level policies.

Third, users of BI applications can benefit from single sign-on between applications. Your end-users no longer need to understand your company’s AWS accounts and IAM roles. Instead, they can sign in to EMR Studio (for example) using their corporate single sign-on that they’re used to for so many other things they do at work.

How does trusted identity propagation work?
Trusted identity propagation relies on standard mechanisms from our industry: OAuth2 and JWT. OAuth2 is an open standard for access delegation that allows users to grant third-party user-facing applications access to data on other services (downstream services) without exposing their credentials. JWT (JSON Web Token) is a compact, URL-safe means of representing identities and claims to be transferred between two parties. JWTs are signed, which means their integrity and authenticity can be verified.

How to configure trusted identity propagation
Configuring trusted identity propagation requires setup in IAM Identity Center, at the user-facing application, and at the downstream service because each of these needs to be told to work with end-user identities. Although the particulars will be different for each application, they will all follow this pattern:

  1. Configure an identity source in AWS IAM Identity Center. AWS recommends enabling automated provisioning if your identity provider supports it, as most do. Automated provisioning works through the SCIM synchronization standard to synchronize your directory users and groups into IAM Identity Center. You probably have configured this already if you currently use IAM Identity Center to federate your workforce into the AWS Management Console. This is a one-time configuration, and you don’t have to repeat this step for each user-facing application.
  2. Configure your user-facing application to authenticate its users with your identity provider. For example, configure Tableau to use Okta.
  3. Configure the connection between the user-facing application and the downstream service. For example, configure Tableau to access Amazon Redshift. In some cases, it requires using the ODBC or JDBC driver for Redshift.

Then comes the configuration specific to trusted identity propagation. For example, imagine your organization has developed a user-facing web application that authenticates the users with your identity provider, and that you want to access data in AWS on behalf of the current authenticated user. For this use case, you would create a trusted token issuer in IAM Identity Center. This powerful new construct gives you a way to map your application’s authenticated users to the users in your IAM Identity Center directory so that it can make use of trusted identity propagation. My colleague Becky wrote a blog post to show you how to develop such an application. This additional configuration is required only when using third-party applications, such as Tableau, or a customer-developed application, that authenticate outside of AWS. When using user-facing applications managed by AWS, such as Amazon QuickSight, no further setup is required.

setup an external IdP to issue trusted token

Finally, downstream service administrators must configure the access policies based on the user identity and group memberships. The exact configuration varies from one downstream service to the other. If the application reads or writes data in Amazon S3, the data owner may use S3 Access Grants in the Amazon S3 console to grant access for users and groups to prefixes in Amazon S3. If the application makes queries to an Amazon Redshift data warehouse, the data owner must configure IAM Identity Center trusted connection in the Amazon Redshift console and match the audience claim (aud) from the identity provider.

Now that you have a high-level overview of the configuration, let’s dive into the most important part: the user experience.

The end-user experience
Although the precise experience of the end user will obviously be different for different applications, in all cases, it will be simpler and more familiar to workforce users than before. The user interaction will begin with a redirect-based authentication single sign-on flow that takes the user to their identity provider, where they can sign in with credentials, multi-factor authentication, and so on.

Let’s look at the details of how an end user might interact with Okta and Tableau when trusted identity propagation has been configured.

Here is an illustration of the flow and the main interactions between systems and services.

Trusted Identity Propagation flow

Here’s how it goes.

1. As a user, I attempt to sign in to Tableau.

2. Tableau initiates a browser-based flow and redirects to the Okta sign-in page where I can enter my sign-in credentials. On successful authentication, Okta issues an authentication token (ID and access token) to Tableau.

3. Tableau initiates a JDBC connection with Amazon Redshift and includes the access token in the connection request. The Amazon Redshift JDBC driver makes a call to Amazon Redshift. Because your Amazon Redshift administrator enabled IAM Identity Center, Amazon Redshift forwards the access token to IAM Identity Center.

4. IAM Identity Center verifies and validates the access token and exchange the access token for an Identity Center issued token.

5. Amazon Redshift will resolve the Identity Center token to determine the corresponding Identity Center user and authorize access to the resource. Upon successful authorization, I can connect from Tableau to Amazon Redshift.

Once authenticated, I can start to use Tableau as usual.

Trusted Identity Propagation - Tableau usage

And when I connect to Amazon Redshift Query Editor, I can observe the sys_query_history table to check who was the user who made the query. It correctly reports awsidc:<email address>, the Okta email address I used when I connected from Tableau.

Trusted Identity Propagation - audit in Redshift

You can read Tableau’s documentation for more details about this configuration.

Pricing and availability
Trusted identity propagation is provided at no additional cost in the 26 AWS Regions where AWS IAM Identity Center is available today.

Here are more details about trusted identity propagation and downstream service configurations.

Happy reading!

With trusted identity propagation, you can now configure analytics systems to propagate the actual user identity, group membership, and attributes to AWS services such as Amazon Redshift, Amazon Athena, or Amazon S3. It simplifies the management of access policies on these services. It also allows auditors to verify your organization’s compliance posture to know the real identity of users accessing data.

Get started now and configure your Tableau integration with Amazon Redshift.

— seb

PS: Writing a blog post at AWS is always a team effort, even when you see only one name under the post title. In this case, I want to thank Eva Mineva, Laura Reith, and Roberto Migli for their much-appreciated help in understanding the many subtleties and technical details of trusted identity propagation.

Amazon EC2 high memory U7i Instances for large in-memory databases

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/amazon-ec2-high-memory-u7i-instances-for-large-in-memory-databases/

Announced in preview form at re:Invent 2023, Amazon Elastic Compute Cloud (Amazon EC2) U7i instances with up to 32 TiB of DDR5 memory and 896 vCPUs are now available. Powered by custom fourth generation Intel Xeon Scalable Processors (Sapphire Rapids), these high memory instances are designed to support large, in-memory databases including SAP HANA, Oracle, and SQL Server. Here are the specs:

Instance Name vCPUs
Memory (DDR5)
EBS Bandwidth
Network Bandwidth
u7i-12tb.224xlarge 896 12,288 GiB 60 Gbps 100 Gbps
u7in-16tb.224xlarge 896 16,384 GiB 100 Gbps 200 Gbps
u7in-24tb.224xlarge 896 24,576 GiB 100 Gbps 200 Gbps
u7in-32tb.224xlarge 896 32,768 GiB 100 Gbps 200 Gbps

The new instances deliver the best compute price performance for large in-memory workloads, and offer the highest memory and compute power of any SAP-certified virtual instance from a leading cloud provider.

Thanks to AWS Nitro System, all of the memory on the instance is available for use. For example, here’s the 32 TiB instance:

In comparison to the previous generation of EC2 High Memory instances, the U7i instances offer more than 135% of the compute performance, up to 115% more memory performance, and 2.5x the EBS bandwidth. This increased bandwidth allows you to transfer 30 TiB of data from EBS into memory in an hour or less, making data loads and cache refreshes faster than ever before. The instances also support ENA Express with 25 Gbps of bandwidth per flow, and provide an 85% improvement in P99.9 latency between instances.

Each U7i instance supports attachment of up to 128 General Purpose (gp2 and gp3) or Provisioned IOPS (io1 and io2 Block Express) EBS volumes. Each io2 Block Express volume can be as big as 64 TiB and can deliver up to 256K IOPS at up to 32 Gbps, making them a great match for U7i instances.

The instances are SAP certified to run Business Suite on HANA, Business Suite S/4HANA, Business Warehouse on HANA (BW), and SAP BW/4HANA in production environments. To learn more, consult the Certified and Supported SAP HANA Hardware and the SAP HANA to AWS Migration Guide. Also, be sure to take a look at the AWS Launch Wizard for SAP.

Things to Know
Here are a couple of things that you should know about these new instances:

Regions – U7i instances are available in the US East (N. Virginia), US West (Oregon), and Asia Pacific (Seoul, Sydney) AWS Regions.

Operating Systems – Supported operating systems include Amazon Linux, Red Hat Enterprise Linux, SUSE Linux Enterprise Server, Ubuntu, and Windows Server.

Larger Instances – We are also working on offering even larger instance later this year with increased compute to meet our customer needs.

— Jeff;

AWS Weekly Roundup – LlamaIndex support for Amazon Neptune, force AWS CloudFormation stack deletion, and more (May 27, 2024)

Post Syndicated from Antje Barth original https://aws.amazon.com/blogs/aws/aws-weekly-roundup-llamaindex-support-for-amazon-neptune-force-aws-cloudformation-stack-deletion-and-more-may-27-2024/

Last week, Dr. Matt Wood, VP for AI Products at Amazon Web Services (AWS), delivered the keynote at the AWS Summit Los Angeles. Matt and guest speakers shared the latest advancements in generative artificial intelligence (generative AI), developer tooling, and foundational infrastructure, showcasing how they come together to change what’s possible for builders. You can watch the full keynote on YouTube.

AWS Summit LA 2024 keynote

Announcements during the LA Summit included two new Amazon Q courses as part of Amazon’s AI Ready initiative to provide free AI skills training to 2 million people globally by 2025. The courses are part of the Amazon Q learning plan. But that’s not all that happened last week.

Last week’s launches
Here are some launches that got my attention:

LlamaIndex support for Amazon Neptune — You can now build Graph Retrieval Augmented Generation (GraphRAG) applications by combining knowledge graphs stored in Amazon Neptune and LlamaIndex, a popular open source framework for building applications with large language models (LLMs) such as those available in Amazon Bedrock. To learn more, check the LlamaIndex documentation for Amazon Neptune Graph Store.

AWS CloudFormation launches a new parameter called DeletionMode for the DeleteStack API — You can use the AWS CloudFormation DeleteStack API to delete your stacks and stack resources. However, certain stack resources can prevent the DeleteStack API from successfully completing, for example, when you attempt to delete non-empty Amazon Simple Storage Service (Amazon S3) buckets. The DeleteStack API can enter into the DELETE_FAILED state in such scenarios. With this launch, you can now pass FORCE_DELETE_STACK value to the new DeletionMode parameter and delete such stacks. To learn more, check the DeleteStack API documentation.

Mistral Small now available in Amazon Bedrock — The Mistral Small foundation model (FM) from Mistral AI is now generally available in Amazon Bedrock. This a fast-follow to our recent announcements of Mistral 7B and Mixtral 8x7B in March, and Mistral Large in April. Mistral Small, developed by Mistral AI, is a highly efficient large language model (LLM) optimized for high-volume, low-latency language-based tasks. To learn more, check Esra’s post.

New Amazon CloudFront edge location in Cairo, Egypt — The new AWS edge location brings the full suite of benefits provided by Amazon CloudFront, a secure, highly distributed, and scalable content delivery network (CDN) that delivers static and dynamic content, APIs, and live and on-demand video with low latency and high performance. Customers in Egypt can expect up to 30 percent improvement in latency, on average, for data delivered through the new edge location. To learn more about AWS edge locations, visit CloudFront edge locations.

Amazon OpenSearch Service zero-ETL integration with Amazon S3 — This Amazon OpenSearch Service integration offers a new efficient way to query operational logs in Amazon S3 data lakes, eliminating the need to switch between tools to analyze data. You can get started by installing out-of-the-box dashboards for AWS log types such as Amazon VPC Flow Logs, AWS WAF Logs, and Elastic Load Balancing (ELB). To learn more, check out the Amazon OpenSearch Service Integrations page and the Amazon OpenSearch Service Developer Guide.

For a full list of AWS announcements, be sure to keep an eye on the What’s New at AWS page.

Other AWS news
Here are some additional news items and a Twitch show that you might find interesting:

AWS Build On Generative AIBuild On Generative AI — Now streaming every Thursday, 2:00 PM US PT on twitch.tv/aws, my colleagues Tiffany and Mike discuss different aspects of generative AI and invite guest speakers to demo their work. Check out show notes and the full list of episodes on community.aws.

Amazon Bedrock Studio bootstrapper script — We’ve heard your feedback! To everyone who struggled setting up the required AWS Identity and Access Management (IAM) roles and permissions to get started with Amazon Bedrock Studio: You can now use the Bedrock Studio bootstrapper script to automate the creation of the permissions boundary, service role, and provisioning role.

Upcoming AWS events
Check your calendars and sign up for these AWS events:

AWS SummitsAWS Summits — It’s AWS Summit season! Join free online and in-person events that bring the cloud computing community together to connect, collaborate, and learn about AWS. Register in your nearest city: Dubai (May 29), Bangkok (May 30), Stockholm (June 4), Madrid (June 5), and Washington, DC (June 26–27).

AWS re:InforceAWS re:Inforce — Join us for AWS re:Inforce (June 10–12) in Philadelphia, PA. AWS re:Inforce is a learning conference focused on AWS security solutions, cloud security, compliance, and identity. Connect with the AWS teams that build the security tools and meet AWS customers to learn about their security journeys.

AWS Community DaysAWS Community Days — Join community-led conferences that feature technical discussions, workshops, and hands-on labs led by expert AWS users and industry leaders from around the world: Midwest | Columbus (June 13), Sri Lanka (June 27), Cameroon (July 13), New Zealand (August 15), Nigeria (August 24), and New York (August 28).

You can browse all upcoming in-person and virtual events.

That’s all for this week. Check back next Monday for another Weekly Roundup!

— Antje

This post is part of our Weekly Roundup series. Check back each week for a quick roundup of interesting news and announcements from AWS!

Optimized for low-latency workloads, Mistral Small now available in Amazon Bedrock

Post Syndicated from Esra Kayabali original https://aws.amazon.com/blogs/aws/optimized-for-low-latency-workloads-mistral-small-now-available-in-amazon-bedrock/

Today, I am happy to announce that the Mistral Small foundation model (FM) from Mistral AI is now generally available in Amazon Bedrock. This a fast-follow to our recent announcements of Mistral 7B and Mixtral 8x7B in March, and Mistral Large in April. You can now access four high-performing models from Mistral AI in Amazon Bedrock including Mistral Small, Mistral Large, Mistral 7B, and Mixtral 8x7B, further expanding model choice.

Mistral Small, developed by Mistral AI, is a highly efficient large language model (LLM) optimized for high-volume, low-latency language-based tasks. Mistral Small is perfectly suited for straightforward tasks that can be performed in bulk, such as classification, customer support, or text generation. It provides outstanding performance at a cost-effective price point.

Some key features of Mistral Small you need to know about:

  • Retrieval-Augmented Generation (RAG) specialization – Mistral Small ensures that important information is retained even in long context windows, which can extend up to 32K tokens.
  • Coding proficiency – Mistral Small excels in code generation, review, and commenting, supporting major coding languages.
  • Multilingual capability – Mistral Small delivers top-tier performance in French, German, Spanish, and Italian, in addition to English. It also supports dozens of other languages.

Getting started with Mistral Small
I first need access to the model to get started with Mistral Small. I go to the Amazon Bedrock console, choose Model access, and then choose Manage model access. I expand the Mistral AI section, choose Mistral Small, and then choose Save changes.

I now have model access to Mistral Small, and I can start using it in Amazon Bedrock. I refresh the Base models table to view the current status.

I use the following template to build a prompt for the model to get sub-optimal outputs:

<s>[INST] Instruction [/INST]

Note that <s> is a special token for beginning of string (BOS) while [INST] and [/INST] are regular strings.

I try the following prompt to see a classification example:

Prompt:

<s>[INST] Classify the following email to determine if it is spam or not. Only respond with the exact words "Spam" or "Not spam".
🎉 Limited time offer! Buy 2, Get 1 Free! Offer ends today! Don't miss out the CHANCE TO SAVE, please click on the link below: https://bit.ly/buy-2-get-1-free [/INST]

Mistral 7B, Mixtral 8x7B, and Mistral Large can all correctly classify this email as “Spam.” Mistral Small is also able to classify this accurately, just as the larger models can. I also try several similar tasks, such as generating a Bash script from a text prompt and generating a recipe to prepare yoghurt, and get good results. For this reason, Mistral Small is the most cost-effective and efficient option of the Mistral AI models in Amazon Bedrock for such tasks.

Mistral Small excels in multilingual tasks. Along with English, it delivers superior performance in French, German, Spanish, and Italian. I test the model’s understanding of the German language by asking it to give me two sentences about sustainability:

Prompt:

<s>[INST] Geben Sie mir zwei Sätze zum Thema Nachhaltigkeit. [/INST]

Output:

1. Nachhaltigkeit bedeutet, die Bedürfnisse der heutigen Generation zu befriedigen, ohne die Möglichkeiten künftiger Generationen zu gefährden.
2. Ein nachhaltiger Lebensstil umfasst den bewussten Umgang mit Ressourcen, wie Wasser, Energie und Rohstoffen, sowie den Schutz von Umwelt und Klima.
Diese Sätze sollten Ihnen einen guten Überblick über das Thema Nachhaltigkeit geben.

Programmatically interact with Mistral Small
I can use AWS Command Line Interface (AWS CLI) and AWS Software Development Kit (SDK) to programmatically interact with Mistral Small using Amazon Bedrock APIs. I use the following code in Python, which interacts with Amazon Bedrock Runtime APIs with AWS SDK, asking, “What is the color of the sky?”:

import argparse
import boto3
from botocore.exceptions import ClientError
import json

accept = "application/json"
content_type = "application/json"

def invoke_model(model_id, input_data, region, streaming): 
  client = boto3.client('bedrock-runtime', region_name=region)
  try:
    if streaming:
      response = client.invoke_model_with_response_stream(body=input_data, modelId=model_id, accept=accept, contentType=content_type)
    else:
      response = client.invoke_model(body=input_data, modelId=model_id, accept=accept, contentType=content_type)
    status_code = response['ResponseMetadata']['HTTPStatusCode']
    print(json.loads(response.get('body').read()))
  except ClientError as e:
    print(e)

if __name__ == "__main__":
  parser = argparse.ArgumentParser(description="Bedrock Testing Tool")
  parser.add_argument("--prompt", type=str, help="prompt to use", default="Hello")
  parser.add_argument("--max-tokens", type=int, default=64)
  parser.add_argument("--streaming", choices=["true", "false"], help="whether to stream or not", default="false")
  args = parser.parse_args()
  streaming = False
  if args.streaming == "true":
    streaming = True
  input_data = json.dumps({
    "prompt": f"<s>[INST]{args.prompt}[/INST]",
    "max_tokens": args.max_tokens
  })
  invoke_model(model_id="mistral.mistral-small-2402-v1:0", input_data=input_data, region="us-east-1", streaming=streaming)

I get the following output:

{'outputs': [{'text': ' The color of the sky can vary depending on the time of day, weather,', 'stop_reason': 'length'}]}

Now available
The Mistral Small model is now available in Amazon Bedrock in the US East (N. Virginia) Region.

To learn more, visit the Mistral AI in Amazon Bedrock product page. For pricing details, review the Amazon Bedrock pricing page.

To get started with Mistral Small in Amazon Bedrock, visit the Amazon Bedrock console and Amazon Bedrock User Guide.

— Esra