Tag Archives: policy

How to use resource-based policies in the AWS Secrets Manager console to securely access secrets across AWS accounts

Post Syndicated from Tracy Pierce original https://aws.amazon.com/blogs/security/how-to-use-resource-based-policies-aws-secrets-manager-console-to-securely-access-secrets-aws-accounts/

AWS Secrets Manager now enables you to create and manage your resource-based policies using the Secrets Manager console. With this launch, we are also improving your security posture by both identifying and preventing creation of resource policies that grant overly broad access to your secrets across your Amazon Web Services (AWS) accounts. To achieve this, we use the Zelkova engine to mathematically analyze access granted by your resource policy and alert you if such permissions are found. The analysis verifies access across all resource-policy statements, actions, and the set of condition keys used in your policies. To be considered non-public, the resource policy must grant access only to fixed values (values that don’t contain a wildcard) of one or more of the following: aws:SourceArn, aws:SourceVpc, aws:SourceVpce, aws:SourceAccount, aws:SourceIP, and ensure the Principal does not include a “*” entry.

If the policy grants Public or overly broad access to your secrets across AWS accounts, Secrets Manager will block you from applying the policy in the console and alert you with a dashboard message. This prevents your policy from accidentally granting broader access to your secrets, instead ensuring you are restricting it to the intended AWS accounts, AWS services, and AWS Identity and Access Management (IAM) entities. Access to AWS Secrets Manager requires AWS credentials. Those credentials must contain permission to access the AWS resources you want to access, such as your Secrets Manager secrets. In this blog post, we use Public or broad access to refer to values (or a combination of values) in the resource policy that result in a wide access across AWS accounts and principals.

With AWS Secrets Manager, you have the option to store, rotate, manage, and retrieve many types of secrets. These can be database usernames and passwords, API keys, string values, and binary data. AWS supports the ability to share these secrets cross-account by applying resource policies via the AWS Command Line Interface (AWS CLI) and now via the Secrets Manager console.

Why would you need to share a secret? There are many reasons. Perhaps you have database credentials managed in a central account that are needed by applications in your production account. Maybe you have the binary stored for an encryption key that other accounts will use to create AWS Key Management Service (AWS KMS) keys in their accounts. To achieve this goal while ensuring a secure transfer of information and least privilege permissions, you will need a resource-based policy on your secret, a resource-based policy on your AWS KMS Customer Managed Key (CMK) used for encrypting the secret, and a user-based policy on your IAM principal.

You can still create a policy using AWS CLI or AWS SDK permitting access to a broader scope of entities if your business needs dictate. If you do permit this type of broader access, AWS Secrets Manager will show a notification in your dashboard, as shown in Figure 2, below.

Figure 1. This shows the warning when you try to create a resource policy that grants broad access to your secrets via the AWS Secrets Manager console.

Figure 1. This shows the warning when you try to create a resource policy that grants broad access to your secrets via the AWS Secrets Manager console.

 

Figure 2. This alert pops up when you click on a secret that has a resource policy attached via the CLI that grants broad access to the secret.

Figure 2. This alert pops up when you click on a secret that has a resource policy attached via the CLI that grants broad access to the secret.

In the example below, you’ll see how to use the AWS Secrets Manager console to attach a resource-based policy and allow access to your secret from a secondary account. A secret in the CENTRAL_SECURITY_ACCOUNT will be set to allow it to be accessed by an IAM role in the PRODUCTION_ACCOUNT.

In this example:

  • SECURITY_SECRET = The secret created in the CENTRAL_SECURITY_ACCOUNT.
  • SECURITY_CMK = The AWS KMS CMK used to encrypt the SECURITY_SECRET.
  • PRODUCTION_ROLE = The AWS IAM role used to access the SECURITY_SECRET.
  • PRODUCTION_ACCOUNT = The AWS account that owns the AWS IAM role used for cross-account access.

Overview of solution

The architecture of the solution can be broken down into four steps, which are outlined in Figure 3. The four main steps are:

  1. Create the resource-based policy via the AWS Secrets Manager console on the SECURITY_SECRET in the CENTRAL_SECURITY_ACCOUNT.
  2. Update the SECURITY_CMK policy in the CENTRAL_SECURITY_ACCOUNT to allow the role from the PRODUCTION account access.
  3. Grant the AWS IAM role in the PRODUCTION_ACCOUNT permissions to access the secret.
  4. Test and verify access from the PRODUCTION_ACCOUNT.

 

Figure 3. A visual overview of the four steps to use the AWS Secrets Manager console to attach a resource-based policy, allow access to your secret from a secondary account, and test and verify the process.

Figure 3. A visual overview of the four steps to use the AWS Secrets Manager console to attach a resource-based policy, allow access to your secret from a secondary account, and test and verify the process.

Prerequisites

To use the example in this post, you need:

  • An AWS account.
  • An IAM Role with permissions to make modifications in both the CENTRAL_SECURITY_ACCOUNT and the PRODUCTION_ACCOUNT.
  • An IAM role in the PRODUCTION_ACCOUNT you wish to grant permissions to access the SECURITY_SECRET.

Deploying the solution

Step 1: Create a resource-based policy in your CENTRAL_SECURITY account on the SECURITY_SECRET secret

  1. Log in to the AWS Secrets Manager console in the CENTRAL_SECURITY_ACCOUNT.
  2. Choose SECURITY_SECRET.
  3. Choose Edit Permissions next to Resource Permissions (optional).

    Figure 4. The dashboard view where you edit permissions.

    Figure 4. The dashboard view where you edit permissions.

  4. This will bring you to the page to add the resource policy. It will give you a basic template as shown in Figure 5, below.

    Figure 5. The basic template to add the resource policy.

    Figure 5. The basic template to add the resource policy.

  5. Since the full policy is provided for you in this example, delete the template from the text box.
  6. Copy the policy below and paste it in the text box. Make sure to replace PRODUCTION with your AWS account ID. You can also adjust the permissions you grant if needed. This policy allows a specific role in the PRODUCTION_ACCOUNT account to retrieve the current version of your secret. In the example, my IAM Role is called PRODUCTION_ROLE. Note you do not need to replace AWSCURRENT with any other value.
    
    {
        "Version": "2012-10-17",
        "Statement": [
            {
                "Effect": "Allow",
                "Principal": {
                    "AWS": "arn:aws:iam::PRODUCTION:role/PRODUCTION_ROLE"
                },
                "Action": "secretsmanager:GetSecretValue",
                "Resource": "*",
                "Condition": {
                    "ForAnyValue:StringEquals": {
                        "secretsmanager:VersionStage": "AWSCURRENT"
                    }
                }
            }
        ]
    }
    

    As shown in Figure 6, below, you’ll see this in the resource policy text area (with your AWS account ID in place of PRODUCTION).

    Figure 6. The example resource policy shown in the console.

    Figure 6. The example resource policy shown in the console.

  7. Choose Save.

Step 2: Update the resource-based policy in your CENTRAL_SECURITY account on the SECURITY_CMK

Note: Secrets in AWS Secrets Manager are encrypted by default. However, it is important for you to provide authorization for IAM Principals that need to access your secrets. Complete authorization requires access to the secret and the KMS CMK used to encrypt it, which prevents accidental public permissions on the secret. It is important to maintain both sets of authorization to provide appropriate access to secrets.

  1. Log in to the AWS KMS console in the CENTRAL_SECURITY_ACCOUNT.
  2. Choose SECURITY_CMK.
  3. Next to Key policy choose the Edit button.
  4. Paste the below code snippet into your key policy to allow the PRODUCTION_ACCOUNT access:
    
    {
        "Sid": "AllowUseOfTheKey",
        "Effect": "Allow",
        "Principal": {
            "AWS": "arn:aws:iam::PRODUCTION:role/PRODUCTION_ROLE"
        },
        "Action": [
            "kms:Decrypt",
            "kms:DescribeKey"
        ],
        "Resource": "arn:aws:kms:us-east-1:CENTRAL_SECURITY:key/SECURITY_CMK"
    }
    

    You will need to replace PRODUCTION with your production account ID, PRODUCTION_ROLE with your production Role name, CENTRAL_SECURITY with your security account ID, and SECURITY_CMK with the CMK key ID of your security CMK. If you forget to swap out the account IDs in the policy with your own, you’ll see an error message similar to the one shown in Figure 7, below.

    Figure 7. Error message that appears if you don’t swap out your account number correctly.

    Figure 7. Error message that appears if you don’t swap out your account number correctly.

  5. Choose Save changes.

Step 3: Add permissions to the PRODUCTION_ROLE in the PRODUCTION account

  1. Log in to the AWS IAM console in the PRODUCTION_ACCOUNT account.
  2. In the left navigation pane, choose Roles.
  3. Select PRODUCTION_ROLE.
  4. Under the Permissions tab, choose Add inline policy.
  5. Choose the JSON tab and paste the below policy:
    
    {
        "Version": "2012-10-17",
        "Statement": [
            {
                "Effect": "Allow",
                "Action": "secretsmanager:GetSecretValue",
                "Resource": " arn:aws:secretsmanager:us-east-1:CENTRAL_SECURITY:secret:SECURITY_SECRET"
            },
            {
                "Effect": "Allow",
                "Action": [
                    "kms:Decrypt",
                    "kms:DescribeKey"
                ],
                "Resource": "arn:aws:kms:us-east-1:CENTRAL_SECURITY:key/SECURITY_CMK"
            }
        ]
    }
    

    You will need to replace CENTRAL_SECURITY with your security account id, SECURITY_SECRET with the secret id, and SECURITY_CMK with the CMK key id of your security CMK.

  6. Choose Review policy.
  7. Name the policy Central_Security_Account_Security-Secret-Access, and choose Create policy.

Step 4: Test access to the SECURITY_SECRET from the PRODUCTION account

Verification of access via AWS CLI

  1. From the AWS CLI, use the PRODUCTION_ROLE credentials to run the get-secret-value command.
  2. Returned output should look like the example, below, in Figure 8.
    
    $aws secretsmanager get-secret-value --secret-id SECURITY_SECRET --version-stage AWSCURRENT
    
    {
        “ARN”: “arn:aws:secretsmanager:us-east-1:CENTRAL_SECURITY:secret:SECURITY_SECRET”,
        “Name”: “SECURITY_SECRET”,
        “SecretString”: “TheSecretString”,
        “CreatedDate”: 123456789,
        “VersionId”: “64c4250d-0b81-42e0-9a0c-e189d3c9aea8”,
        “VersionsStages”: [
            “AWSCURRENT”
        ]
    }
    

You can also verify the policy was attached from the CENTRAL_SECURITY_ACCOUNT by following the steps below.

Verification of policy via console

  1. Log into the AWS Secrets Manager console in the CENTRAL_SECURITY_ACCOUNT.
  2. Choose SECURITY_SECRET.
  3. Scroll down to where it shows Resource Permissions (optional), and you’ll see your resource policy stored in the console, as shown in Figure 8, below.

    Figure 8. What the example resource policy looks like in the console.

    Figure 8. What the example resource policy looks like in the console.

Conclusion

In this post, you saw how to add a resource-based policy on a secret in AWS Secrets Manager using the console, and how to update your AWS KMS CMK resource-based policy to enable access. The example showed setting up cross-account access, and allowing a role from the PRODUCTION_ACCOUNT to use the secret in the CENTRAL_SECURITY_ACCOUNT. By using the AWS Secrets Manager console to set up the resource-based policy, you now have a straight-forward, visual way to add and manage resource-based policies for your secrets and receive notifications if that policy is too broad.

If you have feedback about this post, submit comments in the Comments section below. If you have questions about this post, start a new thread on the AWS Secrets Manager forum or contact AWS Support.

Want more AWS Security how-to content, news, and feature announcements? Follow us on Twitter.

Author

Tracy Pierce

Tracy is a Senior Consultant, Security Specialty, for Remote Consulting Services. She enjoys the peculiar culture of Amazon and uses that to ensure every day is exciting for her fellow engineers and customers alike. Customer Obsession is her highest priority and she shows this by improving processes, documentation, and building tutorials. She has her AS in Computer Security and Forensics from SCTD, SSCP certification, AWS Developer Associate certification, and AWS Security Specialist certification. Outside of work, she enjoys time with friends, her Great Dane, and three cats. She keeps work interesting by drawing cartoon characters on the walls at request.

[$] Deferring seccomp decisions to user space

Post Syndicated from corbet original https://lwn.net/Articles/756233/rss

There has been a lot of work in recent years to use BPF to push policy
decisions into the kernel. But sometimes, it seems, what is really wanted
is a way for a BPF program to punt a decision back to user space. That is
the objective behind this patch set giving
the secure
computing (seccomp)
mechanism a way to pass complex decisions to
a user-space helper program.

Monitoring your Amazon SNS message filtering activity with Amazon CloudWatch

Post Syndicated from Rachel Richardson original https://aws.amazon.com/blogs/compute/monitoring-your-amazon-sns-message-filtering-activity-with-amazon-cloudwatch/

This post is courtesy of Otavio Ferreira, Manager, Amazon SNS, AWS Messaging.

Amazon SNS message filtering provides a set of string and numeric matching operators that allow each subscription to receive only the messages of interest. Hence, SNS message filtering can simplify your pub/sub messaging architecture by offloading the message filtering logic from your subscriber systems, as well as the message routing logic from your publisher systems.

After you set the subscription attribute that defines a filter policy, the subscribing endpoint receives only the messages that carry attributes matching this filter policy. Other messages published to the topic are filtered out for this subscription. In this way, the native integration between SNS and Amazon CloudWatch provides visibility into the number of messages delivered, as well as the number of messages filtered out.

CloudWatch metrics are captured automatically for you. To get started with SNS message filtering, see Filtering Messages with Amazon SNS.

Message Filtering Metrics

The following six CloudWatch metrics are relevant to understanding your SNS message filtering activity:

  • NumberOfMessagesPublished – Inbound traffic to SNS. This metric tracks all the messages that have been published to the topic.
  • NumberOfNotificationsDelivered – Outbound traffic from SNS. This metric tracks all the messages that have been successfully delivered to endpoints subscribed to the topic. A delivery takes place either when the incoming message attributes match a subscription filter policy, or when the subscription has no filter policy at all, which results in a catch-all behavior.
  • NumberOfNotificationsFilteredOut – This metric tracks all the messages that were filtered out because they carried attributes that didn’t match the subscription filter policy.
  • NumberOfNotificationsFilteredOut-NoMessageAttributes – This metric tracks all the messages that were filtered out because they didn’t carry any attributes at all and, consequently, didn’t match the subscription filter policy.
  • NumberOfNotificationsFilteredOut-InvalidAttributes – This metric keeps track of messages that were filtered out because they carried invalid or malformed attributes and, thus, didn’t match the subscription filter policy.
  • NumberOfNotificationsFailed – This last metric tracks all the messages that failed to be delivered to subscribing endpoints, regardless of whether a filter policy had been set for the endpoint. This metric is emitted after the message delivery retry policy is exhausted, and SNS stops attempting to deliver the message. At that moment, the subscribing endpoint is likely no longer reachable. For example, the subscribing SQS queue or Lambda function has been deleted by its owner. You may want to closely monitor this metric to address message delivery issues quickly.

Message filtering graphs

Through the AWS Management Console, you can compose graphs to display your SNS message filtering activity. The graph shows the number of messages published, delivered, and filtered out within the timeframe you specify (1h, 3h, 12h, 1d, 3d, 1w, or custom).

SNS message filtering for CloudWatch Metrics

To compose an SNS message filtering graph with CloudWatch:

  1. Open the CloudWatch console.
  2. Choose Metrics, SNS, All Metrics, and Topic Metrics.
  3. Select all metrics to add to the graph, such as:
    • NumberOfMessagesPublished
    • NumberOfNotificationsDelivered
    • NumberOfNotificationsFilteredOut
  4. Choose Graphed metrics.
  5. In the Statistic column, switch from Average to Sum.
  6. Title your graph with a descriptive name, such as “SNS Message Filtering”

After you have your graph set up, you may want to copy the graph link for bookmarking, emailing, or sharing with co-workers. You may also want to add your graph to a CloudWatch dashboard for easy access in the future. Both actions are available to you on the Actions menu, which is found above the graph.

Summary

SNS message filtering defines how SNS topics behave in terms of message delivery. By using CloudWatch metrics, you gain visibility into the number of messages published, delivered, and filtered out. This enables you to validate the operation of filter policies and more easily troubleshoot during development phases.

SNS message filtering can be implemented easily with existing AWS SDKs by applying message and subscription attributes across all SNS supported protocols (Amazon SQS, AWS Lambda, HTTP, SMS, email, and mobile push). CloudWatch metrics for SNS message filtering is available now, in all AWS Regions.

For information about pricing, see the CloudWatch pricing page.

For more information, see:

timeShift(GrafanaBuzz, 1w) Issue 46

Post Syndicated from Blogs on Grafana Labs Blog original https://grafana.com/blog/2018/05/24/timeshiftgrafanabuzz-1w-issue-46/

Welcome to TimeShift The day has finally arrived; GDPR is officially in effect! These new policies are meant to provide more transparency about the data companies collect on users, and how that data is used. I for one am just excited that the onslaught of "We’ve updated our privacy policy" emails arriving in my pummeled inbox is nearing its end.
Grafana Labs is no exception. We encourage you to check out our privacy policy, and if you have any questions, feel free to contact us at [email protected]

The Benefits of Side Projects

Post Syndicated from Bozho original https://techblog.bozho.net/the-benefits-of-side-projects/

Side projects are the things you do at home, after work, for your own “entertainment”, or to satisfy your desire to learn new stuff, in case your workplace doesn’t give you that opportunity (or at least not enough of it). Side projects are also a way to build stuff that you think is valuable but not necessarily “commercialisable”. Many side projects are open-sourced sooner or later and some of them contribute to the pool of tools at other people’s disposal.

I’ve outlined one recommendation about side projects before – do them with technologies that are new to you, so that you learn important things that will keep you better positioned in the software world.

But there are more benefits than that – serendipitous benefits, for example. And I’d like to tell some personal stories about that. I’ll focus on a few examples from my list of side projects to show how, through a sort-of butterfly effect, they helped shape my career.

The computoser project, no matter how cool algorithmic music composition, didn’t manage to have much of a long term impact. But it did teach me something apart from niche musical theory – how to read a bulk of scientific papers (mostly computer science) and understand them without being formally trained in the particular field. We’ll see how that was useful later.

Then there was the “State alerts” project – a website that scraped content from public institutions in my country (legislation, legislation proposals, decisions by regulators, new tenders, etc.), made them searchable, and “subscribable” – so that you get notified when a keyword of interest is mentioned in newly proposed legislation, for example. (I obviously subscribed for “information technologies” and “electronic”).

And that project turned out to have a significant impact on the following years. First, I chose a new technology to write it with – Scala. Which turned out to be of great use when I started working at TomTom, and on the 3rd day I was transferred to a Scala project, which was way cooler and much more complex than the original one I was hired for. It was a bit ironic, as my colleagues had just read that “I don’t like Scala” a few weeks earlier, but nevertheless, that was one of the most interesting projects I’ve worked on, and it went on for two years. Had I not known Scala, I’d probably be gone from TomTom much earlier (as the other project was restructured a few times), and I would not have learned many of the scalability, architecture and AWS lessons that I did learn there.

But the very same project had an even more important follow-up. Because if its “civic hacking” flavour, I was invited to join an informal group of developers (later officiated as an NGO) who create tools that are useful for society (something like MySociety.org). That group gathered regularly, discussed both tools and policies, and at some point we put up a list of policy priorities that we wanted to lobby policy makers. One of them was open source for the government, the other one was open data. As a result of our interaction with an interim government, we donated the official open data portal of my country, functioning to this day.

As a result of that, a few months later we got a proposal from the deputy prime minister’s office to “elect” one of the group for an advisor to the cabinet. And we decided that could be me. So I went for it and became advisor to the deputy prime minister. The job has nothing to do with anything one could imagine, and it was challenging and fascinating. We managed to pass legislation, including one that requires open source for custom projects, eID and open data. And all of that would not have been possible without my little side project.

As for my latest side project, LogSentinel – it became my current startup company. And not without help from the previous two mentioned above – the computer science paper reading was of great use when I was navigating the crypto papers landscape, and from the government job I not only gained invaluable legal knowledge, but I also “got” a co-founder.

Some other side projects died without much fanfare, and that’s fine. But the ones above shaped my “story” in a way that would not have been possible otherwise.

And I agree that such serendipitous chain of events could have happened without side projects – I could’ve gotten these opportunities by meeting someone at a bar (unlikely, but who knows). But we, as software engineers, are capable of tilting chance towards us by utilizing our skills. Side projects are our “extracurricular activities”, and they often lead to unpredictable, but rather positive chains of events. They would rarely be the only factor, but they are certainly great at unlocking potential.

The post The Benefits of Side Projects appeared first on Bozho's tech blog.

The Practical Effects of GDPR at Backblaze

Post Syndicated from Andy Klein original https://www.backblaze.com/blog/the-practical-effects-of-gdpr-at-backblaze/


GDPR day, May 25, 2018, is nearly here. On that day, will your inbox explode with update notices, opt-in agreements, and offers from lawyers searching for GDPR violators? Perhaps all the companies on earth that are not GDPR ready will just dissolve into dust. More likely, there will be some changes, but business as usual will continue and we’ll all be more aware of data privacy. Let’s go with the last one.

What’s Different With GDPR at Backblaze

The biggest difference you’ll notice is a completely updated Privacy Policy. Last week we sent out a service email announcing the new Privacy Policy. Some people asked what was different. Basically everything. About 95% of the agreement was rewritten. In the agreement, we added in the appropriate provisions required by GDPR, and hopefully did a better job specifying the data we collect from you, why we collect it, and what we are going to do with it.

As a reminder, at Backblaze your data falls into two catagories. The first type of data is the data you store with us — stored data. These are the files and objects you upload and store, and as needed, restore. We do not share this data. We do not process this data, except as requested by you to store and restore the data. We do not analyze this data looking for keywords, tags, images, etc. No one outside of Backblaze has access to this data unless you explicitly shared the data by providing that person access to one or more files.

The second type of data is your account data. Some of your account data is considered personal data. This is the information we collect from you to provide our Personal Backup, Business Backup and B2 Cloud Storage services. Examples include your email address to provide access to your account, or the name of your computer so we can organize your files like they are arranged on your computer to make restoration easier. We have written a number of Help Articles covering the different ways this information is collected and processed. In addition, these help articles outline the various “rights” granted via GDPR. We will continue to add help articles over the coming weeks to assist in making it easy to work with us to understand and exercise your rights.

What’s New With GDPR at Backblaze

The most obvious addition is the Data Processing Addendum (DPA). This covers how we protect the data you store with us, i.e. stored data. As noted above, we don’t do anything with your data, except store it and keep it safe until you need it. Now we have a separate document saying that.

It is important to note the new Data Processing Addendum is now incorporated by reference into our Terms of Service, which everyone agrees to when they sign up for any of our services. Now all of our customers have a shiny new Data Processing Agreement to go along with the updated Privacy Policy. We promise they are not long or complicated, and we encourage you to read them. If you have any questions, stop by our GDPR help section on our website.

Patience, Please

Every company we have dealt with over the last few months is working hard to comply with GDPR. It has been a tough road whether you tried to do it yourself or like Backblaze, hired an EU-based law firm for advice. Over the coming weeks and months as you reach out to discover and assert your rights, please have a little patience. We are all going through a steep learning curve as GDPR gets put into practice. Along the way there are certain to be some growing pains — give us a chance, we all want to get it right.

Regardless, at Backblaze we’ve been diligently protecting our customers’ data for over 11 years and nothing that will happen on May 25th will change that.

The post The Practical Effects of GDPR at Backblaze appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

RFC: LWN’s draft updated privacy policy

Post Syndicated from corbet original https://lwn.net/Articles/755089/rss

It is the season for web sites to be updating their privacy policies and
obtaining consent from their users for whatever data they collect. LWN,
being short of staff with the time or interest to work in this area, is
rather late to this game. The first step is an updated
privacy policy, which we’re now putting out for review. Little has changed
from the current version; we still don’t
collect much data, share data with others, or attempt to
monetize what we have in any way. We would like to ask interested readers
to have a look and let us know about any potential problems they see.

Firefox 60 released

Post Syndicated from ris original https://lwn.net/Articles/754040/rss

Mozilla has released Firefox 60. From the release
notes
: “Firefox 60 offers something for everyone and a little
something extra for everyone who deploys Firefox in an enterprise environment. This release includes changes that give you more content and more ways to customize your New Tab/Firefox Home. It also introduces support for the Web Authentication API, which means you can log in to websites in Firefox with USB tokens like YubiKey.
Firefox 60 also brings a new policy engine and Group Policy support for
enterprise deployments. For more info about why and how to use Firefox in
the enterprise, see this blog post.

How AWS Meets a Physical Separation Requirement with a Logical Separation Approach

Post Syndicated from Min Hyun original https://aws.amazon.com/blogs/security/how-aws-meets-a-physical-separation-requirement-with-a-logical-separation-approach/

We have a new resource available to help you meet a requirement for physically-separated infrastructure using logical separation in the AWS cloud. Our latest guide, Logical Separation: An Evaluation of the U.S. Department of Defense Cloud Security Requirements for Sensitive Workloads outlines how AWS meets the U.S. Department of Defense’s (DoD) stringent physical separation requirement by pioneering a three-pronged logical separation approach that leverages virtualization, encryption, and deploying compute to dedicated hardware.

This guide will help you understand logical separation in the cloud and demonstrates its advantages over a traditional physical separation model. Embracing this approach can help organizations confidently meet or exceed security requirements found in traditional on-premises environments, while also providing increased security control and flexibility.

Logical Separation is the second guide in the AWS Government Handbook Series, which examines cybersecurity policy initiatives and identifies best practices.

If you have questions or want to learn more, contact your account executive or AWS Support.

Ray Ozzie’s Encryption Backdoor

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/05/ray_ozzies_encr.html

Last month, Wired published a long article about Ray Ozzie and his supposed new scheme for adding a backdoor in encrypted devices. It’s a weird article. It paints Ozzie’s proposal as something that “attains the impossible” and “satisfies both law enforcement and privacy purists,” when (1) it’s barely a proposal, and (2) it’s essentially the same key escrow scheme we’ve been hearing about for decades.

Basically, each device has a unique public/private key pair and a secure processor. The public key goes into the processor and the device, and is used to encrypt whatever user key encrypts the data. The private key is stored in a secure database, available to law enforcement on demand. The only other trick is that for law enforcement to use that key, they have to put the device in some sort of irreversible recovery mode, which means it can never be used again. That’s basically it.

I have no idea why anyone is talking as if this were anything new. Several cryptographers have already explained why this key escrow scheme is no better than any other key escrow scheme. The short answer is (1) we won’t be able to secure that database of backdoor keys, (2) we don’t know how to build the secure coprocessor the scheme requires, and (3) it solves none of the policy problems around the whole system. This is the typical mistake non-cryptographers make when they approach this problem: they think that the hard part is the cryptography to create the backdoor. That’s actually the easy part. The hard part is ensuring that it’s only used by the good guys, and there’s nothing in Ozzie’s proposal that addresses any of that.

I worry that this kind of thing is damaging in the long run. There should be some rule that any backdoor or key escrow proposal be a fully specified proposal, not just some cryptography and hand-waving notions about how it will be used in practice. And before it is analyzed and debated, it should have to satisfy some sort of basic security analysis. Otherwise, we’ll be swatting pseudo-proposals like this one, while those on the other side of this debate become increasingly convinced that it’s possible to design one of these things securely.

Already people are using the National Academies report on backdoors for law enforcement as evidence that engineers are developing workable and secure backdoors. Writing in Lawfare, Alan Z. Rozenshtein claims that the report — and a related New York Times story — “undermine the argument that secure third-party access systems are so implausible that it’s not even worth trying to develop them.” Susan Landau effectively corrects this misconception, but the damage is done.

Here’s the thing: it’s not hard to design and build a backdoor. What’s hard is building the systems — both technical and procedural — around them. Here’s Rob Graham:

He’s only solving the part we already know how to solve. He’s deliberately ignoring the stuff we don’t know how to solve. We know how to make backdoors, we just don’t know how to secure them.

A bunch of us cryptographers have already explained why we don’t think this sort of thing will work in the foreseeable future. We write:

Exceptional access would force Internet system developers to reverse “forward secrecy” design practices that seek to minimize the impact on user privacy when systems are breached. The complexity of today’s Internet environment, with millions of apps and globally connected services, means that new law enforcement requirements are likely to introduce unanticipated, hard to detect security flaws. Beyond these and other technical vulnerabilities, the prospect of globally deployed exceptional access systems raises difficult problems about how such an environment would be governed and how to ensure that such systems would respect human rights and the rule of law.

Finally, Matthew Green:

The reason so few of us are willing to bet on massive-scale key escrow systems is that we’ve thought about it and we don’t think it will work. We’ve looked at the threat model, the usage model, and the quality of hardware and software that exists today. Our informed opinion is that there’s no detection system for key theft, there’s no renewability system, HSMs are terrifically vulnerable (and the companies largely staffed with ex-intelligence employees), and insiders can be suborned. We’re not going to put the data of a few billion people on the line an environment where we believe with high probability that the system will fail.

EDITED TO ADD (5/14): An analysis of the proposal.

Analyze data in Amazon DynamoDB using Amazon SageMaker for real-time prediction

Post Syndicated from YongSeong Lee original https://aws.amazon.com/blogs/big-data/analyze-data-in-amazon-dynamodb-using-amazon-sagemaker-for-real-time-prediction/

Many companies across the globe use Amazon DynamoDB to store and query historical user-interaction data. DynamoDB is a fast NoSQL database used by applications that need consistent, single-digit millisecond latency.

Often, customers want to turn their valuable data in DynamoDB into insights by analyzing a copy of their table stored in Amazon S3. Doing this separates their analytical queries from their low-latency critical paths. This data can be the primary source for understanding customers’ past behavior, predicting future behavior, and generating downstream business value. Customers often turn to DynamoDB because of its great scalability and high availability. After a successful launch, many customers want to use the data in DynamoDB to predict future behaviors or provide personalized recommendations.

DynamoDB is a good fit for low-latency reads and writes, but it’s not practical to scan all data in a DynamoDB database to train a model. In this post, I demonstrate how you can use DynamoDB table data copied to Amazon S3 by AWS Data Pipeline to predict customer behavior. I also demonstrate how you can use this data to provide personalized recommendations for customers using Amazon SageMaker. You can also run ad hoc queries using Amazon Athena against the data. DynamoDB recently released on-demand backups to create full table backups with no performance impact. However, it’s not suitable for our purposes in this post, so I chose AWS Data Pipeline instead to create managed backups are accessible from other services.

To do this, I describe how to read the DynamoDB backup file format in Data Pipeline. I also describe how to convert the objects in S3 to a CSV format that Amazon SageMaker can read. In addition, I show how to schedule regular exports and transformations using Data Pipeline. The sample data used in this post is from Bank Marketing Data Set of UCI.

The solution that I describe provides the following benefits:

  • Separates analytical queries from production traffic on your DynamoDB table, preserving your DynamoDB read capacity units (RCUs) for important production requests
  • Automatically updates your model to get real-time predictions
  • Optimizes for performance (so it doesn’t compete with DynamoDB RCUs after the export) and for cost (using data you already have)
  • Makes it easier for developers of all skill levels to use Amazon SageMaker

All code and data set in this post are available in this .zip file.

Solution architecture

The following diagram shows the overall architecture of the solution.

The steps that data follows through the architecture are as follows:

  1. Data Pipeline regularly copies the full contents of a DynamoDB table as JSON into an S3
  2. Exported JSON files are converted to comma-separated value (CSV) format to use as a data source for Amazon SageMaker.
  3. Amazon SageMaker renews the model artifact and update the endpoint.
  4. The converted CSV is available for ad hoc queries with Amazon Athena.
  5. Data Pipeline controls this flow and repeats the cycle based on the schedule defined by customer requirements.

Building the auto-updating model

This section discusses details about how to read the DynamoDB exported data in Data Pipeline and build automated workflows for real-time prediction with a regularly updated model.

Download sample scripts and data

Before you begin, take the following steps:

  1. Download sample scripts in this .zip file.
  2. Unzip the src.zip file.
  3. Find the automation_script.sh file and edit it for your environment. For example, you need to replace 's3://<your bucket>/<datasource path>/' with your own S3 path to the data source for Amazon ML. In the script, the text enclosed by angle brackets—< and >—should be replaced with your own path.
  4. Upload the json-serde-1.3.6-SNAPSHOT-jar-with-dependencies.jar file to your S3 path so that the ADD jar command in Apache Hive can refer to it.

For this solution, the banking.csv  should be imported into a DynamoDB table.

Export a DynamoDB table

To export the DynamoDB table to S3, open the Data Pipeline console and choose the Export DynamoDB table to S3 template. In this template, Data Pipeline creates an Amazon EMR cluster and performs an export in the EMRActivity activity. Set proper intervals for backups according to your business requirements.

One core node(m3.xlarge) provides the default capacity for the EMR cluster and should be suitable for the solution in this post. Leave the option to resize the cluster before running enabled in the TableBackupActivity activity to let Data Pipeline scale the cluster to match the table size. The process of converting to CSV format and renewing models happens in this EMR cluster.

For a more in-depth look at how to export data from DynamoDB, see Export Data from DynamoDB in the Data Pipeline documentation.

Add the script to an existing pipeline

After you export your DynamoDB table, you add an additional EMR step to EMRActivity by following these steps:

  1. Open the Data Pipeline console and choose the ID for the pipeline that you want to add the script to.
  2. For Actions, choose Edit.
  3. In the editing console, choose the Activities category and add an EMR step using the custom script downloaded in the previous section, as shown below.

Paste the following command into the new step after the data ­­upload step:

s3://#{myDDBRegion}.elasticmapreduce/libs/script-runner/script-runner.jar,s3://<your bucket name>/automation_script.sh,#{output.directoryPath},#{myDDBRegion}

The element #{output.directoryPath} references the S3 path where the data pipeline exports DynamoDB data as JSON. The path should be passed to the script as an argument.

The bash script has two goals, converting data formats and renewing the Amazon SageMaker model. Subsequent sections discuss the contents of the automation script.

Automation script: Convert JSON data to CSV with Hive

We use Apache Hive to transform the data into a new format. The Hive QL script to create an external table and transform the data is included in the custom script that you added to the Data Pipeline definition.

When you run the Hive scripts, do so with the -e option. Also, define the Hive table with the 'org.openx.data.jsonserde.JsonSerDe' row format to parse and read JSON format. The SQL creates a Hive EXTERNAL table, and it reads the DynamoDB backup data on the S3 path passed to it by Data Pipeline.

Note: You should create the table with the “EXTERNAL” keyword to avoid the backup data being accidentally deleted from S3 if you drop the table.

The full automation script for converting follows. Add your own bucket name and data source path in the highlighted areas.

#!/bin/bash
hive -e "
ADD jar s3://<your bucket name>/json-serde-1.3.6-SNAPSHOT-jar-with-dependencies.jar ; 
DROP TABLE IF EXISTS blog_backup_data ;
CREATE EXTERNAL TABLE blog_backup_data (
 customer_id map<string,string>,
 age map<string,string>, job map<string,string>, 
 marital map<string,string>,education map<string,string>, 
 default map<string,string>, housing map<string,string>,
 loan map<string,string>, contact map<string,string>, 
 month map<string,string>, day_of_week map<string,string>, 
 duration map<string,string>, campaign map<string,string>,
 pdays map<string,string>, previous map<string,string>, 
 poutcome map<string,string>, emp_var_rate map<string,string>, 
 cons_price_idx map<string,string>, cons_conf_idx map<string,string>,
 euribor3m map<string,string>, nr_employed map<string,string>, 
 y map<string,string> ) 
ROW FORMAT SERDE 'org.openx.data.jsonserde.JsonSerDe' 
LOCATION '$1/';

INSERT OVERWRITE DIRECTORY 's3://<your bucket name>/<datasource path>/' 
SELECT concat( customer_id['s'],',', 
 age['n'],',', job['s'],',', 
 marital['s'],',', education['s'],',', default['s'],',', 
 housing['s'],',', loan['s'],',', contact['s'],',', 
 month['s'],',', day_of_week['s'],',', duration['n'],',', 
 campaign['n'],',',pdays['n'],',',previous['n'],',', 
 poutcome['s'],',', emp_var_rate['n'],',', cons_price_idx['n'],',',
 cons_conf_idx['n'],',', euribor3m['n'],',', nr_employed['n'],',', y['n'] ) 
FROM blog_backup_data
WHERE customer_id['s'] > 0 ; 

After creating an external table, you need to read data. You then use the INSERT OVERWRITE DIRECTORY ~ SELECT command to write CSV data to the S3 path that you designated as the data source for Amazon SageMaker.

Depending on your requirements, you can eliminate or process the columns in the SELECT clause in this step to optimize data analysis. For example, you might remove some columns that have unpredictable correlations with the target value because keeping the wrong columns might expose your model to “overfitting” during the training. In this post, customer_id  columns is removed. Overfitting can make your prediction weak. More information about overfitting can be found in the topic Model Fit: Underfitting vs. Overfitting in the Amazon ML documentation.

Automation script: Renew the Amazon SageMaker model

After the CSV data is replaced and ready to use, create a new model artifact for Amazon SageMaker with the updated dataset on S3.  For renewing model artifact, you must create a new training job.  Training jobs can be run using the AWS SDK ( for example, Amazon SageMaker boto3 ) or the Amazon SageMaker Python SDK that can be installed with “pip install sagemaker” command as well as the AWS CLI for Amazon SageMaker described in this post.

In addition, consider how to smoothly renew your existing model without service impact, because your model is called by applications in real time. To do this, you need to create a new endpoint configuration first and update a current endpoint with the endpoint configuration that is just created.

#!/bin/bash
## Define variable 
REGION=$2
DTTIME=`date +%Y-%m-%d-%H-%M-%S`
ROLE="<your AmazonSageMaker-ExecutionRole>" 


# Select containers image based on region.  
case "$REGION" in
"us-west-2" )
    IMAGE="174872318107.dkr.ecr.us-west-2.amazonaws.com/linear-learner:latest"
    ;;
"us-east-1" )
    IMAGE="382416733822.dkr.ecr.us-east-1.amazonaws.com/linear-learner:latest" 
    ;;
"us-east-2" )
    IMAGE="404615174143.dkr.ecr.us-east-2.amazonaws.com/linear-learner:latest" 
    ;;
"eu-west-1" )
    IMAGE="438346466558.dkr.ecr.eu-west-1.amazonaws.com/linear-learner:latest" 
    ;;
 *)
    echo "Invalid Region Name"
    exit 1 ;  
esac

# Start training job and creating model artifact 
TRAINING_JOB_NAME=TRAIN-${DTTIME} 
S3OUTPUT="s3://<your bucket name>/model/" 
INSTANCETYPE="ml.m4.xlarge"
INSTANCECOUNT=1
VOLUMESIZE=5 
aws sagemaker create-training-job --training-job-name ${TRAINING_JOB_NAME} --region ${REGION}  --algorithm-specification TrainingImage=${IMAGE},TrainingInputMode=File --role-arn ${ROLE}  --input-data-config '[{ "ChannelName": "train", "DataSource": { "S3DataSource": { "S3DataType": "S3Prefix", "S3Uri": "s3://<your bucket name>/<datasource path>/", "S3DataDistributionType": "FullyReplicated" } }, "ContentType": "text/csv", "CompressionType": "None" , "RecordWrapperType": "None"  }]'  --output-data-config S3OutputPath=${S3OUTPUT} --resource-config  InstanceType=${INSTANCETYPE},InstanceCount=${INSTANCECOUNT},VolumeSizeInGB=${VOLUMESIZE} --stopping-condition MaxRuntimeInSeconds=120 --hyper-parameters feature_dim=20,predictor_type=binary_classifier  

# Wait until job completed 
aws sagemaker wait training-job-completed-or-stopped --training-job-name ${TRAINING_JOB_NAME}  --region ${REGION}

# Get newly created model artifact and create model
MODELARTIFACT=`aws sagemaker describe-training-job --training-job-name ${TRAINING_JOB_NAME} --region ${REGION}  --query 'ModelArtifacts.S3ModelArtifacts' --output text `
MODELNAME=MODEL-${DTTIME}
aws sagemaker create-model --region ${REGION} --model-name ${MODELNAME}  --primary-container Image=${IMAGE},ModelDataUrl=${MODELARTIFACT}  --execution-role-arn ${ROLE}

# create a new endpoint configuration 
CONFIGNAME=CONFIG-${DTTIME}
aws sagemaker  create-endpoint-config --region ${REGION} --endpoint-config-name ${CONFIGNAME}  --production-variants  VariantName=Users,ModelName=${MODELNAME},InitialInstanceCount=1,InstanceType=ml.m4.xlarge

# create or update the endpoint
STATUS=`aws sagemaker describe-endpoint --endpoint-name  ServiceEndpoint --query 'EndpointStatus' --output text --region ${REGION} `
if [[ $STATUS -ne "InService" ]] ;
then
    aws sagemaker  create-endpoint --endpoint-name  ServiceEndpoint  --endpoint-config-name ${CONFIGNAME} --region ${REGION}    
else
    aws sagemaker  update-endpoint --endpoint-name  ServiceEndpoint  --endpoint-config-name ${CONFIGNAME} --region ${REGION}
fi

Grant permission

Before you execute the script, you must grant proper permission to Data Pipeline. Data Pipeline uses the DataPipelineDefaultResourceRole role by default. I added the following policy to DataPipelineDefaultResourceRole to allow Data Pipeline to create, delete, and update the Amazon SageMaker model and data source in the script.

{
 "Version": "2012-10-17",
 "Statement": [
 {
 "Effect": "Allow",
 "Action": [
 "sagemaker:CreateTrainingJob",
 "sagemaker:DescribeTrainingJob",
 "sagemaker:CreateModel",
 "sagemaker:CreateEndpointConfig",
 "sagemaker:DescribeEndpoint",
 "sagemaker:CreateEndpoint",
 "sagemaker:UpdateEndpoint",
 "iam:PassRole"
 ],
 "Resource": "*"
 }
 ]
}

Use real-time prediction

After you deploy a model into production using Amazon SageMaker hosting services, your client applications use this API to get inferences from the model hosted at the specified endpoint. This approach is useful for interactive web, mobile, or desktop applications.

Following, I provide a simple Python code example that queries against Amazon SageMaker endpoint URL with its name (“ServiceEndpoint”) and then uses them for real-time prediction.

=== Python sample for real-time prediction ===

#!/usr/bin/env python
import boto3
import json 

client = boto3.client('sagemaker-runtime', region_name ='<your region>' )
new_customer_info = '34,10,2,4,1,2,1,1,6,3,190,1,3,4,3,-1.7,94.055,-39.8,0.715,4991.6'
response = client.invoke_endpoint(
    EndpointName='ServiceEndpoint',
    Body=new_customer_info, 
    ContentType='text/csv'
)
result = json.loads(response['Body'].read().decode())
print(result)
--- output(response) ---
{u'predictions': [{u'score': 0.7528127431869507, u'predicted_label': 1.0}]}

Solution summary

The solution takes the following steps:

  1. Data Pipeline exports DynamoDB table data into S3. The original JSON data should be kept to recover the table in the rare event that this is needed. Data Pipeline then converts JSON to CSV so that Amazon SageMaker can read the data.Note: You should select only meaningful attributes when you convert CSV. For example, if you judge that the “campaign” attribute is not correlated, you can eliminate this attribute from the CSV.
  2. Train the Amazon SageMaker model with the new data source.
  3. When a new customer comes to your site, you can judge how likely it is for this customer to subscribe to your new product based on “predictedScores” provided by Amazon SageMaker.
  4. If the new user subscribes your new product, your application must update the attribute “y” to the value 1 (for yes). This updated data is provided for the next model renewal as a new data source. It serves to improve the accuracy of your prediction. With each new entry, your application can become smarter and deliver better predictions.

Running ad hoc queries using Amazon Athena

Amazon Athena is a serverless query service that makes it easy to analyze large amounts of data stored in Amazon S3 using standard SQL. Athena is useful for examining data and collecting statistics or informative summaries about data. You can also use the powerful analytic functions of Presto, as described in the topic Aggregate Functions of Presto in the Presto documentation.

With the Data Pipeline scheduled activity, recent CSV data is always located in S3 so that you can run ad hoc queries against the data using Amazon Athena. I show this with example SQL statements following. For an in-depth description of this process, see the post Interactive SQL Queries for Data in Amazon S3 on the AWS News Blog. 

Creating an Amazon Athena table and running it

Simply, you can create an EXTERNAL table for the CSV data on S3 in Amazon Athena Management Console.

=== Table Creation ===
CREATE EXTERNAL TABLE datasource (
 age int, 
 job string, 
 marital string , 
 education string, 
 default string, 
 housing string, 
 loan string, 
 contact string, 
 month string, 
 day_of_week string, 
 duration int, 
 campaign int, 
 pdays int , 
 previous int , 
 poutcome string, 
 emp_var_rate double, 
 cons_price_idx double,
 cons_conf_idx double, 
 euribor3m double, 
 nr_employed double, 
 y int 
)
ROW FORMAT DELIMITED 
FIELDS TERMINATED BY ',' ESCAPED BY '\\' LINES TERMINATED BY '\n' 
LOCATION 's3://<your bucket name>/<datasource path>/';

The following query calculates the correlation coefficient between the target attribute and other attributes using Amazon Athena.

=== Sample Query ===

SELECT corr(age,y) AS correlation_age_and_target, 
 corr(duration,y) AS correlation_duration_and_target, 
 corr(campaign,y) AS correlation_campaign_and_target,
 corr(contact,y) AS correlation_contact_and_target
FROM ( SELECT age , duration , campaign , y , 
 CASE WHEN contact = 'telephone' THEN 1 ELSE 0 END AS contact 
 FROM datasource 
 ) datasource ;

Conclusion

In this post, I introduce an example of how to analyze data in DynamoDB by using table data in Amazon S3 to optimize DynamoDB table read capacity. You can then use the analyzed data as a new data source to train an Amazon SageMaker model for accurate real-time prediction. In addition, you can run ad hoc queries against the data on S3 using Amazon Athena. I also present how to automate these procedures by using Data Pipeline.

You can adapt this example to your specific use case at hand, and hopefully this post helps you accelerate your development. You can find more examples and use cases for Amazon SageMaker in the video AWS 2017: Introducing Amazon SageMaker on the AWS website.

 


Additional Reading

If you found this post useful, be sure to check out Serving Real-Time Machine Learning Predictions on Amazon EMR and Analyzing Data in S3 using Amazon Athena.

 


About the Author

Yong Seong Lee is a Cloud Support Engineer for AWS Big Data Services. He is interested in every technology related to data/databases and helping customers who have difficulties in using AWS services. His motto is “Enjoy life, be curious and have maximum experience.”

 

 

Security updates for Tuesday

Post Syndicated from ris original https://lwn.net/Articles/753257/rss

Security updates have been issued by Fedora (cups-filters, ghostscript, glusterfs, PackageKit, qpdf, and xen), Mageia (anki, libofx, ming, sox, webkit2, and xdg-user-dirs), Oracle (corosync, java-1.7.0-openjdk, and pcs), Red Hat (java-1.7.0-openjdk), Scientific Linux (corosync, firefox, gcc, glibc, golang, java-1.7.0-openjdk, java-1.8.0-openjdk, kernel, krb5, librelp, libvncserver, libvorbis, ntp, openssh, openssl, PackageKit, patch, pcs, policycoreutils, qemu-kvm, and xdg-user-dirs), Slackware (libwmf and mozilla), and Ubuntu (apache2, ghostscript, mysql-5.7, wavpack, and webkit2gtk).