Tag Archives: FIPS

Build a Schema-On-Read Analytics Pipeline Using Amazon Athena

Post Syndicated from Ujjwal Ratan original https://aws.amazon.com/blogs/big-data/build-a-schema-on-read-analytics-pipeline-using-amazon-athena/

A robust analytics platform is critical for an organization’s success. However, an analytical system is only as good as the data that is used to power it. Wrong or incomplete data can derail analytical projects completely. Moreover, with varied data types and sources being added to the analytical platform, it’s important that the platform is able to keep up. This means the system has to be flexible enough to adapt to changing data types and volumes.

For a platform based on relational databases, this would mean countless data model updates, code changes, and regression testing, all of which can take a very long time. For a decision support system, it’s imperative to provide the correct answers quickly. Architects must design analytical platforms with the following criteria:

  • Help ingest data easily from multiple sources
  • Analyze it for completeness and accuracy
  • Use it for metrics computations
  • Store the data assets and scale as they grow rapidly without causing disruptions
  • Adapt to changes as they happen
  • Have a relatively short development cycle that is repeatable and easy to implement.

Schema-on-read is a unique approach for storing and querying datasets. It reverses the order of things when compared to schema-on-write in that the data can be stored as is and you apply a schema at the time that you read it. This approach has changed the time to value for analytical projects. It means you can immediately load data and can query it to do exploratory activities.

In this post, I show how to build a schema-on-read analytical pipeline, similar to the one used with relational databases, using Amazon Athena. The approach is completely serverless, which allows the analytical platform to scale as more data is stored and processed via the pipeline.

The data integration challenge

Part of the reason why it’s so difficult to build analytical platforms are the challenges associated with data integration. This is usually done as a series of Extract Transform Load (ETL) jobs that pull data from multiple varied sources and integrate them into a central repository. The multiple steps of ETL can be viewed as a pipeline where raw data is fed in one end and integrated data accumulates at the other.

Two major hurdles complicate the development of ETL pipelines:

  1. The sheer volume of data needing to be integrated makes it very difficult to scale these jobs.
  2. There are an immense variety of data formats from where information needs to be gathered.

Put this in context by looking at the healthcare industry. Building an analytical pipeline for healthcare usually involves working with multiple datasets that cover care quality, provider operations, patient clinical records, and patient claims data. Customers are now looking for even more data sources that may contain valuable information that can be analyzed. Examples include social media data, data from devices and sensors, and biometric and human-generated data such as notes from doctors. These new data sources do not rely on fixed schemas, so integrating them by conventional ETL jobs is much more difficult.

The volume of data available for analysis is growing as well. According to an article published by NCBI, the US healthcare system reached a collective data volume of 150 exabytes in 2011. With the current rate of growth, it is expected to quickly reach zettabyte scale, and soon grow into the yottabytes.

Why schema-on-read?

The traditional data warehouses of the early 2000s, like the one shown in the following diagram, were based on a standard four-layer approach of ingest, stage, store, and report. This involved building and maintaining huge data models that suited certain sources and analytical queries. This type of design is based on a schema-on-write approach, where you write data into a predefined schema and read data by querying it.

As we progressed to more varied datasets and analytical requirements, the predefined schemas were not able to keep up. Moreover, by the time the models were built and put into production, the requirements had changed.

Schema-on-read provides much needed flexibility to an analytical project. Not having to rely on physical schemas improves the overall performance when it comes to high volume data loads. Because there are no physical constraints for the data, it works well with datasets with undefined data structures. It gives customers the power to experiment and try out different options.

How can Athena help?

Athena is a serverless analytical query engine that allows you to start querying data stored in Amazon S3 instantly. It supports standard formats like CSV and Parquet and integrates with Amazon QuickSight, which allows you to build interactive business intelligence reports.

The Amazon Athena User Guide provides multiple best practices that should be considered when using Athena for analytical pipelines at production scale. There are various performance tuning techniques that you can apply to speed up query performance. For more information, see Top 10 Performance Tuning Tips for Amazon Athena.

Solution architecture

To demonstrate this solution, I use the healthcare dataset from the Centers for Disease Control (CDC) Behavioral Risk Factor Surveillance system (BRFSS). It gathers data via telephone surveys for health-related risk behaviors and conditions, and the use of preventive services. The dataset is available as zip files from the CDC FTP portal for general download and analysis. There is also a user guide with comprehensive details about the program and the process of collecting data.

The following diagram shows the schema-on-read pipeline that demonstrates this solution.

In this architecture, S3 is the central data repository for the CSV files, which are divided by behavioral risk factors like smoking, drinking, obesity, and high blood pressure.

Athena is used to collectively query the CSV files in S3. It uses the AWS Glue Data Catalog to maintain the schema details and applies it at the time of querying the data.

The dataset is further filtered and transformed into a subset that is specifically used for reporting with Amazon QuickSight.

As new data files become available, they are incrementally added to the S3 bucket and the subset query automatically appends them to the end of the table. The dashboards in Amazon QuickSight are refreshed with the new values of calculated metrics.

Walkthrough

To use Athena in an analytical pipeline, you have to consider how to design it for initial data ingestion and the subsequent incremental ingestions. Moreover, you also have to decide on the mechanism to trigger a particular stage in the pipeline, which can either be scheduled or event-based.

For the purposes of this post, you build a simple pipeline where the data is:

  • Ingested into a staging area in S3
  • Filtered and transformed for reporting into a different S3 location
  • Transformed into a reporting table in Athena
  • Included into a dashboard created using Amazon QuickSight
  • Incrementally ingested for updates

This approach is highly customizable and can be used for building more complex pipelines with many more steps.

Data staging in S3

The data ingestion into S3 is fairly straightforward. The files can be ingested via the S3 command line interface (CLI), API, or the AWS Management Console. The data files are in CSV format and already divided by the behavioral condition. To improve performance, I recommend that you use partitioned data with Athena, especially when dealing with large volumes. You can use pre-partitioned data in S3 or build partitions later in the process. The example you are working with has a total of 247 CSV files storing about 205 MB of data across them, but typical production scale deployment would be much larger.

To automate the pipeline, you can make it either event-based or schedule-based. If you take the event-based approach, you can make use of S3 events to trigger an action when the files are uploaded. The event triggers an action, using an AWS Lambda function that corresponds to another step in the pipeline. Traditional ETL jobs have to rely on mechanisms like database triggers to enable this, which can cause additional performance overhead.

If you choose to go with a scheduled-based approach, you can use Lambda with scheduled events. The schedule is managed via a cron expression and the Lambda function is used to run the next step of the pipeline. This is suitable for workloads similar to a scheduled batch ETL job.

Filter and data transformation

To filter and transform the dataset, first look at the overall counts and structure of the data. This allows you to choose the columns that are important for a given report and use the filter clause to extract a subset of the data. Transforming the data as you progress through the pipeline ensures that you are only exposing relevant data to the reporting layer, to optimize performance.

To look at the entire dataset, create a table in Athena to go across the entire data volume. This can be done using the following query:

CREATE EXTERNAL TABLE IF NOT EXISTS brfsdata(
ID	STRING,
HIW	STRING,
SUSA_NAME	STRING,
MATCH_NAME	STRING,
CHSI_NAME	STRING,
NSUM	STRING,
MEAN	STRING,
FLAG	STRING,
IND	STRING,
UP_CI	STRING,
LOW_CI	STRING,
SEMEAN	STRING,
AGE_ADJ	STRING,
DATASRC	STRING,
FIPS	STRING,
FIPNAME	STRING,
HRR	STRING,
DATA_YR	STRING,
UNIT	STRING,
AGEGRP	STRING,
GENDER	STRING,
RACE	STRING,
EHN	STRING,
EDU	STRING,
FAMINC	STRING,
DISAB	STRING,
METRO	STRING,
SEXUAL	STRING,
FAMSTRC	STRING,
MARITAL	STRING,
POP_SPC	STRING,
POP_POLICY	STRING 
)
ROW FORMAT DELIMITED
  FIELDS TERMINATED BY ','
  ESCAPED BY '\\'
  LINES TERMINATED BY '\n'
  LOCATION "s3://<YourBucket/YourPrefix>"

Replace YourBucket and YourPrefix with your corresponding values.

In this case, there are a total of ~1.4 million records, which you get by running a simple COUNT(*) query on the table.

From here, you can run multiple analysis queries on the dataset. For example, you can find the number of records that fall into a certain behavioral risk, or the state that has the highest number of diabetic patients recorded. These metrics provide data points that help to determine the attributes that would be needed from a reporting perspective.

As you can see, this is much simpler compared to a schema-on-write approach where the analysis of the source dataset is much more difficult. This solution allows you to design the reporting platform in accordance with the questions you are looking to answer from your data. The source data analysis is the first step to design a good analytical platform and this approach allows customers to do that much earlier in the project lifecycle.

Reporting table

After you have completed the source data analysis, the next step is to filter out the required data and transform it to create a reporting database. This is synonymous to the data mart in a standard analytical pipeline. Based on the analysis carried out in the previous step, you might notice some mismatches with the data headers. You might also identify the filter clauses to apply to the dataset to get to your reporting data.

Athena automatically saves query results in S3 for every run. The default bucket for this is created in the following format:

aws-athena-query-results-<AWS Account ID>-<AWS Region>

Athena creates a prefix for each saved query and stores the result set as CSV files organized by dates. You can use this feature to filter out result datasets and store them in an S3 bucket for reporting.

To enable this, create queries that can filter out and transform the subset of data on which to report. For this use case, create three separate queries to filter out unwanted data and fix the column headers:

Query 1:

SELECT ID, up_ci AS source, semean AS state, datasrc 
AS year, fips AS unit, fipname AS age,mean, current_date AS dt, current_time AS tm FROM brfsdata 
WHERE ID != '' AND hrr IS NULL AND semean NOT LIKE '%29193%'

Query 2:

SELECT ID, up_ci AS source, semean AS state, datasrc 
AS year, fips AS unit, fipname AS age,mean, current_date AS dt, current_time AS tm
FROM brfsdata WHERE ID != '' AND hrr IS NOT NULL AND up_ci LIKE '%BRFSS%'and semean NOT LIKE '"%' AND semean NOT LIKE '%29193%'

Query 3:

SELECT ID, low_ci AS source, age_adj AS state, fips 
AS year, fipname AS unit, hrr AS age,mean, current_date AS dt, current_time AS tm
FROM brfsdata WHERE ID != '' AND hrr IS NOT NULL AND up_ci NOT LIKE '%BRFSS%' AND age_adj NOT LIKE '"%' AND semean NOT LIKE '%29193%' AND low_ci LIKE 'BRFSS'

You can save these queries in Athena so that you can get to the query results easily every time they are executed. The following screenshot is an example of the results when query 1 is executed four times.

The next step is to copy these results over to a new bucket for creating your reporting table. This can be done by running an S3 CP command from the CLI or API, as shown below:

aws s3 cp s3://aws-athena-query-results-YOUR_ACCOUNT_ID-us-east-1/Query1/2017/03/23/ s3://healthdataset/brfsdata/Reporting_Data --recursive --exclude "*.*" --include "*.csv"

Note the prefix structure in which Athena stores query results. It creates a separate prefix for each day in which the query is executed, and stores the corresponding CSV and metadata file for each run. Copy the result set over to a new prefix “Reporting_Data”. Use the “exclude” and “include” option of S3 CP to only copy the CSV files and use “recursive” to copy all the files from the run.

You can replace the value of the saved query name from “Query1” to “Query2” or “Query3” to copy all data resulting from those queries to the same target prefix. For pipelines that require more complicated transformations, divide the query transformation into multiple steps and execute them based on events or schedule them, as described in the earlier data staging step.

Amazon QuickSight dashboard

After the filtered results sets are copied as CSV files into the new Reporting_Data prefix, create a new table in Athena that is used specifically for BI reporting. This can be done using a create table statement similar to the one below:

CREATE EXTERNAL TABLE IF NOT EXISTS BRFSS_REPORTING(
ID varchar(100),
source varchar(100),
state varchar(100),
year int,
unit varchar(10),
age varchar(10),
mean float 
)
ROW FORMAT DELIMITED
  FIELDS TERMINATED BY ','
  ESCAPED BY '\\'
  LINES TERMINATED BY '\n'
  LOCATION "s3://healthdataset/brfsdata/Reporting_Data"

This table can now act as a source for a dashboard on Amazon QuickSight, which is a straightforward process to enable. When you choose a new data source, Athena shows up as an option and Amazon QuickSight automatically detects the tables in Athena that are exposed for querying. Here are the data sources supported by Athena at the time of this post:

After choosing Athena, give a name to the data source and choose the database. The tables available for querying automatically show up in the list.

If you choose “BRFSS_REPORTING”, you can create custom metrics using the columns in the reporting table, which can then be used in reports and dashboards.

For more information about features and visualizations, see the Amazon QuickSight User Guide.

Incremental data ingestion

To build a complete pipeline, think about ingesting data incrementally for new records as they become available. To enable this, make sure that the data can be incrementally ingested into the reporting schema and that the reporting metrics are refreshed on each run of the report. To demonstrate this, look at a scenario where the new dataset is ingested in a periodic basis into S3, and which has to be included into the reporting schema when calculating the metrics.

Look at the number of records in the reporting table before the incremental ingestion.

SELECT count(*) FROM brfss_reporting;

The results are as follows:

  _col0
1 713123

Use Query1 as an example transformation query that can isolate the incremental load. Here is a view of the query result bucket before Query1 runs.

After the incremental data is ingested in S3, trigger an event (or pre-schedule) an execution of Query1 in Athena, which results in the csv result set and the metadata file as shown below.

Next, trigger (or schedule) the copy command to copy the incremental records file into the reporting prefix. This is easily automated by using the predefined structure in which Athena saves the query results on S3. On checking the records count in the reporting table after copying, you get an increased count.

  _col0
1 810042

This shows that 96,919 records were added to our reporting table that can be used in metric calculations.

This process can be implemented programmatically to add incremental records into the reporting table every time new records are ingested into the staging area. As a result, you can simulate an end-to-end analytical pipeline that runs based on events or is scheduled to run as a batch workload.

Conclusion

Using Athena, you can benefit from the advantages of a schema-on-read analytical application. You can combine other AWS services like Lambda and Amazon QuickSight to build an end-to-end analytical pipeline.

However, it’s important to note that a schema-on-read analytical pipeline may not be the answer for all use cases. Carefully consider the choices between schema-on-read and schema-on-write. The source systems you are working with play a critical role in making that decision. For example:

  • Some systems have defined data structures and the chances of variation is very rare. These systems can work with a fixed relational target schema.
  • If the target queries mostly involve joining across normalized tables, they work better with a relational database. For this use case, schema-on-write is a good choice.
  • The query performance in a schema-on-write application is faster as the data is pre-structured. For fixed dashboards with little to no changes, a schema-on-write is a good choice.

You can also choose to go hybrid. Offload a part of the pipeline that deals with unstructured flat datasets into a schema-on-read architecture, and integrate the output into the main schema-on-write pipeline at a later stage. AWS provides multiple options to build analytical pipelines that suit various use cases. For more information, read about the AWS big data and analytics services.

If you have questions or suggestions, please comment below.

Additional Reading

Don’t forget to check out the top 10 tips for Amazon Athena that can improve query performance.

About the Author

Ujjwal Ratan is a healthcare and life sciences Solutions Architect at AWS. He has worked with organizations ranging from large enterprises to smaller startups on problems related to distributed computing, analytics and machine learning. In his free time, he enjoys listening to (and playing) music and taking unplanned road trips with his family.

 

 

 

How to Configure an LDAPS Endpoint for Simple AD

Post Syndicated from Cameron Worrell original https://aws.amazon.com/blogs/security/how-to-configure-an-ldaps-endpoint-for-simple-ad/

Simple AD, which is powered by Samba  4, supports basic Active Directory (AD) authentication features such as users, groups, and the ability to join domains. Simple AD also includes an integrated Lightweight Directory Access Protocol (LDAP) server. LDAP is a standard application protocol for the access and management of directory information. You can use the BIND operation from Simple AD to authenticate LDAP client sessions. This makes LDAP a common choice for centralized authentication and authorization for services such as Secure Shell (SSH), client-based virtual private networks (VPNs), and many other applications. Authentication, the process of confirming the identity of a principal, typically involves the transmission of highly sensitive information such as user names and passwords. To protect this information in transit over untrusted networks, companies often require encryption as part of their information security strategy.

In this blog post, we show you how to configure an LDAPS (LDAP over SSL/TLS) encrypted endpoint for Simple AD so that you can extend Simple AD over untrusted networks. Our solution uses Elastic Load Balancing (ELB) to send decrypted LDAP traffic to HAProxy running on Amazon EC2, which then sends the traffic to Simple AD. ELB offers integrated certificate management, SSL/TLS termination, and the ability to use a scalable EC2 backend to process decrypted traffic. ELB also tightly integrates with Amazon Route 53, enabling you to use a custom domain for the LDAPS endpoint. The solution needs the intermediate HAProxy layer because ELB can direct traffic only to EC2 instances. To simplify testing and deployment, we have provided an AWS CloudFormation template to provision the ELB and HAProxy layers.

This post assumes that you have an understanding of concepts such as Amazon Virtual Private Cloud (VPC) and its components, including subnets, routing, Internet and network address translation (NAT) gateways, DNS, and security groups. You should also be familiar with launching EC2 instances and logging in to them with SSH. If needed, you should familiarize yourself with these concepts and review the solution overview and prerequisites in the next section before proceeding with the deployment.

Note: This solution is intended for use by clients requiring an LDAPS endpoint only. If your requirements extend beyond this, you should consider accessing the Simple AD servers directly or by using AWS Directory Service for Microsoft AD.

Solution overview

The following diagram and description illustrates and explains the Simple AD LDAPS environment. The CloudFormation template creates the items designated by the bracket (internal ELB load balancer and two HAProxy nodes configured in an Auto Scaling group).

Diagram of the the Simple AD LDAPS environment

Here is how the solution works, as shown in the preceding numbered diagram:

  1. The LDAP client sends an LDAPS request to ELB on TCP port 636.
  2. ELB terminates the SSL/TLS session and decrypts the traffic using a certificate. ELB sends the decrypted LDAP traffic to the EC2 instances running HAProxy on TCP port 389.
  3. The HAProxy servers forward the LDAP request to the Simple AD servers listening on TCP port 389 in a fixed Auto Scaling group configuration.
  4. The Simple AD servers send an LDAP response through the HAProxy layer to ELB. ELB encrypts the response and sends it to the client.

Note: Amazon VPC prevents a third party from intercepting traffic within the VPC. Because of this, the VPC protects the decrypted traffic between ELB and HAProxy and between HAProxy and Simple AD. The ELB encryption provides an additional layer of security for client connections and protects traffic coming from hosts outside the VPC.

Prerequisites

  1. Our approach requires an Amazon VPC with two public and two private subnets. The previous diagram illustrates the environment’s VPC requirements. If you do not yet have these components in place, follow these guidelines for setting up a sample environment:
    1. Identify a region that supports Simple AD, ELB, and NAT gateways. The NAT gateways are used with an Internet gateway to allow the HAProxy instances to access the internet to perform their required configuration. You also need to identify the two Availability Zones in that region for use by Simple AD. You will supply these Availability Zones as parameters to the CloudFormation template later in this process.
    2. Create or choose an Amazon VPC in the region you chose. In order to use Route 53 to resolve the LDAPS endpoint, make sure you enable DNS support within your VPC. Create an Internet gateway and attach it to the VPC, which will be used by the NAT gateways to access the internet.
    3. Create a route table with a default route to the Internet gateway. Create two NAT gateways, one per Availability Zone in your public subnets to provide additional resiliency across the Availability Zones. Together, the routing table, the NAT gateways, and the Internet gateway enable the HAProxy instances to access the internet.
    4. Create two private routing tables, one per Availability Zone. Create two private subnets, one per Availability Zone. The dual routing tables and subnets allow for a higher level of redundancy. Add each subnet to the routing table in the same Availability Zone. Add a default route in each routing table to the NAT gateway in the same Availability Zone. The Simple AD servers use subnets that you create.
    5. The LDAP service requires a DNS domain that resolves within your VPC and from your LDAP clients. If you do not have an existing DNS domain, follow the steps to create a private hosted zone and associate it with your VPC. To avoid encryption protocol errors, you must ensure that the DNS domain name is consistent across your Route 53 zone and in the SSL/TLS certificate (see Step 2 in the “Solution deployment” section).
  2. Make sure you have completed the Simple AD Prerequisites.
  3. We will use a self-signed certificate for ELB to perform SSL/TLS decryption. You can use a certificate issued by your preferred certificate authority or a certificate issued by AWS Certificate Manager (ACM).
    Note: To prevent unauthorized connections directly to your Simple AD servers, you can modify the Simple AD security group on port 389 to block traffic from locations outside of the Simple AD VPC. You can find the security group in the EC2 console by creating a search filter for your Simple AD directory ID. It is also important to allow the Simple AD servers to communicate with each other as shown on Simple AD Prerequisites.

Solution deployment

This solution includes five main parts:

  1. Create a Simple AD directory.
  2. Create a certificate.
  3. Create the ELB and HAProxy layers by using the supplied CloudFormation template.
  4. Create a Route 53 record.
  5. Test LDAPS access using an Amazon Linux client.

1. Create a Simple AD directory

With the prerequisites completed, you will create a Simple AD directory in your private VPC subnets:

  1. In the Directory Service console navigation pane, choose Directories and then choose Set up directory.
  2. Choose Simple AD.
    Screenshot of choosing "Simple AD"
  3. Provide the following information:
    • Directory DNS – The fully qualified domain name (FQDN) of the directory, such as corp.example.com. You will use the FQDN as part of the testing procedure.
    • NetBIOS name – The short name for the directory, such as CORP.
    • Administrator password – The password for the directory administrator. The directory creation process creates an administrator account with the user name Administrator and this password. Do not lose this password because it is nonrecoverable. You also need this password for testing LDAPS access in a later step.
    • Description – An optional description for the directory.
    • Directory Size – The size of the directory.
      Screenshot of the directory details to provide
  4. Provide the following information in the VPC Details section, and then choose Next Step:
    • VPC – Specify the VPC in which to install the directory.
    • Subnets – Choose two private subnets for the directory servers. The two subnets must be in different Availability Zones. Make a note of the VPC and subnet IDs for use as CloudFormation input parameters. In the following example, the Availability Zones are us-east-1a and us-east-1c.
      Screenshot of the VPC details to provide
  5. Review the directory information and make any necessary changes. When the information is correct, choose Create Simple AD.

It takes several minutes to create the directory. From the AWS Directory Service console , refresh the screen periodically and wait until the directory Status value changes to Active before continuing. Choose your Simple AD directory and note the two IP addresses in the DNS address section. You will enter them when you run the CloudFormation template later.

Note: Full administration of your Simple AD implementation is out of scope for this blog post. See the documentation to add users, groups, or instances to your directory. Also see the previous blog post, How to Manage Identities in Simple AD Directories.

2. Create a certificate

In the previous step, you created the Simple AD directory. Next, you will generate a self-signed SSL/TLS certificate using OpenSSL. You will use the certificate with ELB to secure the LDAPS endpoint. OpenSSL is a standard, open source library that supports a wide range of cryptographic functions, including the creation and signing of x509 certificates. You then import the certificate into ACM that is integrated with ELB.

  1. You must have a system with OpenSSL installed to complete this step. If you do not have OpenSSL, you can install it on Amazon Linux by running the command, sudo yum install openssl. If you do not have access to an Amazon Linux instance you can create one with SSH access enabled to proceed with this step. Run the command, openssl version, at the command line to see if you already have OpenSSL installed. 
    [[email protected] ~]$ openssl version
OpenSSL 1.0.1k-fips 8 Jan 2015

  2. Create a private key using the command, openssl genrsa command. 
    [[email protected] tmp]$ openssl genrsa 2048 > privatekey.pem
Generating RSA private key, 2048 bit long modulus
......................................................................................................................................................................+++
..........................+++
e is 65537 (0x10001)

  3. Generate a certificate signing request (CSR) using the openssl req command. Provide the requested information for each field. The Common Name is the FQDN for your LDAPS endpoint (for example, ldap.corp.example.com). The Common Name must use the domain name you will later register in Route 53. You will encounter certificate errors if the names do not match. 
    [[email protected] tmp]$ openssl req -new -key privatekey.pem -out server.csr
You are about to be asked to enter information that will be incorporated into your certificate request.

  4. Use the openssl x509 command to sign the certificate. The following example uses the private key from the previous step (privatekey.pem) and the signing request (server.csr) to create a public certificate named server.crt that is valid for 365 days. This certificate must be updated within 365 days to avoid disruption of LDAPS functionality. 
    [[email protected] tmp]$ openssl x509 -req -sha256 -days 365 -in server.csr -signkey privatekey.pem -out server.crt
Signature ok
subject=/C=XX/L=Default City/O=Default Company Ltd/CN=ldap.corp.example.com
Getting Private key

  5. You should see three files: privatekey.pem, server.crt, and server.csr. 
    [[email protected] tmp]$ ls
privatekey.pem server.crt server.csr

    Restrict access to the private key.

    [[email protected] tmp]$ chmod 600 privatekey.pem

    Keep the private key and public certificate for later use. You can discard the signing request because you are using a self-signed certificate and not using a Certificate Authority. Always store the private key in a secure location and avoid adding it to your source code.

  6. In the ACM console, choose Import a certificate.
  7. Using your favorite Linux text editor, paste the contents of your server.crt file in the Certificate body box.
  8. Using your favorite Linux text editor, paste the contents of your privatekey.pem file in the Certificate private key box. For a self-signed certificate, you can leave the Certificate chain box blank.
  9. Choose Review and import. Confirm the information and choose Import.

3. Create the ELB and HAProxy layers by using the supplied CloudFormation template

Now that you have created your Simple AD directory and SSL/TLS certificate, you are ready to use the CloudFormation template to create the ELB and HAProxy layers.

  1. Load the supplied CloudFormation template to deploy an internal ELB and two HAProxy EC2 instances into a fixed Auto Scaling group. After you load the template, provide the following input parameters. Note: You can find the parameters relating to your Simple AD from the directory details page by choosing your Simple AD in the Directory Service console.
Input parameter Input parameter description
HAProxyInstanceSize The EC2 instance size for HAProxy servers. The default size is t2.micro and can scale up for large Simple AD environments.
MyKeyPair The SSH key pair for EC2 instances. If you do not have an existing key pair, you must create one.
VPCId The target VPC for this solution. Must be in the VPC where you deployed Simple AD and is available in your Simple AD directory details page.
SubnetId1 The Simple AD primary subnet. This information is available in your Simple AD directory details page.
SubnetId2 The Simple AD secondary subnet. This information is available in your Simple AD directory details page.
MyTrustedNetwork Trusted network Classless Inter-Domain Routing (CIDR) to allow connections to the LDAPS endpoint. For example, use the VPC CIDR to allow clients in the VPC to connect.
SimpleADPriIP The primary Simple AD Server IP. This information is available in your Simple AD directory details page.
SimpleADSecIP The secondary Simple AD Server IP. This information is available in your Simple AD directory details page.
LDAPSCertificateARN The Amazon Resource Name (ARN) for the SSL certificate. This information is available in the ACM console.
  1. Enter the input parameters and choose Next.
  2. On the Options page, accept the defaults and choose Next.
  3. On the Review page, confirm the details and choose Create. The stack will be created in approximately 5 minutes.

4. Create a Route 53 record

The next step is to create a Route 53 record in your private hosted zone so that clients can resolve your LDAPS endpoint.

  1. If you do not have an existing DNS domain for use with LDAP, create a private hosted zone and associate it with your VPC. The hosted zone name should be consistent with your Simple AD (for example, corp.example.com).
  2. When the CloudFormation stack is in CREATE_COMPLETE status, locate the value of the LDAPSURL on the Outputs tab of the stack. Copy this value for use in the next step.
  3. On the Route 53 console, choose Hosted Zones and then choose the zone you used for the Common Name box for your self-signed certificate. Choose Create Record Set and enter the following information:
    1. Name – The label of the record (such as ldap).
    2. Type – Leave as A – IPv4 address.
    3. Alias – Choose Yes.
    4. Alias Target – Paste the value of the LDAPSURL on the Outputs tab of the stack.
  4. Leave the defaults for Routing Policy and Evaluate Target Health, and choose Create.
    Screenshot of finishing the creation of the Route 53 record

5. Test LDAPS access using an Amazon Linux client

At this point, you have configured your LDAPS endpoint and now you can test it from an Amazon Linux client.

  1. Create an Amazon Linux instance with SSH access enabled to test the solution. Launch the instance into one of the public subnets in your VPC. Make sure the IP assigned to the instance is in the trusted IP range you specified in the CloudFormation parameter MyTrustedNetwork in Step 3.b.
  2. SSH into the instance and complete the following steps to verify access.
    1. Install the openldap-clients package and any required dependencies:
      sudo yum install -y openldap-clients.
    2. Add the server.crt file to the /etc/openldap/certs/ directory so that the LDAPS client will trust your SSL/TLS certificate. You can copy the file using Secure Copy (SCP) or create it using a text editor.
    3. Edit the /etc/openldap/ldap.conf file and define the environment variables BASE, URI, and TLS_CACERT.
      • The value for BASE should match the configuration of the Simple AD directory name.
      • The value for URI should match your DNS alias.
      • The value for TLS_CACERT is the path to your public certificate.

Here is an example of the contents of the file.

BASE dc=corp,dc=example,dc=com
URI ldaps://ldap.corp.example.com
TLS_CACERT /etc/openldap/certs/server.crt

To test the solution, query the directory through the LDAPS endpoint, as shown in the following command. Replace corp.example.com with your domain name and use the Administrator password that you configured with the Simple AD directory

$ ldapsearch -D "[email protected]corp.example.com" -W sAMAccountName=Administrator

You should see a response similar to the following response, which provides the directory information in LDAP Data Interchange Format (LDIF) for the administrator distinguished name (DN) from your Simple AD LDAP server.

# extended LDIF
#
# LDAPv3
# base <dc=corp,dc=example,dc=com> (default) with scope subtree
# filter: sAMAccountName=Administrator
# requesting: ALL
#

# Administrator, Users, corp.example.com
dn: CN=Administrator,CN=Users,DC=corp,DC=example,DC=com
objectClass: top
objectClass: person
objectClass: organizationalPerson
objectClass: user
description: Built-in account for administering the computer/domain
instanceType: 4
whenCreated: 20170721123204.0Z
uSNCreated: 3223
name: Administrator
objectGUID:: l3h0HIiKO0a/ShL4yVK/vw==
userAccountControl: 512
…

You can now use the LDAPS endpoint for directory operations and authentication within your environment. If you would like to learn more about how to interact with your LDAPS endpoint within a Linux environment, here are a few resources to get started:

Troubleshooting

If you receive an error such as the following error when issuing the ldapsearch command, there are a few things you can do to help identify issues.

ldap_sasl_bind(SIMPLE): Can't contact LDAP server (-1)
  • You might be able to obtain additional error details by adding the -d1 debug flag to the ldapsearch command in the previous section. 
    $ ldapsearch -D "[email protected]" -W sAMAccountName=Administrator –d1

  • Verify that the parameters in ldap.conf match your configured LDAPS URI endpoint and that all parameters can be resolved by DNS. You can use the following dig command, substituting your configured endpoint DNS name. 
    $ dig ldap.corp.example.com

  • Confirm that the client instance from which you are connecting is in the CIDR range of the CloudFormation parameter, MyTrustedNetwork.
  • Confirm that the path to your public SSL/TLS certificate configured in ldap.conf as TLS_CAERT is correct. You configured this in Step 5.b.3. You can check your SSL/TLS connection with the command, substituting your configured endpoint DNS name for the string after –connect. 
    $ echo -n | openssl s_client -connect ldap.corp.example.com:636

  • Verify that your HAProxy instances have the status InService in the EC2 console: Choose Load Balancers under Load Balancing in the navigation pane, highlight your LDAPS load balancer, and then choose the Instances

Conclusion

You can use ELB and HAProxy to provide an LDAPS endpoint for Simple AD and transport sensitive authentication information over untrusted networks. You can explore using LDAPS to authenticate SSH users or integrate with other software solutions that support LDAP authentication. This solution’s CloudFormation template is available on GitHub.

If you have comments about this post, submit them in the “Comments” section below. If you have questions about or issues implementing this solution, start a new thread on the Directory Service forum.

– Cameron and Jeff

New – Encryption of Data at Rest for Amazon Elastic File System (EFS)

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/new-encryption-at-rest-for-amazon-elastic-file-system-efs/

We launched Amazon Elastic File System in production form a little over a year ago (see Amazon Elastic File System – Production Ready in Three Regions for more information). Later in the year we added On-Premises access via Direct Connect and made EFS available in the US East (Ohio) Region, following up this year with availability in the EU (Frankfurt) and Asia Pacific (Sydney) Regions.

Encryption at Rest
Today we are adding support for encryption of data at rest. When you create a new file system, you can select a key that will be used to encrypt the contents of the files that you store on the file system. The key can be a built-in key that is managed by AWS or a key that you created yourself using AWS Key Management Service (KMS). File metadata (file names, directory names, and directory contents) will be encrypted using a key managed by AWS. Both forms of encryption are implemented using an industry-standard AES-256 algorithm.

You can set this up in seconds when you create a new file system. You simply choose the built-in key (aws/elasticfilesystem) or one of your own:

EFS will take care of the rest! You can select the filesystem in the console to verify that it is encrypted as desired:

A cryptographic algorithm that meets the approval of FIPS 140-2 is used to encrypt data and metadata. The encryption is transparent and has a minimal effect on overall performance.

You can use AWS Identity and Access Management (IAM) to control access to the Customer Master Key (CMK). The CMK must be enabled in order to grant access to the file system; disabling the key prevents it from being used to create new file systems and blocks access (after a period of time) to existing file systems that it protects. To learn more about your options, read Managing Access to Encrypted File Systems.

Available Now
Encryption of data at rest is available now in all regions where EFS is supported, at no additional charge.

— Jeff;

 

AWS CloudHSM Update – Cost Effective Hardware Key Management at Cloud Scale for Sensitive & Regulated Workloads

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-cloudhsm-update-cost-effective-hardware-key-management/

Our customers run an incredible variety of mission-critical workloads on AWS, many of which process and store sensitive data. As detailed in our Overview of Security Processes document, AWS customers have access to an ever-growing set of options for encrypting and protecting this data. For example, Amazon Relational Database Service (RDS) supports encryption of data at rest and in transit, with options tailored for each supported database engine (MySQL, SQL Server, Oracle, MariaDB, PostgreSQL, and Aurora).

Many customers use AWS Key Management Service (KMS) to centralize their key management, with others taking advantage of the hardware-based key management, encryption, and decryption provided by AWS CloudHSM to meet stringent security and compliance requirements for their most sensitive data and regulated workloads (you can read my post, AWS CloudHSM – Secure Key Storage and Cryptographic Operations, to learn more about Hardware Security Modules, also known as HSMs).

Major CloudHSM Update
Today, building on what we have learned from our first-generation product, we are making a major update to CloudHSM, with a set of improvements designed to make the benefits of hardware-based key management available to a much wider audience while reducing the need for specialized operating expertise. Here’s a summary of the improvements:

Pay As You Go – CloudHSM is now offered under a pay-as-you-go model that is simpler and more cost-effective, with no up-front fees.

Fully Managed – CloudHSM is now a scalable managed service; provisioning, patching, high availability, and backups are all built-in and taken care of for you. Scheduled backups extract an encrypted image of your HSM from the hardware (using keys that only the HSM hardware itself knows) that can be restored only to identical HSM hardware owned by AWS. For durability, those backups are stored in Amazon Simple Storage Service (S3), and for an additional layer of security, encrypted again with server-side S3 encryption using an AWS KMS master key.

Open & Compatible  – CloudHSM is open and standards-compliant, with support for multiple APIs, programming languages, and cryptography extensions such as PKCS #11, Java Cryptography Extension (JCE), and Microsoft CryptoNG (CNG). The open nature of CloudHSM gives you more control and simplifies the process of moving keys (in encrypted form) from one CloudHSM to another, and also allows migration to and from other commercially available HSMs.

More Secure – CloudHSM Classic (the original model) supports the generation and use of keys that comply with FIPS 140-2 Level 2. We’re stepping that up a notch today with support for FIPS 140-2 Level 3, with security mechanisms that are designed to detect and respond to physical attempts to access or modify the HSM. Your keys are protected with exclusive, single-tenant access to tamper-resistant HSMs that appear within your Virtual Private Clouds (VPCs). CloudHSM supports quorum authentication for critical administrative and key management functions. This feature allows you to define a list of N possible identities that can access the functions, and then require at least M of them to authorize the action. It also supports multi-factor authentication using tokens that you provide.

AWS-Native – The updated CloudHSM is an integral part of AWS and plays well with other tools and services. You can create and manage a cluster of HSMs using the AWS Management Console, AWS Command Line Interface (CLI), or API calls.

Diving In
You can create CloudHSM clusters that contain 1 to 32 HSMs, each in a separate Availability Zone in a particular AWS Region. Spreading HSMs across AZs gives you high availability (including built-in load balancing); adding more HSMs gives you additional throughput. The HSMs within a cluster are kept in sync: performing a task or operation on one HSM in a cluster automatically updates the others. Each HSM in a cluster has its own Elastic Network Interface (ENI).

All interaction with an HSM takes place via the AWS CloudHSM client. It runs on an EC2 instance and uses certificate-based mutual authentication to create secure (TLS) connections to the HSMs.

At the hardware level, each HSM includes hardware-enforced isolation of crypto operations and key storage. Each customer HSM runs on dedicated processor cores.

Setting Up a Cluster
Let’s set up a cluster using the CloudHSM Console:

I click on Create cluster to get started, select my desired VPC and the subnets within it (I can also create a new VPC and/or subnets if needed):

Then I review my settings and click on Create:

After a few minutes, my cluster exists, but is uninitialized:

Initialization simply means retrieving a certificate signing request (the Cluster CSR):

And then creating a private key and using it to sign the request (these commands were copied from the Initialize Cluster docs and I have omitted the output. Note that ID identifies the cluster):

$ openssl genrsa -out CustomerRoot.key 2048
$ openssl req -new -x509 -days 365 -key CustomerRoot.key -out CustomerRoot.crt
$ openssl x509 -req -days 365 -in ID_ClusterCsr.csr   \
                              -CA CustomerRoot.crt    \
                              -CAkey CustomerRoot.key \
                              -CAcreateserial         \
                              -out ID_CustomerHsmCertificate.crt

The next step is to apply the signed certificate to the cluster using the console or the CLI. After this has been done, the cluster can be activated by changing the password for the HSM’s administrative user, otherwise known as the Crypto Officer (CO).

Once the cluster has been created, initialized and activated, it can be used to protect data. Applications can use the APIs in AWS CloudHSM SDKs to manage keys, encrypt & decrypt objects, and more. The SDKs provide access to the CloudHSM client (running on the same instance as the application). The client, in turn, connects to the cluster across an encrypted connection.

Available Today
The new HSM is available today in the US East (Northern Virginia), US West (Oregon), US East (Ohio), and EU (Ireland) Regions, with more in the works. Pricing starts at $1.45 per HSM per hour.

— Jeff;

How to Update AWS CloudHSM Devices and Client Instances to the Software and Firmware Versions Supported by AWS

Post Syndicated from Tracy Pierce original https://aws.amazon.com/blogs/security/how-to-update-aws-cloudhsm-devices-and-client-instances-to-the-software-and-firmware-versions-supported-by-aws/

As I explained in my previous Security Blog post, a hardware security module (HSM) is a hardware device designed with the security of your data and cryptographic key material in mind. It is tamper-resistant hardware that prevents unauthorized users from attempting to pry open the device, plug in any extra devices to access data or keys such as subtokens, or damage the outside housing. The HSM device AWS CloudHSM offers is the Luna SA 7000 (also called Safenet Network HSM 7000), which is created by Gemalto. Depending on the firmware version you install, many of the security properties of these HSMs will have been validated under Federal Information Processing Standard (FIPS) 140-2, a standard issued by the National Institute of Standards and Technology (NIST) for cryptography modules. These standards are in place to protect the integrity and confidentiality of the data stored on cryptographic modules.

To help ensure its continued use, functionality, and support from AWS, we suggest that you update your AWS CloudHSM device software and firmware as well as the client instance software to current versions offered by AWS. As of the publication of this blog post, the current non-FIPS-validated versions are 5.4.9/client, 5.3.13/software, and 6.20.2/firmware, and the current FIPS-validated versions are 5.4.9/client, 5.3.13/software, and 6.10.9/firmware. (The firmware version determines FIPS validation.) It is important to know your current versions before updating so that you can follow the correct update path.

In this post, I demonstrate how to update your current CloudHSM devices and client instances so that you are using the most current versions of software and firmware. If you contact AWS Support for CloudHSM hardware and application issues, you will be required to update to these supported versions before proceeding. Also, any newly provisioned CloudHSM devices will use these supported software and firmware versions only, and AWS does not offer “downgrade” options.

Note: Before you perform any updates, check with your local CloudHSM administrator and application developer to verify that these updates will not conflict with your current applications or architecture.

Overview of the update process

To update your client and CloudHSM devices, you must use both update paths offered by AWS. The first path involves updating the software on your client instance, also known as a control instance. Following the second path updates the software first and then the firmware on your CloudHSM devices. The CloudHSM software must be updated before the firmware because of the firmware’s dependencies on the software in order to work appropriately.

As I demonstrate in this post, the correct update order is:

  1. Updating your client instance
  2. Updating your CloudHSM software
  3. Updating your CloudHSM firmware

To update your client instance, you must have the private SSH key you created when you first set up your client environment. This key is used to connect via SSH protocol on port 22 of your client instance. If you have more than one client instance, you must repeat this connection and update process on each of them. The following diagram shows the flow of an SSH connection from your local network to your client instances in the AWS Cloud.

Diagram that shows the flow of an SSH connection from your local network to your client instances in the AWS Cloud

After you update your client instance to the most recent software (5.3.13), you then must update the CloudHSM device software and firmware. First, you must initiate an SSH connection from any one client instance to each CloudHSM device, as illustrated in the following diagram. A successful SSH connection will have you land at the Luna shell, denoted by lunash:>. Second, you must be able to initiate a Secure Copy (SCP) of files to each device from the client instance. Because the software and firmware updates require an elevated level of privilege, you must have the Security Officer (SO) password that you created when you initialized your CloudHSM devices.

Diagram illustrating the initiation of an SSH connection from any one client instance to each CloudHSM device

After you have completed all updates, you can receive enhanced troubleshooting assistance from AWS, if you need it. When new versions of software and firmware are released, AWS performs extensive testing to ensure your smooth transition from version to version.

Detailed guidance for updating your client instance, CloudHSM software, and CloudHSM firmware

1.  Updating your client instance

Let’s start by updating your client instances. My client instance and CloudHSM devices are in the eu-west-1 region, but these steps work the same in any AWS region. Because Gemalto offers client instances in both Linux and Windows, I will cover steps to update both. I will start with Linux. Please note that all commands should be run as the “root” user.

Updating the Linux client

  1. SSH from your local network into the client instance. You can do this from Linux or Windows. Typically, you would do this from the directory where you have stored your private SSH key by using a command like the following command in a terminal or PuTTY This initiates the SSH connection by pointing to the path of your SSH key and denoting the user name and IP address of your client instance. 
    ssh –i /Users/Bob/Keys/CloudHSM_SSH_Key.pem [email protected]

  1. After the SSH connection is established, you must stop all applications and services on the instance that are using the CloudHSM device. This is required because you are removing old software and installing new software in its place. After you have stopped all applications and services, you can move on to remove the existing version of the client software. 
    /usr/safenet/lunaclient/bin/uninstall.sh

This command will remove the old client software, but will not remove your configuration file or certificates. These will be saved in the Chrystoki.conf file of your /etc directory and your usr/safenet/lunaclient/cert directory. Do not delete these files because you will lose the configuration of your CloudHSM devices and client connections.

  1. Download the new client software package: cloudhsm-safenet-client. Double-click it to extract the archive. 
    SafeNet-Luna-client-5-4-9/linux/64/install.sh

    Make sure you choose the Luna SA option when presented with it. Because the directory where your certificates are installed is the same, you do not need to copy these certificates to another directory. You do, however, need to ensure that the Chrystoki.conf file, located at /etc/Chrystoki.conf, has the same path and name for the certificates as when you created them. (The path or names should not have changed, but you should still verify they are same as before the update.)

  1. Check to ensure that the PATH environment variable points to the directory, /usr/safenet/lunaclient/bin, to ensure no issues when you restart applications and services. The update process for your Linux client Instance is now complete.

Updating the Windows client

Use the following steps to update your Windows client instances:

  1. Use Remote Desktop Protocol (RDP) from your local network into the client instance. You can accomplish this with the RDP application of your choice.
  2. After you establish the RDP connection, stop all applications and services on the instance that are using the CloudHSM device. This is required because you will remove old software and install new software in its place or overwrite If your client software version is older than 5.4.1, you need to completely remove it and all patches by using Programs and Features in the Windows Control Panel. If your client software version is 5.4.1 or newer, proceed without removing the software. Your configuration file will remain intact in the crystoki.ini file of your C:\Program Files\SafeNet\Lunaclient\ directory. All certificates are preserved in the C:\Program Files\SafeNet\Lunaclient\cert\ directory. Again, do not delete these files, or you will lose all configuration and client connection data.
  3. After you have completed these steps, download the new client software: cloudhsm-safenet-client. Extract the archive from the downloaded file, and launch the SafeNet-Luna-client-5-4-9\win\64\Lunaclient.msi Choose the Luna SA option when it is presented to you. Because the directory where your certificates are installed is the same, you do not need to copy these certificates to another directory. You do, however, need to ensure that the crystoki.ini file, which is located at C:\Program Files\SafeNet\Lunaclient\crystoki.ini, has the same path and name for the certificates as when you created them. (The path and names should not have changed, but you should still verify they are same as before the update.)
  4. Make one last check to ensure the PATH environment variable points to the directory C:\Program Files\SafeNet\Lunaclient\ to help ensure no issues when you restart applications and services. The update process for your Windows client instance is now complete.

2.  Updating your CloudHSM software

Now that your clients are up to date with the most current software version, it’s time to move on to your CloudHSM devices. A few important notes:

  • Back up your data to a Luna SA Backup device. AWS does not sell or support the Luna SA Backup devices, but you can purchase them from Gemalto. We do, however, offer the steps to back up your data to a Luna SA Backup device. Do not update your CloudHSM devices without backing up your data first.
  • If the names of your clients used for Network Trust Link Service (NTLS) connections has a capital “T” as the eighth character, the client will not work after this update. This is because of a Gemalto naming convention. Before upgrading, ensure you modify your client names accordingly. The NTLS connection uses a two-way digital certificate authentication and SSL data encryption to protect sensitive data transmitted between your CloudHSM device and the client Instances.
  • The syslog configuration for the CloudHSM devices will be lost. After the update is complete, notify AWS Support and we will update the configuration for you.

Now on to updating the software versions. There are actually three different update paths to follow, and I will cover each. Depending on the current software versions on your CloudHSM devices, you might need to follow all three or just one.

Updating the software from version 5.1.x to 5.1.5

If you are running any version of the software older than 5.1.5, you must first update to version 5.1.5 before proceeding. To update to version 5.1.5:

  1. Stop all applications and services that access the CloudHSM device.
  2. Download the Luna SA software update package.
  3. Extract all files from the archive.
  4. Run the following command from your client instance to copy the lunasa_update-5.1.5-2.spkg file to the CloudHSM device. 
    $ scp –I <private_key_file> lunasa_update-5.1.5-2.spkg [email protected]<hsm_ip_address>:

    <private_key_file> is the private portion of your SSH key pair and <hsm_ip_address> is the IP address of your CloudHSM elastic network interface (ENI). The ENI is the network endpoint that permits access to your CloudHSM device. The IP address was supplied to you when the CloudHSM device was provisioned.

  1. Use the following command to connect to your CloudHSM device and log in with your Security Officer (SO) password. 
    $ ssh –I <private_key_file> [email protected]<hsm_ip_address>

lunash:> hsm login

  1. Run the following commands to verify and then install the updated Luna SA software package. 
    lunash:> package verify lunasa_update-5.1.5-2.spkg –authcode <auth_code>

lunash:> package update lunasa_update-5.1.5-2.spkg –authcode <auth_code>

    The value you will use for <auth_code> is contained in the lunasa_update-5.1.5-2.auth file found in the 630-010165-018_reva.tar archive you downloaded in Step 2.

  1. Reboot the CloudHSM device by running the following command. 
    lunash:> sysconf appliance reboot

When all the steps in this section are completed, you will have updated your CloudHSM software to version 5.1.5. You can now move on to update to version 5.3.10.

Updating the software to version 5.3.10

You can update to version 5.3.10 only if you are currently running version 5.1.5. To update to version 5.3.10:

  1. Stop all applications and services that access the CloudHSM device.
  2. Download the v 5.3.10 Luna SA software update package.
  3. Extract all files from the archive.
  4. Run the following command to copy the lunasa_update-5.3.10-7.spkg file to the CloudHSM device. 
    $ scp –i <private_key_file> lunasa_update-5.3.10-7.spkg [email protected]<hsm_ip_address>:

    <private_key_file> is the private portion of your SSH key pair and <hsm_ip_address> is the IP address of your CloudHSM ENI.

  1. Run the following command to connect to your CloudHSM device and log in with your SO password. 
    $ ssh –i <private_key_file> [email protected]<hsm_ip_address>

lunash:> hsm login

  1. Run the following commands to verify and then install the updated Luna SA software package. 
    lunash:> package verify lunasa_update-5.3.10-7.spkg –authcode <auth_code>

lunash:> package update lunasa_update-5.3.10-7.spkg –authcode <auth_code>

The value you will use for <auth_code> is contained in the lunasa_update-5.3.10-7.auth file found in the SafeNet-Luna-SA-5-3-10.zip archive you downloaded in Step 2.

  1. Reboot the CloudHSM device by running the following command. 
    lunash:> sysconf appliance reboot

When all the steps in this section are completed, you will have updated your CloudHSM software to version 5.3.10. You can now move on to update to version 5.3.13.

Note: Do not configure your applog settings at this point; you must first update the software to version 5.3.13 in the following step.

Updating the software to version 5.3.13

You can update to version 5.3.13 only if you are currently running version 5.3.10. If you are not already running version 5.3.10, follow the two update paths mentioned previously in this section.

To update to version 5.3.13:

  1. Stop all applications and services that access the CloudHSM device.
  2. Download the Luna SA software update package.
  3. Extract all files from the archive.
  4. Run the following command to copy the lunasa_update-5.3.13-1.spkg file to the CloudHSM device. 
    $ scp –i <private_key_file> lunasa_update-5.3.13-1.spkg [email protected]<hsm_ip_address>

<private_key_file> is the private portion of your SSH key pair and <hsm_ip_address> is the IP address of your CloudHSM ENI.

  1. Run the following command to connect to your CloudHSM device and log in with your SO password. 
    $ ssh –i <private_key_file> [email protected]<hsm_ip_address>

lunash:> hsm login

  1. Run the following commands to verify and then install the updated Luna SA software package. 
    lunash:> package verify lunasa_update-5.3.13-1.spkg –authcode <auth_code>

lunash:> package update lunasa_update-5.3.13-1.spkg –authcode <auth_code>

The value you will use for <auth_code> is contained in the lunasa_update-5.3.13-1.auth file found in the SafeNet-Luna-SA-5-3-13.zip archive that you downloaded in Step 2.

  1. When updating to this software version, the option to update the firmware also is offered. If you do not require a version of the firmware validated under FIPS 140-2, accept the firmware update to version 6.20.2. If you do require a version of the firmware validated under FIPS 140-2, do not accept the firmware update and instead update by using the steps in the next section, “Updating your CloudHSM FIPS 140-2 validated firmware.”
  2. After updating the CloudHSM device, reboot it by running the following command. 
    lunash:> sysconf appliance reboot

  1. Disable NTLS IP checking on the CloudHSM device so that it can operate within its VPC. To do this, run the following command. 
    lunash:> ntls ipcheck disable

When all the steps in this section are completed, you will have updated your CloudHSM software to version 5.3.13. If you don’t need the FIPS 140-2 validated firmware, you will have also updated the firmware to version 6.20.2. If you do need the FIPS 140-2 validated firmware, proceed to the next section.

3.  Updating your CloudHSM FIPS 140-2 validated firmware

To update the FIPS 140-2 validated version of the firmware to 6.10.9, use the following steps:

  1. Download version 6.10.9 of the firmware package.
  2. Extract all files from the archive.
  3. Run the following command to copy the 630-010430-010_SPKG_LunaFW_6.10.9.spkg file to the CloudHSM device. 
    $ scp –i <private_key_file> 630-010430-010_SPKG_LunaFW_6.10.9.spkg [email protected]<hsm_ip_address>:

<private_key_file> is the private portion of your SSH key pair, and <hsm_ip_address> is the IP address of your CloudHSM ENI.

  1. Run the following command to connect to your CloudHSM device and log in with your SO password. 
    $ ssh –i <private_key_file> manager#<hsm_ip_address>

lunash:> hsm login

  1. Run the following commands to verify and then install the updated Luna SA firmware package. 
    lunash:> package verify 630-010430-010_SPKG_LunaFW_6.10.9.spkg –authcode <auth_code>

lunash:> package update 630-010430-010_SPKG_LunaFW_6.10.9.spkg –authcode <auth_code>

The value you will use for <auth_code> is contained in the 630-010430-010_SPKG_LunaFW_6.10.9.auth file found in the 630-010430-010_SPKG_LunaFW_6.10.9.zip archive that you downloaded in Step 1.

  1. Run the following command to update the firmware of the CloudHSM devices. 
    lunash:> hsm update firmware

  1. After you have updated the firmware, reboot the CloudHSM devices to complete the installation. 
    lunash:> sysconf appliance reboot

Summary

In this blog post, I walked you through how to update your existing CloudHSM devices and clients so that they are using supported client, software, and firmware versions. Per AWS Support and CloudHSM Terms and Conditions, your devices and clients must use the most current supported software and firmware for continued troubleshooting assistance. Software and firmware versions regularly change based on customer use cases and requirements. Because AWS tests and validates all updates from Gemalto, you must install all updates for firmware and software by using our package links described in this post and elsewhere in our documentation.

If you have comments about this blog post, submit them in the “Comments” section below. If you have questions about implementing this solution, please start a new thread on the CloudHSM forum.

– Tracy