Tag Archives: policies

Message Filtering Operators for Numeric Matching, Prefix Matching, and Blacklisting in Amazon SNS

Post Syndicated from Christie Gifrin original https://aws.amazon.com/blogs/compute/message-filtering-operators-for-numeric-matching-prefix-matching-and-blacklisting-in-amazon-sns/

This blog was contributed by Otavio Ferreira, Software Development Manager for Amazon SNS

Message filtering simplifies the overall pub/sub messaging architecture by offloading message filtering logic from subscribers, as well as message routing logic from publishers. The initial launch of message filtering provided a basic operator that was based on exact string comparison. For more information, see Simplify Your Pub/Sub Messaging with Amazon SNS Message Filtering.

Today, AWS is announcing an additional set of filtering operators that bring even more power and flexibility to your pub/sub messaging use cases.

Message filtering operators

Amazon SNS now supports both numeric and string matching. Specifically, string matching operators allow for exact, prefix, and “anything-but” comparisons, while numeric matching operators allow for exact and range comparisons, as outlined below. Numeric matching operators work for values between -10e9 and +10e9 inclusive, with five digits of accuracy right of the decimal point.

  • Exact matching on string values (Whitelisting): Subscription filter policy   {"sport": ["rugby"]} matches message attribute {"sport": "rugby"} only.
  • Anything-but matching on string values (Blacklisting): Subscription filter policy {"sport": [{"anything-but": "rugby"}]} matches message attributes such as {"sport": "baseball"} and {"sport": "basketball"} and {"sport": "football"} but not {"sport": "rugby"}
  • Prefix matching on string values: Subscription filter policy {"sport": [{"prefix": "bas"}]} matches message attributes such as {"sport": "baseball"} and {"sport": "basketball"}
  • Exact matching on numeric values: Subscription filter policy {"balance": [{"numeric": ["=", 301.5]}]} matches message attributes {"balance": 301.500} and {"balance": 3.015e2}
  • Range matching on numeric values: Subscription filter policy {"balance": [{"numeric": ["<", 0]}]} matches negative numbers only, and {"balance": [{"numeric": [">", 0, "<=", 150]}]} matches any positive number up to 150.

As usual, you may apply the “AND” logic by appending multiple keys in the subscription filter policy, and the “OR” logic by appending multiple values for the same key, as follows:

  • AND logic: Subscription filter policy {"sport": ["rugby"], "language": ["English"]} matches only messages that carry both attributes {"sport": "rugby"} and {"language": "English"}
  • OR logic: Subscription filter policy {"sport": ["rugby", "football"]} matches messages that carry either the attribute {"sport": "rugby"} or {"sport": "football"}

Message filtering operators in action

Here’s how this new set of filtering operators works. The following example is based on a pharmaceutical company that develops, produces, and markets a variety of prescription drugs, with research labs located in Asia Pacific and Europe. The company built an internal procurement system to manage the purchasing of lab supplies (for example, chemicals and utensils), office supplies (for example, paper, folders, and markers) and tech supplies (for example, laptops, monitors, and printers) from global suppliers.

This distributed system is composed of the four following subsystems:

  • A requisition system that presents the catalog of products from suppliers, and takes orders from buyers
  • An approval system for orders targeted to Asia Pacific labs
  • Another approval system for orders targeted to European labs
  • A fulfillment system that integrates with shipping partners

As shown in the following diagram, the company leverages AWS messaging services to integrate these distributed systems.

  • Firstly, an SNS topic named “Orders” was created to take all orders placed by buyers on the requisition system.
  • Secondly, two Amazon SQS queues, named “Lab-Orders-AP” and “Lab-Orders-EU” (for Asia Pacific and Europe respectively), were created to backlog orders that are up for review on the approval systems.
  • Lastly, an SQS queue named “Common-Orders” was created to backlog orders that aren’t related to lab supplies, which can already be picked up by shipping partners on the fulfillment system.

The company also uses AWS Lambda functions to automatically process lab supply orders that don’t require approval or which are invalid.

In this example, because different types of orders have been published to the SNS topic, the subscribing endpoints have had to set advanced filter policies on their SNS subscriptions, to have SNS automatically filter out orders they can’t deal with.

As depicted in the above diagram, the following five filter policies have been created:

  • The SNS subscription that points to the SQS queue “Lab-Orders-AP” sets a filter policy that matches lab supply orders, with a total value greater than $1,000, and that target Asia Pacific labs only. These more expensive transactions require an approver to review orders placed by buyers.
  • The SNS subscription that points to the SQS queue “Lab-Orders-EU” sets a filter policy that matches lab supply orders, also with a total value greater than $1,000, but that target European labs instead.
  • The SNS subscription that points to the Lambda function “Lab-Preapproved” sets a filter policy that only matches lab supply orders that aren’t as expensive, up to $1,000, regardless of their target lab location. These orders simply don’t require approval and can be automatically processed.
  • The SNS subscription that points to the Lambda function “Lab-Cancelled” sets a filter policy that only matches lab supply orders with total value of $0 (zero), regardless of their target lab location. These orders carry no actual items, obviously need neither approval nor fulfillment, and as such can be automatically canceled.
  • The SNS subscription that points to the SQS queue “Common-Orders” sets a filter policy that blacklists lab supply orders. Hence, this policy matches only office and tech supply orders, which have a more streamlined fulfillment process, and require no approval, regardless of price or target location.

After the company finished building this advanced pub/sub architecture, they were then able to launch their internal procurement system and allow buyers to begin placing orders. The diagram above shows six example orders published to the SNS topic. Each order contains message attributes that describe the order, and cause them to be filtered in a different manner, as follows:

  • Message #1 is a lab supply order, with a total value of $15,700 and targeting a research lab in Singapore. Because the value is greater than $1,000, and the location “Asia-Pacific-Southeast” matches the prefix “Asia-Pacific-“, this message matches the first SNS subscription and is delivered to SQS queue “Lab-Orders-AP”.
  • Message #2 is a lab supply order, with a total value of $1,833 and targeting a research lab in Ireland. Because the value is greater than $1,000, and the location “Europe-West” matches the prefix “Europe-“, this message matches the second SNS subscription and is delivered to SQS queue “Lab-Orders-EU”.
  • Message #3 is a lab supply order, with a total value of $415. Because the value is greater than $0 and less than $1,000, this message matches the third SNS subscription and is delivered to Lambda function “Lab-Preapproved”.
  • Message #4 is a lab supply order, but with a total value of $0. Therefore, it only matches the fourth SNS subscription, and is delivered to Lambda function “Lab-Cancelled”.
  • Messages #5 and #6 aren’t lab supply orders actually; one is an office supply order, and the other is a tech supply order. Therefore, they only match the fifth SNS subscription, and are both delivered to SQS queue “Common-Orders”.

Although each message only matched a single subscription, each was tested against the filter policy of every subscription in the topic. Hence, depending on which attributes are set on the incoming message, the message might actually match multiple subscriptions, and multiple deliveries will take place. Also, it is important to bear in mind that subscriptions with no filter policies catch every single message published to the topic, as a blank filter policy equates to a catch-all behavior.


Amazon SNS allows for both string and numeric filtering operators. As explained in this post, string operators allow for exact, prefix, and “anything-but” comparisons, while numeric operators allow for exact and range comparisons. These advanced filtering operators bring even more power and flexibility to your pub/sub messaging functionality and also allow you to simplify your architecture further by removing even more logic from your subscribers.

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). SNS filtering operators for numeric matching, prefix matching, and blacklisting are available now in all AWS Regions, for no extra charge.

To experiment with these new filtering operators yourself, and continue learning, try the 10-minute Tutorial Filter Messages Published to Topics. For more information, see Filtering Messages with Amazon SNS in the SNS documentation.

How to Delegate Administration of Your AWS Managed Microsoft AD Directory to Your On-Premises Active Directory Users

Post Syndicated from Vijay Sharma original https://aws.amazon.com/blogs/security/how-to-delegate-administration-of-your-aws-managed-microsoft-ad-directory-to-your-on-premises-active-directory-users/

You can now enable your on-premises users administer your AWS Directory Service for Microsoft Active Directory, also known as AWS Managed Microsoft AD. Using an Active Directory (AD) trust and the new AWS delegated AD security groups, you can grant administrative permissions to your on-premises users by managing group membership in your on-premises AD directory. This simplifies how you manage who can perform administration. It also makes it easier for your administrators because they can sign in to their existing workstation with their on-premises AD credential to administer your AWS Managed Microsoft AD.

AWS created new domain local AD security groups (AWS delegated groups) in your AWS Managed Microsoft AD directory. Each AWS delegated group has unique AD administrative permissions. Users that are members in the new AWS delegated groups get permissions to perform administrative tasks, such as add users, configure fine-grained password policies and enable Microsoft enterprise Certificate Authority. Because the AWS delegated groups are domain local in scope, you can use them through an AD Trust to your on-premises AD. This eliminates the requirement to create and use separate identities to administer your AWS Managed Microsoft AD. Instead, by adding selected on-premises users to desired AWS delegated groups, you can grant your administrators some or all of the permissions. You can simplify this even further by adding on-premises AD security groups to the AWS delegated groups. This enables you to add and remove users from your on-premises AD security group so that they can manage administrative permissions in your AWS Managed Microsoft AD.

In this blog post, I will show you how to delegate permissions to your on-premises users to perform an administrative task–configuring fine-grained password policies–in your AWS Managed Microsoft AD directory. You can follow the steps in this post to delegate other administrative permissions, such as configuring group Managed Service Accounts and Kerberos constrained delegation, to your on-premises users.


Until now, AWS Managed Microsoft AD delegated administrative permissions for your directory by creating AD security groups in your Organization Unit (OU) and authorizing these AWS delegated groups for common administrative activities. The admin user in your directory created user accounts within your OU, and granted these users permissions to administer your directory by adding them to one or more of these AWS delegated groups.

However, if you used your AWS Managed Microsoft AD with a trust to an on-premises AD forest, you couldn’t add users from your on-premises directory to these AWS delegated groups. This is because AWS created the AWS delegated groups with global scope, which restricts adding users from another forest. This necessitated that you create different user accounts in AWS Managed Microsoft AD for the purpose of administration. As a result, AD administrators typically had to remember additional credentials for AWS Managed Microsoft AD.

To address this, AWS created new AWS delegated groups with domain local scope in a separate OU called AWS Delegated Groups. These new AWS delegated groups with domain local scope are more flexible and permit adding users and groups from other domains and forests. This allows your admin user to delegate your on-premises users and groups administrative permissions to your AWS Managed Microsoft AD directory.

Note: If you already have an existing AWS Managed Microsoft AD directory containing the original AWS delegated groups with global scope, AWS preserved the original AWS delegated groups in the event you are currently using them with identities in AWS Managed Microsoft AD. AWS recommends that you transition to use the new AWS delegated groups with domain local scope. All newly created AWS Managed Microsoft AD directories have the new AWS delegated groups with domain local scope only.

Now, I will show you the steps to delegate administrative permissions to your on-premises users and groups to configure fine-grained password policies in your AWS Managed Microsoft AD directory.


For this post, I assume you are familiar with AD security groups and how security group scope rules work. I also assume you are familiar with AD trusts.

The instructions in this blog post require you to have the following components running:

Solution overview

I will now show you how to manage which on-premises users have delegated permissions to administer your directory by efficiently using on-premises AD security groups to manage these permissions. I will do this by:

  1. Adding on-premises groups to an AWS delegated group. In this step, you sign in to management instance connected to AWS Managed Microsoft AD directory as admin user and add on-premises groups to AWS delegated groups.
  2. Administer your AWS Managed Microsoft AD directory as on-premises user. In this step, you sign in to a workstation connected to your on-premises AD using your on-premises credentials and administer your AWS Managed Microsoft AD directory.

For the purpose of this blog, I already have an on-premises AD directory (in this case, on-premises.com). I also created an AWS Managed Microsoft AD directory (in this case, corp.example.com) that I use with Amazon RDS for SQL Server. To enable Integrated Windows Authentication to my on-premises.com domain, I established a one-way outgoing trust from my AWS Managed Microsoft AD directory to my on-premises AD directory. To administer my AWS Managed Microsoft AD, I created an Amazon EC2 for Windows Server instance (in this case, Cloud Management). I also have an on-premises workstation (in this case, On-premises Management), that is connected to my on-premises AD directory.

The following diagram represents the relationships between the on-premises AD and the AWS Managed Microsoft AD directory.

The left side represents the AWS Cloud containing AWS Managed Microsoft AD directory. I connected the directory to the on-premises AD directory via a 1-way forest trust relationship. When AWS created my AWS Managed Microsoft AD directory, AWS created a group called AWS Delegated Fine Grained Password Policy Administrators that has permissions to configure fine-grained password policies in AWS Managed Microsoft AD.

The right side of the diagram represents the on-premises AD directory. I created a global AD security group called On-premises fine grained password policy admins and I configured it so all members can manage fine grained password policies in my on-premises AD. I have two administrators in my company, John and Richard, who I added as members of On-premises fine grained password policy admins. I want to enable John and Richard to also manage fine grained password policies in my AWS Managed Microsoft AD.

While I could add John and Richard to the AWS Delegated Fine Grained Password Policy Administrators individually, I want a more efficient way to delegate and remove permissions for on-premises users to manage fine grained password policies in my AWS Managed Microsoft AD. In fact, I want to assign permissions to the same people that manage password policies in my on-premises directory.

Diagram showing delegation of administrative permissions to on-premises users

To do this, I will:

  1. As admin user, add the On-premises fine grained password policy admins as member of the AWS Delegated Fine Grained Password Policy Administrators security group from my Cloud Management machine.
  2. Manage who can administer password policies in my AWS Managed Microsoft AD directory by adding and removing users as members of the On-premises fine grained password policy admins. Doing so enables me to perform all my delegation work in my on-premises directory without the need to use a remote desktop protocol (RDP) session to my Cloud Management instance. In this case, Richard, who is a member of On-premises fine grained password policy admins group can now administer AWS Managed Microsoft AD directory from On-premises Management workstation.

Although I’m showing a specific case using fine grained password policy delegation, you can do this with any of the new AWS delegated groups and your on-premises groups and users.

Let’s get started.

Step 1 – Add on-premises groups to AWS delegated groups

In this step, open an RDP session to the Cloud Management instance and sign in as the admin user in your AWS Managed Microsoft AD directory. Then, add your users and groups from your on-premises AD to AWS delegated groups in AWS Managed Microsoft AD directory. In this example, I do the following:

  1. Sign in to the Cloud Management instance with the user name admin and the password that you set for the admin user when you created your directory.
  2. Open the Microsoft Windows Server Manager and navigate to Tools > Active Directory Users and Computers.
  3. Switch to the tree view and navigate to corp.example.com > AWS Delegated Groups. Right-click AWS Delegated Fine Grained Password Policy Administrators and select Properties.
  4. In the AWS Delegated Fine Grained Password Policy window, switch to Members tab and choose Add.
  5. In the Select Users, Contacts, Computers, Service Accounts, or Groups window, choose Locations.
  6. In the Locations window, select on-premises.com domain and choose OK.
  7. In the Enter the object names to select box, enter on-premises fine grained password policy admins and choose Check Names.
  8. Because I have a 1-way trust from AWS Managed Microsoft AD to my on-premises AD, Windows prompts me to enter credentials for an on-premises user account that has permissions to complete the search. If I had a 2-way trust and the admin account in my AWS Managed Microsoft AD has permissions to read my on-premises directory, Windows will not prompt me.In the Windows Security window, enter the credentials for an account with permissions for on-premises.com and choose OK.
  9. Click OK to add On-premises fine grained password policy admins group as a member of the AWS Delegated Fine Grained Password Policy Administrators group in your AWS Managed Microsoft AD directory.

At this point, any user that is a member of On-premises fine grained password policy admins group has permissions to manage password policies in your AWS Managed Microsoft AD directory.

Step 2 – Administer your AWS Managed Microsoft AD as on-premises user

Any member of the on-premises group(s) that you added to an AWS delegated group inherited the permissions of the AWS delegated group.

In this example, Richard signs in to the On-premises Management instance. Because Richard inherited permissions from Delegated Fine Grained Password Policy Administrators, he can now administer fine grained password policies in the AWS Managed Microsoft AD directory using on-premises credentials.

  1. Sign in to the On-premises Management instance as Richard.
  2. Open the Microsoft Windows Server Manager and navigate to Tools > Active Directory Users and Computers.
  3. Switch to the tree view, right-click Active Directory Users and Computers, and then select Change Domain.
  4. In the Change Domain window, enter corp.example.com, and then choose OK.
  5. You’ll be connected to your AWS Managed Microsoft AD domain:

Richard can now administer the password policies. Because John is also a member of the AWS delegated group, John can also perform password policy administration the same way.

In future, if Richard moves to another division within the company and you hire Judy as a replacement for Richard, you can simply remove Richard from On-premises fine grained password policy admins group and add Judy to this group. Richard will no longer have administrative permissions, while Judy can now administer password policies for your AWS Managed Microsoft AD directory.


We’ve tried to make it easier for you to administer your AWS Managed Microsoft AD directory by creating AWS delegated groups with domain local scope. You can add your on-premises AD groups to the AWS delegated groups. You can then control who can administer your directory by managing group membership in your on-premises AD directory. Your administrators can sign in to their existing on-premises workstations using their on-premises credentials and administer your AWS Managed Microsoft AD directory. I encourage you to explore the new AWS delegated security groups by using Active Directory Users and Computers from the management instance for your AWS Managed Microsoft AD. To learn more about AWS Directory Service, see the AWS Directory Service home page. If you have questions, please post them on the Directory Service forum. If you have comments about this post, submit them in the “Comments” section below.


Judge Issues Mixed Order in RIAA’s Piracy Case Against ISP Grande

Post Syndicated from Ernesto original https://torrentfreak.com/judge-issues-mixed-order-in-riaas-piracy-case-against-isp-grande-180306/

Regular Internet providers are being put under increasing pressure for not doing enough to curb copyright infringement.

Last year several major record labels, represented by the RIAA, filed a lawsuit in a Texas District Court, accusing ISP Grande Communications of turning a blind eye on its pirating subscribers.

According to the RIAA, the Internet provider knew that some of its subscribers were frequently distributing copyrighted material, and accused the company of failing to take any meaningful action in response.

Grande disagreed with this assertion and filed a motion to dismiss the case. The ISP argued that it doesn’t encourage any of its customers to download copyrighted works, and that it has no control over the content subscribers access.

The Internet provider admitted that it received millions of takedown notices through the piracy tracking company Rightscorp. However, it believes that these notices are flawed and not worthy of acting upon. It was not keeping subscribers on board with a profit motive, as the RIAA suggested.

A few days ago US Magistrate Judge Andrew Austin issued his “report and recommendation” on the motions to dismiss, which brings some good and bad news for both sides.

First of all, Judge Austin recommends granting the motion to dismiss the piracy claims against Grande’s management company Patriot Media Consulting, which is also listed as a defendant.

According to the order, the RIAA failed to show that Patriot employees were involved in the decisions or actions that led to the infringements, only that they may have been involved in formulating Grande’s infringement related policies.

“This is a far cry from showing that Patriot as an entity was an active participant in the alleged secondary infringement,” Judge Austin writes.

Moving to Grande Communications itself, Judge Austin recommends dropping the vicarious infringement claim, as Grande requested. To show vicarious infringement, the RIAA would have to prove that the ISP has a direct financial interest in the infringing activity. That is not the case here.

The record labels argued that the availability of copyrighted music lures customers, but the Judge found this allegation too vague, as it would apply to all ISPs.

“There are no allegations that Grande’s actions in failing to adequately police their infringing subscribers is a draw to subscribers to purchase its services, so that they can then use those services to infringe on UMG’s (and others’) copyrights,” Judge Austin argues.

“Instead UMG only alleges that the existence of music and the BitTorrent protocol is the draw. But that would impose liability on every ISP, as the music at issue is available on the Internet generally, as is the BitTorrent protocol, and is not something exclusively available through Grande’s services.”

While the above is good news for the Internet provider, the report and recommendation opt to keep the contributory infringement claim alive. Contributory copyright infringement happens where a defendant intentionally induces or encourages direct infringement.

Grande argued that Rightcorp’s notices were not sufficient to show that copyrighted material was ever downloaded, but Judge Austin disagrees. The RIAA has made a “plausible claim” that the ISP’s subscribers are infringing the labels’ copyrights.

“It would be inappropriate to dismiss the case based on factual allegations Grande makes about the Rightscorp notices and system, without any evidence to back those up,” Judge Austin’s recommendation reads.

In addition, Grande also argued that it’s protected from a secondary copyright infringement claim under the “staple article of commerce” doctrine, as “it is beyond dispute” that ISPs have numerous non-infringing uses.

Referring to the legal case between BMG and Cox Communications, Judge Austin says that this isn’t as clear as Grande suggests.

“The Court acknowledges that this is not yet a well-defined area of the law, and that there are good arguments on both sides of this issue,” the recommendation reads.

“However, at this point in the case, the Court is persuaded that UMG has pled a plausible claim of secondary infringement based on Grande’s alleged failure to act when presented with evidence of ongoing, pervasive infringement by its subscribers.”

The recommendation, therefore, is to deny the motion to dismiss the contributory infringement claim against Grande. If the U.S. District Court Judge adopts this position, it would mean that the case is heading to trial based on this claim.

Judge Austin’s full report and recommendations filing is available here (pdf).

Source: TF, for the latest info on copyright, file-sharing, torrent sites and more. We also have VPN discounts, offers and coupons

Setting up bug bounties for success

Post Syndicated from Michal Zalewski original https://lcamtuf.blogspot.com/2018/03/setting-up-bug-bounties-for-success.html

Bug bounties end up in the news with some regularity, usually for the wrong reasons. I’ve been itching to write
about that for a while – but instead of dwelling on the mistakes of the bygone days, I figured it may be better to
talk about some of the ways to get vulnerability rewards right.

What do you get out of bug bounties?

There’s plenty of differing views, but I like to think of such programs
simply as a bid on researchers’ time. In the most basic sense, you get three benefits:

  • Improved ability to detect bugs in production before they become major incidents.
  • A comparatively unbiased feedback loop to help you prioritize and measure other security work.
  • A robust talent pipeline for when you need to hire.

What bug bounties don’t offer?

You don’t get anything resembling a comprehensive security program or a systematic assessment of your platforms.
Researchers end up looking for bugs that offer favorable effort-to-payoff ratios for their skills and given the
very imperfect information they have about your enterprise. In other words, you may end up with a hundred
people looking for XSS and just one person looking for RCE.

Your reward structure can steer them toward the targets and bugs you care about, but it’s difficult to fully
eliminate this inherent skew. There’s only so far you can jack up your top-tier rewards, and only so far you can
go lowering the bottom-tier ones.

Don’t you have to outcompete the black market to get all the “good” bugs?

There is a free market price discovery component to it all: if you’re not getting the engagement you
were hoping for, you should probably consider paying more.

That said, there are going to be researchers who’d rather hurt you than work for you, no matter how much you pay;
you don’t have to win them over, and you don’t have to outspend every authoritarian government or
every crime syndicate. A bug bounty is effective simply if it attracts enough eyeballs to make bugs statistically
harder to find, and reduces the useful lifespan of any zero-days in black market trade. Plus, most
researchers don’t want their work to be used to crack down on dissidents in Egypt or Vietnam.

Another factor is that you’re paying for different things: a black market buyer probably wants a reliable exploit
capable of delivering payloads, and then demands silence for months or years to come; a vendor-run
bug bounty program is usually perfectly happy with a reproducible crash and doesn’t mind a researcher blogging
about their work.

In fact, while money is important, you will probably find out that it’s not enough to retain your top talent;
many folks want bug bounties to be more than a business transaction, and find a lot of value in having a close
relationship with your security team, comparing notes, and growing together. Fostering that partnership can
be more important than adding another $10,000 to your top reward.

How do I prevent it all from going horribly wrong?

Bug bounties are an unfamiliar beast to most lawyers and PR folks, so it’s a natural to be wary and try to plan
for every eventuality with pages and pages of impenetrable rules and fine-print legalese.

This is generally unnecessary: there is a strong self-selection bias, and almost every participant in a
vulnerability reward program will be coming to you in good faith. The more friendly, forthcoming, and
approachable you seem, and the more you treat them like peers, the more likely it is for your relationship to stay
positive. On the flip side, there is no faster way to make enemies than to make a security researcher feel that they
are now talking to a lawyer or to the PR dept.

Most people have strong opinions on disclosure policies; instead of imposing your own views, strive to patch reported bugs
reasonably quickly, and almost every reporter will play along. Demand researchers to cancel conference appearances,
take down blog posts, or sign NDAs, and you will sooner or later end up in the news.

But what if that’s not enough?

As with any business endeavor, mistakes will happen; total risk avoidance is seldom the answer. Learn to sincerely
apologize for mishaps; it’s not a sign of weakness to say “sorry, we messed up”. And you will almost certainly not end
up in the courtroom for doing so.

It’s good to foster a healthy and productive relationship with the community, so that they come to your defense when
something goes wrong. Encouraging people to disclose bugs and talk about their experiences is one way of accomplishing that.

What about extortion?

You should structure your program to naturally discourage bad behavior and make it stand out like a sore thumb.
Require bona fide reports with complete technical details before any reward decision is made by a panel of named peers;
and make it clear that you never demand non-disclosure as a condition of getting a reward.

To avoid researchers accidentally putting themselves in awkward situations, have clear rules around data exfiltration
and lateral movement: assure them that you will always pay based on the worst-case impact of their findings; in exchange,
ask them to stop as soon as they get a shell and never access any data that isn’t their own.

So… are there any downsides?

Yep. Other than souring up your relationship with the community if you implement your program wrong, the other consideration
is that bug bounties tend to generate a lot of noise from well-meaning but less-skilled researchers.

When this happens, do not get frustrated and do not penalize such participants; instead, help them grow. Consider
publishing educational articles, giving advice on how to investigate and structure reports, or
offering free workshops every now and then.

The other downside is cost; although bug bounties tend to offer far more bang for your buck than your average penetration
test, they are more random. The annual expenses tend to be fairly predictable, but there is always
some possibility of having to pay multiple top-tier rewards in rapid succession. This is the kind of uncertainty that
many mid-level budget planners react badly to.

Finally, you need to be able to fix the bugs you receive. It would be nuts to prefer to not know about the
vulnerabilities in the first place – but once you invite the research, the clock starts ticking and you need to
ship fixes reasonably fast.

So… should I try it?

There are folks who enthusiastically advocate for bug bounties in every conceivable situation, and people who dislike them
with fierce passion; both sentiments are usually strongly correlated with the line of business they are in.

In reality, bug bounties are not a cure-all, and there are some ways to make them ineffectual or even dangerous.
But they are not as risky or expensive as most people suspect, and when done right, they can actually be fun for your
team, too. You won’t know for sure until you try.

Central Logging in Multi-Account Environments

Post Syndicated from matouk original https://aws.amazon.com/blogs/architecture/central-logging-in-multi-account-environments/

Centralized logging is often required in large enterprise environments for a number of reasons, ranging from compliance and security to analytics and application-specific needs.

I’ve seen that in a multi-account environment, whether the accounts belong to the same line of business or multiple business units, collecting logs in a central, dedicated logging account is an established best practice. It helps security teams detect malicious activities both in real-time and during incident response. It provides protection to log data in case it is accidentally or intentionally deleted. It also helps application teams correlate and analyze log data across multiple application tiers.

This blog post provides a solution and building blocks to stream Amazon CloudWatch log data across accounts. In a multi-account environment this repeatable solution could be deployed multiple times to stream all relevant Amazon CloudWatch log data from all accounts to a centralized logging account.

Solution Summary 

The solution uses Amazon Kinesis Data Streams and a log destination to set up an endpoint in the logging account to receive streamed logs and uses Amazon Kinesis Data Firehose to deliver log data to the Amazon Simple Storage Solution (S3) bucket. Application accounts will subscribe to stream all (or part) of their Amazon CloudWatch logs to a defined destination in the logging account via subscription filters.

Below is a diagram illustrating how the various services work together.

In logging an account, a Kinesis Data Stream is created to receive streamed log data and a log destination is created to facilitate remote streaming, configured to use the Kinesis Data Stream as its target.

The Amazon Kinesis Data Firehose stream is created to deliver log data from the data stream to S3. The delivery stream uses a generic AWS Lambda function for data validation and transformation.

In each application account, a subscription filter is created between each Amazon CloudWatch log group and the destination created for this log group in the logging account.

The following steps are involved in setting up the central-logging solution:

  1. Create an Amazon S3 bucket for your central logging in the logging account
  2. Create an AWS Lambda function for log data transformation and decoding in logging account
  3. Create a central logging stack as a logging-account destination ready to receive streamed logs and deliver them to S3
  4. Create a subscription in application accounts to deliver logs from a specific CloudWatch log group to the logging account destination
  5. Create Amazon Athena tables to query and analyze log data in your logging account

Creating a log destination in your logging account

In this section, we will setup the logging account side of the solution, providing detail on the list above. The example I use is for the us-east-1 region, however any region where required services are available could be used.

It’s important to note that your logging-account destination and application-account subscription must be in the same region. You can deploy the solution multiple times to create destinations in all required regions if application accounts use multiple regions.

Step 1: Create an S3 bucket

Use the CloudFormation template below to create S3 bucket in logging account. This template also configures the bucket to archive log data to Glacier after 60 days.

  "Description": "CF Template to create S3 bucket for central logging",

      "Description":"Central logging bucket name"
   "CentralLoggingBucket" : {
      "Type" : "AWS::S3::Bucket",
      "Properties" : {
        "BucketName" : {"Ref": "BucketName"},
        "LifecycleConfiguration": {
            "Rules": [
                  "Id": "ArchiveToGlacier",
                  "Prefix": "",
                  "Status": "Enabled",
                      "TransitionInDays": "60",
                      "StorageClass": "GLACIER"

    	"Description" : "Central log bucket",
    	"Value" : {"Ref": "BucketName"} ,
    	"Export" : { "Name" : "CentralLogBucketName"}

To create your central-logging bucket do the following:

  1. Save the template file to your local developer machine as “central-log-bucket.json”
  2. From the CloudFormation console, select “create new stack” and import the file “central-log-bucket.json”
  3. Fill in the parameters and complete stack creation steps (as indicated in the screenshot below)
  4. Verify the bucket has been created successfully and take a note of the bucket name

Step 2: Create data processing Lambda function

Use the template below to create a Lambda function in your logging account that will be used by Amazon Firehose for data transformation during the delivery process to S3. This function is based on the AWS Lambda kinesis-firehose-cloudwatch-logs-processor blueprint.

The function could be created manually from the blueprint or using the cloudformation template below. To find the blueprint navigate to Lambda -> Create -> Function -> Blueprints

This function will unzip the event message, parse it and verify that it is a valid CloudWatch log event. Additional processing can be added if needed. As this function is generic, it could be reused by all log-delivery streams.

  "Description": "Create cloudwatch data processing lambda function",
    "LambdaRole": {
        "Type": "AWS::IAM::Role",
        "Properties": {
            "AssumeRolePolicyDocument": {
                "Version": "2012-10-17",
                "Statement": [
                        "Effect": "Allow",
                        "Principal": {
                            "Service": "lambda.amazonaws.com"
                        "Action": "sts:AssumeRole"
            "Path": "/",
            "Policies": [
                    "PolicyName": "firehoseCloudWatchDataProcessing",
                    "PolicyDocument": {
                        "Version": "2012-10-17",
                        "Statement": [
                                "Effect": "Allow",
                                "Action": [
                                "Resource": "arn:aws:logs:*:*:*"
    "FirehoseDataProcessingFunction": {
        "Type": "AWS::Lambda::Function",
        "Properties": {
            "Handler": "index.handler",
            "Role": {"Fn::GetAtt": ["LambdaRole","Arn"]},
            "Description": "Firehose cloudwatch data processing",
            "Code": {
                "ZipFile" : { "Fn::Join" : ["\n", [
                  "'use strict';",
                  "const zlib = require('zlib');",
                  "function transformLogEvent(logEvent) {",
                  "       return Promise.resolve(`${logEvent.message}\n`);",
                  "exports.handler = (event, context, callback) => {",
                  "    Promise.all(event.records.map(r => {",
                  "        const buffer = new Buffer(r.data, 'base64');",
                  "        const decompressed = zlib.gunzipSync(buffer);",
                  "        const data = JSON.parse(decompressed);",
                  "        if (data.messageType !== 'DATA_MESSAGE') {",
                  "            return Promise.resolve({",
                  "                recordId: r.recordId,",
                  "                result: 'ProcessingFailed',",
                  "            });",
                  "         } else {",
                  "            const promises = data.logEvents.map(transformLogEvent);",
                  "            return Promise.all(promises).then(transformed => {",
                  "                const payload = transformed.reduce((a, v) => a + v, '');",
                  "                const encoded = new Buffer(payload).toString('base64');",
                  "                console.log('---------------payloadv2:'+JSON.stringify(payload, null, 2));",
                  "                return {",
                  "                    recordId: r.recordId,",
                  "                    result: 'Ok',",
                  "                    data: encoded,",
                  "                };",
                  "           });",
                  "        }",
                  "    })).then(recs => callback(null, { records: recs }));",

            "Runtime": "nodejs6.10",
            "Timeout": "60"

   "Function" : {
      "Description": "Function ARN",
      "Value": {"Fn::GetAtt": ["FirehoseDataProcessingFunction","Arn"]},
      "Export" : { "Name" : {"Fn::Sub": "${AWS::StackName}-Function" }}

To create the function follow the steps below:

  1. Save the template file as “central-logging-lambda.json”
  2. Login to logging account and, from the CloudFormation console, select “create new stack”
  3. Import the file “central-logging-lambda.json” and click next
  4. Follow the steps to create the stack and verify successful creation
  5. Take a note of Lambda function arn from the output section

Step 3: Create log destination in logging account

Log destination is used as the target of a subscription from application accounts, log destination can be shared between multiple subscriptions however according to the architecture suggested in this solution all logs streamed to the same destination will be stored in the same S3 location, if you would like to store log data in different hierarchy or in a completely different bucket you need to create separate destinations.

As noted previously, your destination and subscription have to be in the same region

Use the template below to create destination stack in logging account.

  "Description": "Create log destination and required resources",

      "Description":"Destination logging bucket"
      "Description":"S3 location for the logs streamed to this destination; example marketing/prod/999999999999/flow-logs/"
      "Description":"CloudWatch logs data processing function"
      "Description":"Source application account number"
    "MyStream": {
      "Type": "AWS::Kinesis::Stream",
      "Properties": {
        "Name": {"Fn::Join" : [ "", [{ "Ref" : "AWS::StackName" },"-Stream"] ]},
        "RetentionPeriodHours" : 48,
        "ShardCount": 1,
        "Tags": [
            "Key": "Solution",
            "Value": "CentralLogging"
    "LogRole" : {
      "Type"  : "AWS::IAM::Role",
      "Properties" : {
          "AssumeRolePolicyDocument" : {
              "Statement" : [ {
                  "Effect" : "Allow",
                  "Principal" : {
                      "Service" : [ {"Fn::Join": [ "", [ "logs.", { "Ref": "AWS::Region" }, ".amazonaws.com" ] ]} ]
                  "Action" : [ "sts:AssumeRole" ]
              } ]
          "Path" : "/service-role/"
    "LogRolePolicy" : {
        "Type" : "AWS::IAM::Policy",
        "Properties" : {
            "PolicyName" : {"Fn::Join" : [ "", [{ "Ref" : "AWS::StackName" },"-LogPolicy"] ]},
            "PolicyDocument" : {
              "Version": "2012-10-17",
              "Statement": [
                  "Effect": "Allow",
                  "Action": ["kinesis:PutRecord"],
                  "Resource": [{ "Fn::GetAtt" : ["MyStream", "Arn"] }]
                  "Effect": "Allow",
                  "Action": ["iam:PassRole"],
                  "Resource": [{ "Fn::GetAtt" : ["LogRole", "Arn"] }]
            "Roles" : [ { "Ref" : "LogRole" } ]
    "LogDestination" : {
      "Type" : "AWS::Logs::Destination",
      "DependsOn" : ["MyStream","LogRole","LogRolePolicy"],
      "Properties" : {
        "DestinationName": {"Fn::Join" : [ "", [{ "Ref" : "AWS::StackName" },"-Destination"] ]},
        "RoleArn": { "Fn::GetAtt" : ["LogRole", "Arn"] },
        "TargetArn": { "Fn::GetAtt" : ["MyStream", "Arn"] },
        "DestinationPolicy": { "Fn::Join" : ["",[
				"{\"Version\" : \"2012-10-17\",\"Statement\" : [{\"Effect\" : \"Allow\",",
                " \"Principal\" : {\"AWS\" : \"", {"Ref":"SourceAccount"} ,"\"},",
                "\"Action\" : \"logs:PutSubscriptionFilter\",",
                " \"Resource\" : \"", 
                {"Fn::Join": [ "", [ "arn:aws:logs:", { "Ref": "AWS::Region" }, ":" ,{ "Ref": "AWS::AccountId" }, ":destination:",{ "Ref" : "AWS::StackName" },"-Destination" ] ]}  ,"\"}]}"

    "S3deliveryStream": {
      "DependsOn": ["S3deliveryRole", "S3deliveryPolicy"],
      "Type": "AWS::KinesisFirehose::DeliveryStream",
      "Properties": {
        "DeliveryStreamName": {"Fn::Join" : [ "", [{ "Ref" : "AWS::StackName" },"-DeliveryStream"] ]},
        "DeliveryStreamType": "KinesisStreamAsSource",
        "KinesisStreamSourceConfiguration": {
            "KinesisStreamARN": { "Fn::GetAtt" : ["MyStream", "Arn"] },
            "RoleARN": {"Fn::GetAtt" : ["S3deliveryRole", "Arn"] }
        "ExtendedS3DestinationConfiguration": {
          "BucketARN": {"Fn::Join" : [ "", ["arn:aws:s3:::",{"Ref":"LogBucketName"}] ]},
          "BufferingHints": {
            "IntervalInSeconds": "60",
            "SizeInMBs": "50"
          "CompressionFormat": "UNCOMPRESSED",
          "Prefix": {"Ref": "LogS3Location"},
          "RoleARN": {"Fn::GetAtt" : ["S3deliveryRole", "Arn"] },
          "ProcessingConfiguration" : {
              "Enabled": "true",
              "Processors": [
                "Parameters": [ 
                    "ParameterName": "LambdaArn",
                    "ParameterValue": {"Ref":"ProcessingLambdaARN"}
                "Type": "Lambda"

    "S3deliveryRole": {
      "Type": "AWS::IAM::Role",
      "Properties": {
        "AssumeRolePolicyDocument": {
          "Version": "2012-10-17",
          "Statement": [
              "Sid": "",
              "Effect": "Allow",
              "Principal": {
                "Service": "firehose.amazonaws.com"
              "Action": "sts:AssumeRole",
              "Condition": {
                "StringEquals": {
                  "sts:ExternalId": {"Ref":"AWS::AccountId"}
    "S3deliveryPolicy": {
      "Type": "AWS::IAM::Policy",
      "Properties": {
        "PolicyName": {"Fn::Join" : [ "", [{ "Ref" : "AWS::StackName" },"-FirehosePolicy"] ]},
        "PolicyDocument": {
          "Version": "2012-10-17",
          "Statement": [
              "Effect": "Allow",
              "Action": [
              "Resource": [
                {"Fn::Join": ["", [ {"Fn::Join" : [ "", ["arn:aws:s3:::",{"Ref":"LogBucketName"}] ]}]]},
                {"Fn::Join": ["", [ {"Fn::Join" : [ "", ["arn:aws:s3:::",{"Ref":"LogBucketName"}] ]}, "*"]]}
              "Effect": "Allow",
              "Action": [
              "Resource": "*"
        "Roles": [{"Ref": "S3deliveryRole"}]

   "Destination" : {
      "Description": "Destination",
      "Value": {"Fn::Join": [ "", [ "arn:aws:logs:", { "Ref": "AWS::Region" }, ":" ,{ "Ref": "AWS::AccountId" }, ":destination:",{ "Ref" : "AWS::StackName" },"-Destination" ] ]},
      "Export" : { "Name" : {"Fn::Sub": "${AWS::StackName}-Destination" }}


To create log your destination and all required resources, follow these steps:

  1. Save your template as “central-logging-destination.json”
  2. Login to your logging account and, from the CloudFormation console, select “create new stack”
  3. Import the file “central-logging-destination.json” and click next
  4. Fill in the parameters to configure the log destination and click Next
  5. Follow the default steps to create the stack and verify successful creation
    1. Bucket name is the same as in the “create central logging bucket” step
    2. LogS3Location is the directory hierarchy for saving log data that will be delivered to this destination
    3. ProcessingLambdaARN is as created in “create data processing Lambda function” step
    4. SourceAccount is the application account number where the subscription will be created
  6. Take a note of destination ARN as it appears in outputs section as you did above.

Step 4: Create the log subscription in your application account

In this section, we will create the subscription filter in one of the application accounts to stream logs from the CloudWatch log group to the log destination that was created in your logging account.

Create log subscription filter

The subscription filter is created between the CloudWatch log group and a destination endpoint. Asubscription could be filtered to send part (or all) of the logs in the log group. For example,you can create a subscription filter to stream only flow logs with status REJECT.

Use the CloudFormation template below to create subscription filter. Subscription filter and log destination must be in the same region.

  "Description": "Create log subscription filter for a specific Log Group",

      "Description":"ARN of logs destination"
      "Description":"Name of LogGroup to forward logs from"
      "Description":"Filter pattern to filter events to be sent to log destination; Leave empty to send all logs"
    "SubscriptionFilter" : {
      "Type" : "AWS::Logs::SubscriptionFilter",
      "Properties" : {
        "LogGroupName" : { "Ref" : "LogGroupName" },
        "FilterPattern" : { "Ref" : "FilterPattern" },
        "DestinationArn" : { "Ref" : "DestinationARN" }

To create a subscription filter for one of CloudWatch log groups in your application account, follow the steps below:

  1. Save the template as “central-logging-subscription.json”
  2. Login to your application account and, from the CloudFormation console, select “create new stack”
  3. Select the file “central-logging-subscription.json” and click next
  4. Fill in the parameters as appropriate to your environment as you did above
    a.  DestinationARN is the value of obtained in “create log destination in logging account” step
    b.  FilterPatterns is the filter value for log data to be streamed to your logging account (leave empty to stream all logs in the selected log group)
    c.  LogGroupName is the log group as it appears under CloudWatch Logs
  5. Verify successful creation of the subscription

This completes the deployment process in both the logging- and application-account side. After a few minutes, log data will be streamed to the central-logging destination defined in your logging account.

Step 5: Analyzing log data

Once log data is centralized, it opens the door to run analytics on the consolidated data for business or security reasons. One of the powerful services that AWS offers is Amazon Athena.

Amazon Athena allows you to query data in S3 using standard SQL.

Follow the steps below to create a simple table and run queries on the flow logs data that has been collected from your application accounts

  1. Login to your logging account and from the Amazon Athena console, use the DDL below in your query  editor to create a new table


Version INT,

Account STRING,

InterfaceId STRING,

SourceAddress STRING,

DestinationAddress STRING,

SourcePort INT,

DestinationPort INT,

Protocol INT,

Packets INT,

Bytes INT,

StartTime INT,

EndTime INT,

Action STRING,

LogStatus STRING


ROW FORMAT SERDE ‘org.apache.hadoop.hive.serde2.RegexSerDe’


“input.regex” = “^([^ ]+)\\s+([0-9]+)\\s+([^ ]+)\\s+([^ ]+)\\s+([^ ]+)\\s+([^ ]+)\\s+([^ ]+)\\s+([^ ]+)\\s+([^ ]+)\\s+([^ ]+)\\s+([0-9]+)\\s+([0-9]+)\\s+([^ ]+)\\s+([^ ]+)$”)

LOCATION ‘s3://central-logging-company-do-not-delete/’;

2. Click ”run query” and verify a successful run/ This creates the table “prod_vpc_flow_logs”

3. You can then run queries against the table data as below:


By following the steps I’ve outlined, you will build a central logging solution to stream CloudWatch logs from one application account to a central logging account. This solution is repeatable and could be deployed multiple times for multiple accounts and logging requirements.


About the Author

Mahmoud Matouk is a Senior Cloud Infrastructure Architect. He works with our customers to help accelerate migration and cloud adoption at the enterprise level.


Switzerland Hopes New Law Will Keep it Off U.S. ‘Pirate Watchlist’

Post Syndicated from Ernesto original https://torrentfreak.com/switzerland-hopes-new-law-will-keep-it-off-us-pirate-watchlist-180228/

In a few weeks, the Office of the United States Trade Representative (USTR) will publish its yearly Special 301 Report, highlighting countries that fail to live up to U.S copyright protection standards.

In recent years Switzerland was among countries that were placed on the ‘Watch List.’ In 2017, the US reported that the Swiss had made some progress, but not enough. Its policies towards online piracy were not up to par, according to U.S. standards.

“Switzerland remains on the Watch List this year due to U.S. concerns regarding specific difficulties in Switzerland’s system of online copyright protection and enforcement,” USTR wrote in its Special 301 Report.

One of the key issues the United States identified is the lack of enforcement against hosting companies that do business with pirate sites. Branding these as a “safe haven” for pirates, the US called for suitable countermeasures.

A second problem that was highlighted is the so-called ‘Logistep Decision.‘ In 2010 the Swiss Federal Supreme Court barred anti-piracy outfit Logistep from harvesting the IP addresses of file-sharers. The Court ruled that IP addresses amount to private data, and outlawed the tracking of file-sharers in Switzerland.

According to the USTR, this ruling prevents copyright holders from enforcing their rights, and they called on the Swiss Government to address this concern as well.

Today nearly a year has passed and it looks like the recommendations were not ignored. In a letter to the USTR, the Swiss Government writes that the two main complaints are dealt with in their new copyright law, which was introduced late last year.

“The draft bill, adopted by the Federal Council at its meeting on November 22, 2017, addresses both of those concerns. It aims at further modernizing Swiss copyright law for the purposes of the digital environment and steps up the fight against Internet piracy,” the Swiss write.

The new copyright law addresses the hosting problem by introducing a “take-down-and-stay-down” policy. Internet services will be required to remove infringing content from their platforms and prevent that same content from reappearing. Failure to comply will result in prosecution.

“The ‘stay down’ will prevent rogue websites from being hosted in Switzerland and will make the fight against Internet piracy more effective and sustainable. That should put an end to criticism directed against Switzerland as a host country for infringing sites,” Switzerland informs the U.S.

Similarly, the Logistep ruling will no longer be an issue either if the country’s new copyright law is implemented.

“[T]he draft bill clarifies that the processing of data for the purposes of prosecuting copyright infringement is permissible. With that, it puts an end to the debate that followed the Logistep decision about the extent to which the recording of IP addresses for prosecution purposes is admissible.”

Many copyright holder groups have also asked for ISP blocking of pirate sites, but Switzerland notes that this idea is off the table for now. There is not enough support in Parliament for an Internet blocking provision which may jeopardize acceptance of the entire draft bill, their letter explains.

While not mentioned in the letter, downloading and streaming copyright infringing content for personal use also remains unpunished, video games and software excepted. Uploading and other types of distribution of infringing content are not permitted, however.

Still, the Swiss hope that the newly proposed changes to its copyright law will be enough to have it removed from the Special 301 Watch List.

“Switzerland is confident that the revision of the Swiss Copyright Act will more effectively address the challenges posed by the Internet,” the Swiss Government writes, adding that it “looks forward to continuing to work with the U.S. to further clarify any issue relating to online piracy.”

Switzerland’s letter to the United States Trade Representative is available here (pdf).

Source: TF, for the latest info on copyright, file-sharing, torrent sites and more. We also have VPN discounts, offers and coupons

Now Available – AWS Serverless Application Repository

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/now-available-aws-serverless-application-repository/

Last year I suggested that you Get Ready for the AWS Serverless Application Repository and gave you a sneak peek. The Repository is designed to make it as easy as possible for you to discover, configure, and deploy serverless applications and components on AWS. It is also an ideal venue for AWS partners, enterprise customers, and independent developers to share their serverless creations.

Now Available
After a well-received public preview, the AWS Serverless Application Repository is now generally available and you can start using it today!

As a consumer, you will be able to tap in to a thriving ecosystem of serverless applications and components that will be a perfect complement to your machine learning, image processing, IoT, and general-purpose work. You can configure and consume them as-is, or you can take them apart, add features, and submit pull requests to the author.

As a publisher, you can publish your contribution in the Serverless Application Repository with ease. You simply enter a name and a description, choose some labels to increase discoverability, select an appropriate open source license from a menu, and supply a README to help users get started. Then you enter a link to your existing source code repo, choose a SAM template, and designate a semantic version.

Let’s take a look at both operations…

Consuming a Serverless Application
The Serverless Application Repository is accessible from the Lambda Console. I can page through the existing applications or I can initiate a search:

A search for “todo” returns some interesting results:

I simply click on an application to learn more:

I can configure the application and deploy it right away if I am already familiar with the application:

I can expand each of the sections to learn more. The Permissions section tells me which IAM policies will be used:

And the Template section displays the SAM template that will be used to deploy the application:

I can inspect the template to learn more about the AWS resources that will be created when the template is deployed. I can also use the templates as a learning resource in preparation for creating and publishing my own application.

The License section displays the application’s license:

To deploy todo, I name the application and click Deploy:

Deployment starts immediately and is done within a minute (application deployment time will vary, depending on the number and type of resources to be created):

I can see all of my deployed applications in the Lambda Console:

There’s currently no way for a SAM template to indicate that an API Gateway function returns binary media types, so I set this up by hand and then re-deploy the API:

Following the directions in the Readme, I open the API Gateway Console and find the URL for the app in the API Gateway Dashboard:

I visit the URL and enter some items into my list:

Publishing a Serverless Application
Publishing applications is a breeze! I visit the Serverless App Repository page and click on Publish application to get started:

Then I assign a name to my application, enter my own name, and so forth:

I can choose from a long list of open-source friendly SPDX licenses:

I can create an initial version of my application at this point, or I can do it later. Either way, I simply provide a version number, a URL to a public repository containing my code, and a SAM template:

Available Now
The AWS Serverless Application Repository is available now and you can start using it today, paying only for the AWS resources consumed by the serverless applications that you deploy.

You can deploy applications in the US East (Ohio), US East (N. Virginia), US West (N. California), US West (Oregon), Asia Pacific (Tokyo), Asia Pacific (Seoul), Asia Pacific (Mumbai), Asia Pacific (Singapore), Asia Pacific (Sydney), Canada (Central), EU (Frankfurt), EU (Ireland), EU (London), and South America (São Paulo) Regions. You can publish from the US East (N. Virginia) or US East (Ohio) Regions for global availability.



How to Patch Linux Workloads on AWS

Post Syndicated from Koen van Blijderveen original https://aws.amazon.com/blogs/security/how-to-patch-linux-workloads-on-aws/

Most malware tries to compromise your systems by using a known vulnerability that the operating system maker has already patched. As best practices to help prevent malware from affecting your systems, you should apply all operating system patches and actively monitor your systems for missing patches.

In this blog post, I show you how to patch Linux workloads using AWS Systems Manager. To accomplish this, I will show you how to use the AWS Command Line Interface (AWS CLI) to:

  1. Launch an Amazon EC2 instance for use with Systems Manager.
  2. Configure Systems Manager to patch your Amazon EC2 Linux instances.

In two previous blog posts (Part 1 and Part 2), I showed how to use the AWS Management Console to perform the necessary steps to patch, inspect, and protect Microsoft Windows workloads. You can implement those same processes for your Linux instances running in AWS by changing the instance tags and types shown in the previous blog posts.

Because most Linux system administrators are more familiar with using a command line, I show how to patch Linux workloads by using the AWS CLI in this blog post. The steps to use the Amazon EBS Snapshot Scheduler and Amazon Inspector are identical for both Microsoft Windows and Linux.

What you should know first

To follow along with the solution in this post, you need one or more Amazon EC2 instances. You may use existing instances or create new instances. For this post, I assume this is an Amazon EC2 for Amazon Linux instance installed from Amazon Machine Images (AMIs).

Systems Manager is a collection of capabilities that helps you automate management tasks for AWS-hosted instances on Amazon EC2 and your on-premises servers. In this post, I use Systems Manager for two purposes: to run remote commands and apply operating system patches. To learn about the full capabilities of Systems Manager, see What Is AWS Systems Manager?

As of Amazon Linux 2017.09, the AMI comes preinstalled with the Systems Manager agent. Systems Manager Patch Manager also supports Red Hat and Ubuntu. To install the agent on these Linux distributions or an older version of Amazon Linux, see Installing and Configuring SSM Agent on Linux Instances.

If you are not familiar with how to launch an Amazon EC2 instance, see Launching an Instance. I also assume you launched or will launch your instance in a private subnet. You must make sure that the Amazon EC2 instance can connect to the internet using a network address translation (NAT) instance or NAT gateway to communicate with Systems Manager. The following diagram shows how you should structure your VPC.

Diagram showing how to structure your VPC

Later in this post, you will assign tasks to a maintenance window to patch your instances with Systems Manager. To do this, the IAM user you are using for this post must have the iam:PassRole permission. This permission allows the IAM user assigning tasks to pass his own IAM permissions to the AWS service. In this example, when you assign a task to a maintenance window, IAM passes your credentials to Systems Manager. You also should authorize your IAM user to use Amazon EC2 and Systems Manager. As mentioned before, you will be using the AWS CLI for most of the steps in this blog post. Our documentation shows you how to get started with the AWS CLI. Make sure you have the AWS CLI installed and configured with an AWS access key and secret access key that belong to an IAM user that have the following AWS managed policies attached to the IAM user you are using for this example: AmazonEC2FullAccess and AmazonSSMFullAccess.

Step 1: Launch an Amazon EC2 Linux instance

In this section, I show you how to launch an Amazon EC2 instance so that you can use Systems Manager with the instance. This step requires you to do three things:

  1. Create an IAM role for Systems Manager before launching your Amazon EC2 instance.
  2. Launch your Amazon EC2 instance with Amazon EBS and the IAM role for Systems Manager.
  3. Add tags to the instances so that you can add your instances to a Systems Manager maintenance window based on tags.

A. Create an IAM role for Systems Manager

Before launching an Amazon EC2 instance, I recommend that you first create an IAM role for Systems Manager, which you will use to update the Amazon EC2 instance. AWS already provides a preconfigured policy that you can use for the new role and it is called AmazonEC2RoleforSSM.

  1. Create a JSON file named trustpolicy-ec2ssm.json that contains the following trust policy. This policy describes which principal (an entity that can take action on an AWS resource) is allowed to assume the role we are going to create. In this example, the principal is the Amazon EC2 service.
      "Version": "2012-10-17",
      "Statement": {
        "Effect": "Allow",
        "Principal": {"Service": "ec2.amazonaws.com"},
        "Action": "sts:AssumeRole"

  1. Use the following command to create a role named EC2SSM that has the AWS managed policy AmazonEC2RoleforSSM attached to it. This generates JSON-based output that describes the role and its parameters, if the command is successful.
    $ aws iam create-role --role-name EC2SSM --assume-role-policy-document file://trustpolicy-ec2ssm.json

  1. Use the following command to attach the AWS managed IAM policy (AmazonEC2RoleforSSM) to your newly created role.
    $ aws iam attach-role-policy --role-name EC2SSM --policy-arn arn:aws:iam::aws:policy/service-role/AmazonEC2RoleforSSM

  1. Use the following commands to create the IAM instance profile and add the role to the instance profile. The instance profile is needed to attach the role we created earlier to your Amazon EC2 instance.
    $ aws iam create-instance-profile --instance-profile-name EC2SSM-IP
    $ aws iam add-role-to-instance-profile --instance-profile-name EC2SSM-IP --role-name EC2SSM

B. Launch your Amazon EC2 instance

To follow along, you need an Amazon EC2 instance that is running Amazon Linux. You can use any existing instance you may have or create a new instance.

When launching a new Amazon EC2 instance, be sure that:

  1. Use the following command to launch a new Amazon EC2 instance using an Amazon Linux AMI available in the US East (N. Virginia) Region (also known as us-east-1). Replace YourKeyPair and YourSubnetId with your information. For more information about creating a key pair, see the create-key-pair documentation. Write down the InstanceId that is in the output because you will need it later in this post.
    $ aws ec2 run-instances --image-id ami-cb9ec1b1 --instance-type t2.micro --key-name YourKeyPair --subnet-id YourSubnetId --iam-instance-profile Name=EC2SSM-IP

  1. If you are using an existing Amazon EC2 instance, you can use the following command to attach the instance profile you created earlier to your instance.
    $ aws ec2 associate-iam-instance-profile --instance-id YourInstanceId --iam-instance-profile Name=EC2SSM-IP

C. Add tags

The final step of configuring your Amazon EC2 instances is to add tags. You will use these tags to configure Systems Manager in Step 2 of this post. For this example, I add a tag named Patch Group and set the value to Linux Servers. I could have other groups of Amazon EC2 instances that I treat differently by having the same tag name but a different tag value. For example, I might have a collection of other servers with the tag name Patch Group with a value of Web Servers.

  • Use the following command to add the Patch Group tag to your Amazon EC2 instance.
    $ aws ec2 create-tags --resources YourInstanceId --tags --tags Key="Patch Group",Value="Linux Servers"

Note: You must wait a few minutes until the Amazon EC2 instance is available before you can proceed to the next section. To make sure your Amazon EC2 instance is online and ready, you can use the following AWS CLI command:

$ aws ec2 describe-instance-status --instance-ids YourInstanceId

At this point, you now have at least one Amazon EC2 instance you can use to configure Systems Manager.

Step 2: Configure Systems Manager

In this section, I show you how to configure and use Systems Manager to apply operating system patches to your Amazon EC2 instances, and how to manage patch compliance.

To start, I provide some background information about Systems Manager. Then, I cover how to:

  1. Create the Systems Manager IAM role so that Systems Manager is able to perform patch operations.
  2. Create a Systems Manager patch baseline and associate it with your instance to define which patches Systems Manager should apply.
  3. Define a maintenance window to make sure Systems Manager patches your instance when you tell it to.
  4. Monitor patch compliance to verify the patch state of your instances.

You must meet two prerequisites to use Systems Manager to apply operating system patches. First, you must attach the IAM role you created in the previous section, EC2SSM, to your Amazon EC2 instance. Second, you must install the Systems Manager agent on your Amazon EC2 instance. If you have used a recent Amazon Linux AMI, Amazon has already installed the Systems Manager agent on your Amazon EC2 instance. You can confirm this by logging in to an Amazon EC2 instance and checking the Systems Manager agent log files that are located at /var/log/amazon/ssm/.

To install the Systems Manager agent on an instance that does not have the agent preinstalled or if you want to use the Systems Manager agent on your on-premises servers, see Installing and Configuring the Systems Manager Agent on Linux Instances. If you forgot to attach the newly created role when launching your Amazon EC2 instance or if you want to attach the role to already running Amazon EC2 instances, see Attach an AWS IAM Role to an Existing Amazon EC2 Instance by Using the AWS CLI or use the AWS Management Console.

A. Create the Systems Manager IAM role

For a maintenance window to be able to run any tasks, you must create a new role for Systems Manager. This role is a different kind of role than the one you created earlier: this role will be used by Systems Manager instead of Amazon EC2. Earlier, you created the role, EC2SSM, with the policy, AmazonEC2RoleforSSM, which allowed the Systems Manager agent on your instance to communicate with Systems Manager. In this section, you need a new role with the policy, AmazonSSMMaintenanceWindowRole, so that the Systems Manager service can execute commands on your instance.

To create the new IAM role for Systems Manager:

  1. Create a JSON file named trustpolicy-maintenancewindowrole.json that contains the following trust policy. This policy describes which principal is allowed to assume the role you are going to create. This trust policy allows not only Amazon EC2 to assume this role, but also Systems Manager.

  1. Use the following command to create a role named MaintenanceWindowRole that has the AWS managed policy, AmazonSSMMaintenanceWindowRole, attached to it. This command generates JSON-based output that describes the role and its parameters, if the command is successful.
    $ aws iam create-role --role-name MaintenanceWindowRole --assume-role-policy-document file://trustpolicy-maintenancewindowrole.json

  1. Use the following command to attach the AWS managed IAM policy (AmazonEC2RoleforSSM) to your newly created role.
    $ aws iam attach-role-policy --role-name MaintenanceWindowRole --policy-arn arn:aws:iam::aws:policy/service-role/AmazonSSMMaintenanceWindowRole

B. Create a Systems Manager patch baseline and associate it with your instance

Next, you will create a Systems Manager patch baseline and associate it with your Amazon EC2 instance. A patch baseline defines which patches Systems Manager should apply to your instance. Before you can associate the patch baseline with your instance, though, you must determine if Systems Manager recognizes your Amazon EC2 instance. Use the following command to list all instances managed by Systems Manager. The --filters option ensures you look only for your newly created Amazon EC2 instance.

$ aws ssm describe-instance-information --filters Key=InstanceIds,Values= YourInstanceId

    "InstanceInformationList": [
            "IsLatestVersion": true,
            "ComputerName": "ip-10-50-2-245",
            "PingStatus": "Online",
            "InstanceId": "YourInstanceId",
            "IPAddress": "",
            "ResourceType": "EC2Instance",
            "AgentVersion": "",
            "PlatformVersion": "2017.09",
            "PlatformName": "Amazon Linux AMI",
            "PlatformType": "Linux",
            "LastPingDateTime": 1515759143.826

If your instance is missing from the list, verify that:

  1. Your instance is running.
  2. You attached the Systems Manager IAM role, EC2SSM.
  3. You deployed a NAT gateway in your public subnet to ensure your VPC reflects the diagram shown earlier in this post so that the Systems Manager agent can connect to the Systems Manager internet endpoint.
  4. The Systems Manager agent logs don’t include any unaddressed errors.

Now that you have checked that Systems Manager can manage your Amazon EC2 instance, it is time to create a patch baseline. With a patch baseline, you define which patches are approved to be installed on all Amazon EC2 instances associated with the patch baseline. The Patch Group resource tag you defined earlier will determine to which patch group an instance belongs. If you do not specifically define a patch baseline, the default AWS-managed patch baseline is used.

To create a patch baseline:

  1. Use the following command to create a patch baseline named AmazonLinuxServers. With approval rules, you can determine the approved patches that will be included in your patch baseline. In this example, you add all Critical severity patches to the patch baseline as soon as they are released, by setting the Auto approval delay to 0 days. By setting the Auto approval delay to 2 days, you add to this patch baseline the Important, Medium, and Low severity patches two days after they are released.
    $ aws ssm create-patch-baseline --name "AmazonLinuxServers" --description "Baseline containing all updates for Amazon Linux" --operating-system AMAZON_LINUX --approval-rules "PatchRules=[{PatchFilterGroup={PatchFilters=[{Values=[Critical],Key=SEVERITY}]},ApproveAfterDays=0,ComplianceLevel=CRITICAL},{PatchFilterGroup={PatchFilters=[{Values=[Important,Medium,Low],Key=SEVERITY}]},ApproveAfterDays=2,ComplianceLevel=HIGH}]"
        "BaselineId": "YourBaselineId"

  1. Use the following command to register the patch baseline you created with your instance. To do so, you use the Patch Group tag that you added to your Amazon EC2 instance.
    $ aws ssm register-patch-baseline-for-patch-group --baseline-id YourPatchBaselineId --patch-group "Linux Servers"
        "PatchGroup": "Linux Servers",
        "BaselineId": "YourBaselineId"

C.  Define a maintenance window

Now that you have successfully set up a role, created a patch baseline, and registered your Amazon EC2 instance with your patch baseline, you will define a maintenance window so that you can control when your Amazon EC2 instances will receive patches. By creating multiple maintenance windows and assigning them to different patch groups, you can make sure your Amazon EC2 instances do not all reboot at the same time.

To define a maintenance window:

  1. Use the following command to define a maintenance window. In this example command, the maintenance window will start every Saturday at 10:00 P.M. UTC. It will have a duration of 4 hours and will not start any new tasks 1 hour before the end of the maintenance window.
    $ aws ssm create-maintenance-window --name SaturdayNight --schedule "cron(0 0 22 ? * SAT *)" --duration 4 --cutoff 1 --allow-unassociated-targets
        "WindowId": "YourMaintenanceWindowId"

For more information about defining a cron-based schedule for maintenance windows, see Cron and Rate Expressions for Maintenance Windows.

  1. After defining the maintenance window, you must register the Amazon EC2 instance with the maintenance window so that Systems Manager knows which Amazon EC2 instance it should patch in this maintenance window. You can register the instance by using the same Patch Group tag you used to associate the Amazon EC2 instance with the AWS-provided patch baseline, as shown in the following command.
    $ aws ssm register-target-with-maintenance-window --window-id YourMaintenanceWindowId --resource-type INSTANCE --targets "Key=tag:Patch Group,Values=Linux Servers"
        "WindowTargetId": "YourWindowTargetId"

  1. Assign a task to the maintenance window that will install the operating system patches on your Amazon EC2 instance. The following command includes the following options.
    1. name is the name of your task and is optional. I named mine Patching.
    2. task-arn is the name of the task document you want to run.
    3. max-concurrency allows you to specify how many of your Amazon EC2 instances Systems Manager should patch at the same time. max-errors determines when Systems Manager should abort the task. For patching, this number should not be too low, because you do not want your entire patch task to stop on all instances if one instance fails. You can set this, for example, to 20%.
    4. service-role-arn is the Amazon Resource Name (ARN) of the AmazonSSMMaintenanceWindowRole role you created earlier in this blog post.
    5. task-invocation-parameters defines the parameters that are specific to the AWS-RunPatchBaseline task document and tells Systems Manager that you want to install patches with a timeout of 600 seconds (10 minutes).
      $ aws ssm register-task-with-maintenance-window --name "Patching" --window-id "YourMaintenanceWindowId" --targets "Key=WindowTargetIds,Values=YourWindowTargetId" --task-arn AWS-RunPatchBaseline --service-role-arn "arn:aws:iam::123456789012:role/MaintenanceWindowRole" --task-type "RUN_COMMAND" --task-invocation-parameters "RunCommand={Comment=,TimeoutSeconds=600,Parameters={SnapshotId=[''],Operation=[Install]}}" --max-concurrency "500" --max-errors "20%"
          "WindowTaskId": "YourWindowTaskId"

Now, you must wait for the maintenance window to run at least once according to the schedule you defined earlier. If your maintenance window has expired, you can check the status of any maintenance tasks Systems Manager has performed by using the following command.

$ aws ssm describe-maintenance-window-executions --window-id "YourMaintenanceWindowId"

    "WindowExecutions": [
            "Status": "SUCCESS",
            "WindowId": "YourMaintenanceWindowId",
            "WindowExecutionId": "b594984b-430e-4ffa-a44c-a2e171de9dd3",
            "EndTime": 1515766467.487,
            "StartTime": 1515766457.691

D.  Monitor patch compliance

You also can see the overall patch compliance of all Amazon EC2 instances using the following command in the AWS CLI.

$ aws ssm list-compliance-summaries

This command shows you the number of instances that are compliant with each category and the number of instances that are not in JSON format.

You also can see overall patch compliance by choosing Compliance under Insights in the navigation pane of the Systems Manager console. You will see a visual representation of how many Amazon EC2 instances are up to date, how many Amazon EC2 instances are noncompliant, and how many Amazon EC2 instances are compliant in relation to the earlier defined patch baseline.

Screenshot of the Compliance page of the Systems Manager console

In this section, you have set everything up for patch management on your instance. Now you know how to patch your Amazon EC2 instance in a controlled manner and how to check if your Amazon EC2 instance is compliant with the patch baseline you have defined. Of course, I recommend that you apply these steps to all Amazon EC2 instances you manage.


In this blog post, I showed how to use Systems Manager to create a patch baseline and maintenance window to keep your Amazon EC2 Linux instances up to date with the latest security patches. Remember that by creating multiple maintenance windows and assigning them to different patch groups, you can make sure your Amazon EC2 instances do not all reboot at the same time.

If you have comments about this post, submit them in the “Comments” section below. If you have questions about or issues implementing any part of this solution, start a new thread on the Amazon EC2 forum or contact AWS Support.

– Koen

Sharing Secrets with AWS Lambda Using AWS Systems Manager Parameter Store

Post Syndicated from Chris Munns original https://aws.amazon.com/blogs/compute/sharing-secrets-with-aws-lambda-using-aws-systems-manager-parameter-store/

This post courtesy of Roberto Iturralde, Sr. Application Developer- AWS Professional Services

Application architects are faced with key decisions throughout the process of designing and implementing their systems. One decision common to nearly all solutions is how to manage the storage and access rights of application configuration. Shared configuration should be stored centrally and securely with each system component having access only to the properties that it needs for functioning.

With AWS Systems Manager Parameter Store, developers have access to central, secure, durable, and highly available storage for application configuration and secrets. Parameter Store also integrates with AWS Identity and Access Management (IAM), allowing fine-grained access control to individual parameters or branches of a hierarchical tree.

This post demonstrates how to create and access shared configurations in Parameter Store from AWS Lambda. Both encrypted and plaintext parameter values are stored with only the Lambda function having permissions to decrypt the secrets. You also use AWS X-Ray to profile the function.

Solution overview

This example is made up of the following components:

  • An AWS SAM template that defines:
    • A Lambda function and its permissions
    • An unencrypted Parameter Store parameter that the Lambda function loads
    • A KMS key that only the Lambda function can access. You use this key to create an encrypted parameter later.
  • Lambda function code in Python 3.6 that demonstrates how to load values from Parameter Store at function initialization for reuse across invocations.

Launch the AWS SAM template

To create the resources shown in this post, you can download the SAM template or choose the button to launch the stack. The template requires one parameter, an IAM user name, which is the name of the IAM user to be the admin of the KMS key that you create. In order to perform the steps listed in this post, this IAM user will need permissions to execute Lambda functions, create Parameter Store parameters, administer keys in KMS, and view the X-Ray console. If you have these privileges in your IAM user account you can use your own account to complete the walkthrough. You can not use the root user to administer the KMS keys.

SAM template resources

The following sections show the code for the resources defined in the template.
Lambda function

    Type: 'AWS::Serverless::Function'
      FunctionName: 'ParameterStoreBlogFunctionDev'
      Description: 'Integrating lambda with Parameter Store'
      Handler: 'lambda_function.lambda_handler'
      Role: !GetAtt ParameterStoreBlogFunctionRoleDev.Arn
      CodeUri: './code'
          ENV: 'dev'
          APP_CONFIG_PATH: 'parameterStoreBlog'
          AWS_XRAY_TRACING_NAME: 'ParameterStoreBlogFunctionDev'
      Runtime: 'python3.6'
      Timeout: 5
      Tracing: 'Active'

    Type: AWS::IAM::Role
        Version: '2012-10-17'
            Effect: Allow
                - 'lambda.amazonaws.com'
              - 'sts:AssumeRole'
        - 'arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole'
          PolicyName: 'ParameterStoreBlogDevParameterAccess'
            Version: '2012-10-17'
                Effect: Allow
                  - 'ssm:GetParameter*'
                Resource: !Sub 'arn:aws:ssm:${AWS::Region}:${AWS::AccountId}:parameter/dev/parameterStoreBlog*'
          PolicyName: 'ParameterStoreBlogDevXRayAccess'
            Version: '2012-10-17'
                Effect: Allow
                  - 'xray:PutTraceSegments'
                  - 'xray:PutTelemetryRecords'
                Resource: '*'

In this YAML code, you define a Lambda function named ParameterStoreBlogFunctionDev using the SAM AWS::Serverless::Function type. The environment variables for this function include the ENV (dev) and the APP_CONFIG_PATH where you find the configuration for this app in Parameter Store. X-Ray tracing is also enabled for profiling later.

The IAM role for this function extends the AWSLambdaBasicExecutionRole by adding IAM policies that grant the function permissions to write to X-Ray and get parameters from Parameter Store, limited to paths under /dev/parameterStoreBlog*.
Parameter Store parameter

    Type: AWS::SSM::Parameter
      Name: '/dev/parameterStoreBlog/appConfig'
      Description: 'Sample dev config values for my app'
      Type: String
      Value: '{"key1": "value1","key2": "value2","key3": "value3"}'

This YAML code creates a plaintext string parameter in Parameter Store in a path that your Lambda function can access.
KMS encryption key

    Type: AWS::KMS::Alias
      AliasName: 'alias/ParameterStoreBlogKeyDev'
      TargetKeyId: !Ref ParameterStoreBlogDevEncryptionKey

    Type: AWS::KMS::Key
      Description: 'Encryption key for secret config values for the Parameter Store blog post'
      Enabled: True
      EnableKeyRotation: False
        Version: '2012-10-17'
        Id: 'key-default-1'
            Sid: 'Allow administration of the key & encryption of new values'
            Effect: Allow
                - !Sub 'arn:aws:iam::${AWS::AccountId}:user/${IAMUsername}'
              - 'kms:Create*'
              - 'kms:Encrypt'
              - 'kms:Describe*'
              - 'kms:Enable*'
              - 'kms:List*'
              - 'kms:Put*'
              - 'kms:Update*'
              - 'kms:Revoke*'
              - 'kms:Disable*'
              - 'kms:Get*'
              - 'kms:Delete*'
              - 'kms:ScheduleKeyDeletion'
              - 'kms:CancelKeyDeletion'
            Resource: '*'
            Sid: 'Allow use of the key'
            Effect: Allow
              AWS: !GetAtt ParameterStoreBlogFunctionRoleDev.Arn
              - 'kms:Encrypt'
              - 'kms:Decrypt'
              - 'kms:ReEncrypt*'
              - 'kms:GenerateDataKey*'
              - 'kms:DescribeKey'
            Resource: '*'

This YAML code creates an encryption key with a key policy with two statements.

The first statement allows a given user (${IAMUsername}) to administer the key. Importantly, this includes the ability to encrypt values using this key and disable or delete this key, but does not allow the administrator to decrypt values that were encrypted with this key.

The second statement grants your Lambda function permission to encrypt and decrypt values using this key. The alias for this key in KMS is ParameterStoreBlogKeyDev, which is how you reference it later.

Lambda function

Here I walk you through the Lambda function code.

import os, traceback, json, configparser, boto3
from aws_xray_sdk.core import patch_all

# Initialize boto3 client at global scope for connection reuse
client = boto3.client('ssm')
env = os.environ['ENV']
app_config_path = os.environ['APP_CONFIG_PATH']
full_config_path = '/' + env + '/' + app_config_path
# Initialize app at global scope for reuse across invocations
app = None

class MyApp:
    def __init__(self, config):
        Construct new MyApp with configuration
        :param config: application configuration
        self.config = config

    def get_config(self):
        return self.config

def load_config(ssm_parameter_path):
    Load configparser from config stored in SSM Parameter Store
    :param ssm_parameter_path: Path to app config in SSM Parameter Store
    :return: ConfigParser holding loaded config
    configuration = configparser.ConfigParser()
        # Get all parameters for this app
        param_details = client.get_parameters_by_path(

        # Loop through the returned parameters and populate the ConfigParser
        if 'Parameters' in param_details and len(param_details.get('Parameters')) > 0:
            for param in param_details.get('Parameters'):
                param_path_array = param.get('Name').split("/")
                section_position = len(param_path_array) - 1
                section_name = param_path_array[section_position]
                config_values = json.loads(param.get('Value'))
                config_dict = {section_name: config_values}
                print("Found configuration: " + str(config_dict))

        print("Encountered an error loading config from SSM.")
        return configuration

def lambda_handler(event, context):
    global app
    # Initialize app if it doesn't yet exist
    if app is None:
        print("Loading config and creating new MyApp...")
        config = load_config(full_config_path)
        app = MyApp(config)

    return "MyApp config is " + str(app.get_config()._sections)

Beneath the import statements, you import the patch_all function from the AWS X-Ray library, which you use to patch boto3 to create X-Ray segments for all your boto3 operations.

Next, you create a boto3 SSM client at the global scope for reuse across function invocations, following Lambda best practices. Using the function environment variables, you assemble the path where you expect to find your configuration in Parameter Store. The class MyApp is meant to serve as an example of an application that would need its configuration injected at construction. In this example, you create an instance of ConfigParser, a class in Python’s standard library for handling basic configurations, to give to MyApp.

The load_config function loads the all the parameters from Parameter Store at the level immediately beneath the path provided in the Lambda function environment variables. Each parameter found is put into a new section in ConfigParser. The name of the section is the name of the parameter, less the base path. In this example, the full parameter name is /dev/parameterStoreBlog/appConfig, which is put in a section named appConfig.

Finally, the lambda_handler function initializes an instance of MyApp if it doesn’t already exist, constructing it with the loaded configuration from Parameter Store. Then it simply returns the currently loaded configuration in MyApp. The impact of this design is that the configuration is only loaded from Parameter Store the first time that the Lambda function execution environment is initialized. Subsequent invocations reuse the existing instance of MyApp, resulting in improved performance. You see this in the X-Ray traces later in this post. For more advanced use cases where configuration changes need to be received immediately, you could implement an expiry policy for your configuration entries or push notifications to your function.

To confirm that everything was created successfully, test the function in the Lambda console.

  1. Open the Lambda console.
  2. In the navigation pane, choose Functions.
  3. In the Functions pane, filter to ParameterStoreBlogFunctionDev to find the function created by the SAM template earlier. Open the function name to view its details.
  4. On the top right of the function detail page, choose Test. You may need to create a new test event. The input JSON doesn’t matter as this function ignores the input.

After running the test, you should see output similar to the following. This demonstrates that the function successfully fetched the unencrypted configuration from Parameter Store.

Create an encrypted parameter

You currently have a simple, unencrypted parameter and a Lambda function that can access it.

Next, you create an encrypted parameter that only your Lambda function has permission to use for decryption. This limits read access for this parameter to only this Lambda function.

To follow along with this section, deploy the SAM template for this post in your account and make your IAM user name the KMS key admin mentioned earlier.

  1. In the Systems Manager console, under Shared Resources, choose Parameter Store.
  2. Choose Create Parameter.
    • For Name, enter /dev/parameterStoreBlog/appSecrets.
    • For Type, select Secure String.
    • For KMS Key ID, choose alias/ParameterStoreBlogKeyDev, which is the key that your SAM template created.
    • For Value, enter {"secretKey": "secretValue"}.
    • Choose Create Parameter.
  3. If you now try to view the value of this parameter by choosing the name of the parameter in the parameters list and then choosing Show next to the Value field, you won’t see the value appear. This is because, even though you have permission to encrypt values using this KMS key, you do not have permissions to decrypt values.
  4. In the Lambda console, run another test of your function. You now also see the secret parameter that you created and its decrypted value.

If you do not see the new parameter in the Lambda output, this may be because the Lambda execution environment is still warm from the previous test. Because the parameters are loaded at Lambda startup, you need a fresh execution environment to refresh the values.

Adjust the function timeout to a different value in the Advanced Settings at the bottom of the Lambda Configuration tab. Choose Save and test to trigger the creation of a new Lambda execution environment.

Profiling the impact of querying Parameter Store using AWS X-Ray

By using the AWS X-Ray SDK to patch boto3 in your Lambda function code, each invocation of the function creates traces in X-Ray. In this example, you can use these traces to validate the performance impact of your design decision to only load configuration from Parameter Store on the first invocation of the function in a new execution environment.

From the Lambda function details page where you tested the function earlier, under the function name, choose Monitoring. Choose View traces in X-Ray.

This opens the X-Ray console in a new window filtered to your function. Be aware of the time range field next to the search bar if you don’t see any search results.
In this screenshot, I’ve invoked the Lambda function twice, one time 10.3 minutes ago with a response time of 1.1 seconds and again 9.8 minutes ago with a response time of 8 milliseconds.

Looking at the details of the longer running trace by clicking the trace ID, you can see that the Lambda function spent the first ~350 ms of the full 1.1 sec routing the request through Lambda and creating a new execution environment for this function, as this was the first invocation with this code. This is the portion of time before the initialization subsegment.

Next, it took 725 ms to initialize the function, which includes executing the code at the global scope (including creating the boto3 client). This is also a one-time cost for a fresh execution environment.

Finally, the function executed for 65 ms, of which 63.5 ms was the GetParametersByPath call to Parameter Store.

Looking at the trace for the second, much faster function invocation, you see that the majority of the 8 ms execution time was Lambda routing the request to the function and returning the response. Only 1 ms of the overall execution time was attributed to the execution of the function, which makes sense given that after the first invocation you’re simply returning the config stored in MyApp.

While the Traces screen allows you to view the details of individual traces, the X-Ray Service Map screen allows you to view aggregate performance data for all traced services over a period of time.

In the X-Ray console navigation pane, choose Service map. Selecting a service node shows the metrics for node-specific requests. Selecting an edge between two nodes shows the metrics for requests that traveled that connection. Again, be aware of the time range field next to the search bar if you don’t see any search results.

After invoking your Lambda function several more times by testing it from the Lambda console, you can view some aggregate performance metrics. Look at the following:

  • From the client perspective, requests to the Lambda service for the function are taking an average of 50 ms to respond. The function is generating ~1 trace per minute.
  • The function itself is responding in an average of 3 ms. In the following screenshot, I’ve clicked on this node, which reveals a latency histogram of the traced requests showing that over 95% of requests return in under 5 ms.
  • Parameter Store is responding to requests in an average of 64 ms, but note the much lower trace rate in the node. This is because you only fetch data from Parameter Store on the initialization of the Lambda execution environment.


Deduplication, encryption, and restricted access to shared configuration and secrets is a key component to any mature architecture. Serverless architectures designed using event-driven, on-demand, compute services like Lambda are no different.

In this post, I walked you through a sample application accessing unencrypted and encrypted values in Parameter Store. These values were created in a hierarchy by application environment and component name, with the permissions to decrypt secret values restricted to only the function needing access. The techniques used here can become the foundation of secure, robust configuration management in your enterprise serverless applications.

Server vs Endpoint Backup — Which is Best?

Post Syndicated from Roderick Bauer original https://www.backblaze.com/blog/endpoint-backup-for-distributed-computing/

server and computer backup to the cloud

How common are these statements in your organization?

  • I know I saved that file. The application must have put it somewhere outside of my documents folder.” — Mike in Marketing
  • I was on the road and couldn’t get a reliable VPN connection. I guess that’s why my laptop wasn’t backed up.” — Sally in Sales
  • I try to follow file policies, but I had a deadline this week and didn’t have time to copy my files to the server.” — Felicia in Finance
  • I just did a commit of my code changes and that was when the coffee mug was knocked over onto the laptop.” — Erin in Engineering
  • If you need a file restored from backup, contact the help desk at [email protected] The IT department will get back to you.” — XYZ corporate intranet
  • Why don’t employees save files on the network drive like they’re supposed to?” — Isaac in IT

If these statements are familiar, most likely you rely on file server backups to safeguard your valuable endpoint data.

The problem is, the workplace has changed. Where server backups might have fit how offices worked at one time in the past, relying solely on server backups today means you could be missing valuable endpoint data from your backups. On top of that, you likely are unnecessarily expending valuable user and IT time in attempting to secure and restore endpoint data.

Times Have Changed, and so have Effective Enterprise Backup Strategies

The ways we use computers and handle files today are vastly different from just five or ten years ago. Employees are mobile, and we no longer are limited to monolithic PC and Mac-based office suites. Cloud applications are everywhere. Company-mandated network drive policies are difficult to enforce as office practices change, devices proliferate, and organizational culture evolves. Besides, your IT staff has other things to do than babysit your employees to make sure they follow your organization’s policies for managing files.

Server Backup has its Place, but Does it Support How People Work Today?

Many organizations still rely on server backup. If your organization works primarily in centralized offices with all endpoints — likely desktops — connected directly to your network, and you maintain tight control of how employees manage their files, it still might work for you.

Your IT department probably has set network drive policies that require employees to save files in standard places that are regularly backed up to your file server. Turns out, though, that even standard applications don’t always save files where IT would like them to be. They could be in a directory or folder that’s not regularly backed up.

As employees have become more mobile, they have adopted practices that enable them to access files from different places, but these practices might not fit in with your organization’s server policies. An employee saving a file to Dropbox might be planning to copy it to an “official” location later, but whether that ever happens could be doubtful. Often people don’t realize until it’s too late that accidentally deleting a file in one sync service directory means that all copies in all locations — even the cloud — are also deleted.

Employees are under increasing demands to produce, which means that network drive policies aren’t always followed; time constraints and deadlines can cause best practices to go out the window. Users will attempt to comply with policies as best they can — and you might get 70% or even 75% effective compliance — but getting even to that level requires training, monitoring, and repeatedly reminding employees of policies they need to follow — none of which leads to a good work environment.

Even if you get to 75% compliance with network file policies, what happens if the critical file needed to close out an end-of-year financial summary isn’t one of the files backed up? The effort required for IT to get from 70% to 80% or 90% of an endpoint’s files effectively backed up could require multiple hours from your IT department, and you still might not have backed up the one critical file you need later.

Your Organization Operates on its Data — And Today That Data Exists in Multiple Locations

Users are no longer tied to one endpoint, and may use different computers in the office, at home, or traveling. The greater the number of endpoints used, the greater the chance of an accidental or malicious device loss or data corruption. The loss of the Sales VP’s laptop at the airport on her way back from meeting with major customers can affect an entire organization and require weeks to resolve.

Even with the best intentions and efforts, following policies when out of the office can be difficult or impossible. Connecting to your private network when remote most likely requires a VPN, and VPN connectivity can be challenging from the lobby Wi-Fi at the Radisson. Server restores require time from the IT staff, which can mean taking resources away from other IT priorities and a growing backlog of requests from users to need their files as soon as possible. When users are dependent on IT to get back files critical to their work, employee productivity and often deadlines are affected.

Managing Finite Server Storage Is an Ongoing Challenge

Network drive backup usually requires on-premises data storage for endpoint backups. Since it is a finite resource, allocating that storage is another burden on your IT staff. To make sure that storage isn’t exceeded, IT departments often ration storage by department and/or user — another oversight duty for IT, and even more choices required by your IT department and department heads who have to decide which files to prioritize for backing up.

Adding Backblaze Endpoint Backup Improves Business Continuity and Productivity

Having an endpoint backup strategy in place can mitigate these problems and improve user productivity, as well. A good endpoint backup service, such as Backblaze Cloud Backup, will ensure that all devices are backed up securely, automatically, without requiring any action by the user or by your IT department.

For 99% of users, no configuration is required for Backblaze Backup. Everything on the endpoint is encrypted and securely backed up to the cloud, including program configuration files and files outside of standard document folders. Even temp files are backed up, which can prove invaluable when recovering a file after a crash or other program interruption. Cloud storage is unlimited with Backblaze Backup, so there are no worries about running out of storage or rationing file backups.

The Backblaze client can be silently and remotely installed to both Macintosh and Windows clients with no user interaction. And, with Backblaze Groups, your IT staff has complete visibility into when files were last backed up. IT staff can recover any backed up file, folder, or entire computer from the admin panel, and even give file restore capability to the user, if desired, which reduces dependency on IT and time spent waiting for restores.

With over 500 petabytes of customer data stored and one million files restored every hour of every day by Backblaze customers, you know that Backblaze Backup works for its users.

You Need Data Security That Matches the Way People Work Today

Both file server and endpoint backup have their places in an organization’s data security plan, but their use and value differ. If you already are using file server backup, adding endpoint backup will make a valuable contribution to your organization by reducing workload, improving productivity, and increasing confidence that all critical files are backed up.

By guaranteeing fast and automatic backup of all endpoint data, and matching the current way organizations and people work with data, Backblaze Backup will enable you to effectively and affordably meet the data security demands of your organization.

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