Tag Archives: nist

TLS 1.2 will be required for all AWS FIPS endpoints beginning March 31, 2021

Post Syndicated from Janelle Hopper original https://aws.amazon.com/blogs/security/tls-1-2-required-for-aws-fips-endpoints/

To help you meet your compliance needs, we’re updating all AWS Federal Information Processing Standard (FIPS) endpoints to a minimum of Transport Layer Security (TLS) 1.2. We have already updated over 40 services to require TLS 1.2, removing support for TLS 1.0 and TLS 1.1. Beginning March 31, 2021, if your client application cannot support TLS 1.2, it will result in connection failures. In order to avoid an interruption in service, we encourage you to act now to ensure that you connect to AWS FIPS endpoints at TLS version 1.2. This change does not affect non-FIPS AWS endpoints.

Amazon Web Services (AWS) continues to notify impacted customers directly via their Personal Health Dashboard and email. However, if you’re connecting anonymously to AWS shared resources, such as through a public Amazon Simple Storage Service (Amazon S3) bucket, then you would not have received a notification, as we cannot identify anonymous connections.

Why are you removing TLS 1.0 and TLS 1.1 support from FIPS endpoints?

At AWS, we’re continually expanding the scope of our compliance programs to meet the needs of customers who want to use our services for sensitive and regulated workloads. Compliance programs, including FedRAMP, require a minimum level of TLS 1.2. To help you meet compliance requirements, we’re updating all AWS FIPS endpoints to a minimum of TLS version 1.2 across all AWS Regions. Following this update, you will not be able to use TLS 1.0 and TLS 1.1 for connections to FIPS endpoints.

How can I detect if I am using TLS 1.0 or TLS 1.1?

To detect the use of TLS 1.0 or 1.1, we recommend that you perform code, network, or log analysis. If you are using an AWS Software Developer Kit (AWS SDK) or Command Line Interface (CLI), we have provided hyperlinks to detailed guidance in our previous TLS blog post about how to examine your client application code and properly configure the TLS version used.

When the application source code is unavailable, you can use a network tool, such as TCPDump (Linux) or Wireshark (Linux or Windows), to analyze your network traffic to find the TLS versions you’re using when connecting to AWS endpoints. For a detailed example of using these tools, see the example, below.

If you’re using Amazon S3, you can also use your access logs to view the TLS connection information for these services and identify client connections that are not at TLS 1.2.

What is the most common use of TLS 1.0 or TLS 1.1?

The most common client applications that use TLS 1.0 or 1.1 are Microsoft .NET Framework versions earlier than 4.6.2. If you use the .NET Framework, please confirm you are using version 4.6.2 or later. For information on how to update and configure .NET Framework to support TLS 1.2, see How to enable TLS 1.2 on clients.

How do I know if I am using an AWS FIPS endpoint?

All AWS services offer TLS 1.2 encrypted endpoints that you can use for all API calls. Some AWS services also offer FIPS 140-2 endpoints for customers who need to use FIPS-validated cryptographic libraries to connect to AWS services. You can check our list of all AWS FIPS endpoints and compare the list to your application code, configuration repositories, DNS logs, or other network logs.

EXAMPLE: TLS version detection using a packet capture

To capture the packets, multiple online sources, such as this article, provide guidance for setting up TCPDump on a Linux operating system. On a Windows operating system, the Wireshark tool provides packet analysis capabilities and can be used to analyze packets captured with TCPDump or it can also directly capture packets.

In this example, we assume there is a client application with the local IP address 10.25.35.243 that is making API calls to the CloudWatch FIPS API endpoint in the AWS GovCloud (US-West) Region. To analyze the traffic, first we look up the endpoint URL in the AWS FIPS endpoint list. In our example, the endpoint URL is monitoring.us-gov-west-1.amazonaws.com. Then we use NSLookup to find the IP addresses used by this FIPS endpoint.

Figure 1: Use NSLookup to find the IP addresses used by this FIPS endpoint

Figure 1: Use NSLookup to find the IP addresses used by this FIPS endpoint

Wireshark is then used to open the captured packets, and filter to just the packets with the relevant IP address. This can be done automatically by selecting one of the packets in the upper section, and then right-clicking to use the Conversation filter/IPv4 option.

After the results are filtered to only the relevant IP addresses, the next step is to find the packet whose description in the Info column is Client Hello. In the lower packet details area, expand the Transport Layer Security section to find the version, which in this example is set to TLS 1.0 (0x0301). This indicates that the client only supports TLS 1.0 and must be modified to support a TLS 1.2 connection.

Figure 2: After the conversation filter has been applied, select the Client Hello packet in the top pane. Expand the Transport Layer Security section in the lower pane to view the packet details and the TLS version.

Figure 2: After the conversation filter has been applied, select the Client Hello packet in the top pane. Expand the Transport Layer Security section in the lower pane to view the packet details and the TLS version.

Figure 3 shows what it looks like after the client has been updated to support TLS 1.2. This second packet capture confirms we are sending TLS 1.2 (0x0303) in the Client Hello packet.

Figure 3: The client TLS has been updated to support TLS 1.2

Figure 3: The client TLS has been updated to support TLS 1.2

Is there more assistance available?

If you have any questions or issues, you can start a new thread on one of the AWS forums, or contact AWS Support or your technical account manager (TAM). The AWS support tiers cover development and production issues for AWS products and services, along with other key stack components. AWS Support doesn’t include code development for client applications.

Additionally, you can use AWS IQ to find, securely collaborate with, and pay AWS-certified third-party experts for on-demand assistance to update your TLS client components. Visit the AWS IQ page for information about how to submit a request, get responses from experts, and choose the expert with the right skills and experience. Log in to your console and select Get Started with AWS IQ to start a request.

If you have feedback about this post, submit comments in the Comments section below.

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Author

Janelle Hopper

Janelle is a Senior Technical Program Manager in AWS Security with over 15 years of experience in the IT security field. She works with AWS services, infrastructure, and administrative teams to identify and drive innovative solutions that improve AWS’ security posture.

Author

Daniel Salzedo

Daniel is a Senior Specialist Technical Account Manager – Security. He has over 25 years of professional experience in IT in industries as diverse as video game development, manufacturing, banking and used car sales. He loves working with our wonderful AWS customers to help them solve their complex security challenges at scale.

Over 40 services require TLS 1.2 minimum for AWS FIPS endpoints

Post Syndicated from Janelle Hopper original https://aws.amazon.com/blogs/security/over-40-services-require-tls-1-2-minimum-for-aws-fips-endpoints/

In a March 2020 blog post, we told you about work Amazon Web Services (AWS) was undertaking to update all of our AWS Federal Information Processing Standard (FIPS) endpoints to a minimum of Transport Layer Security (TLS) 1.2 across all AWS Regions. Today, we’re happy to announce that over 40 services have been updated and now require TLS 1.2:

These services no longer support using TLS 1.0 or TLS 1.1 on their FIPS endpoints. To help you meet your compliance needs, we are updating all AWS FIPS endpoints to a minimum of TLS 1.2 across all Regions. We will continue to update our services to support only TLS 1.2 or later on AWS FIPS endpoints, which you can check on the AWS FIPS webpage. This change doesn’t affect non-FIPS AWS endpoints.

When you make a connection from your client application to an AWS service endpoint, the client provides its TLS minimum and TLS maximum versions. The AWS service endpoint will always select the maximum version offered.

What is TLS?

TLS is a cryptographic protocol designed to provide secure communication across a computer network. API calls to AWS services are secured using TLS.

What is FIPS 140-2?

The FIPS 140-2 is a US and Canadian government standard that specifies the security requirements for cryptographic modules that protect sensitive information.

What are AWS FIPS endpoints?

All AWS services offer TLS 1.2 encrypted endpoints that can be used for all API calls. Some AWS services also offer FIPS 140-2 endpoints for customers who need to use FIPS validated cryptographic libraries to connect to AWS services.

Why are we upgrading to TLS 1.2?

Our upgrade to TLS 1.2 across all Regions reflects our ongoing commitment to help customers meet their compliance needs.

Is there more assistance available to help verify or update client applications?

If you’re using an AWS software development kit (AWS SDK), you can find information about how to properly configure the minimum and maximum TLS versions for your clients in the following AWS SDK topics:

You can also visit Tools to Build on AWS and browse by programming language to find the relevant SDK. AWS Support tiers cover development and production issues for AWS products and services, along with other key stack components. AWS Support doesn’t include code development for client applications.

If you have any questions or issues, you can start a new thread on one of the AWS forums, or contact AWS Support or your technical account manager (TAM).

If you have feedback about this post, submit comments in the Comments section below.

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

Author

Janelle Hopper

Janelle Hopper is a Senior Technical Program Manager in AWS Security with over 15 years of experience in the IT security field. She works with AWS services, infrastructure, and administrative teams to identify and drive innovative solutions that improve AWS’ security posture.

Author

Marta Taggart

Marta is a Seattle-native and Senior Program Manager in AWS Security, where she focuses on privacy, content development, and educational programs. Her interest in education stems from two years she spent in the education sector while serving in the Peace Corps in Romania. In her free time, she’s on a global hunt for the perfect cup of coffee.

Round 2 post-quantum TLS is now supported in AWS KMS

Post Syndicated from Alex Weibel original https://aws.amazon.com/blogs/security/round-2-post-quantum-tls-is-now-supported-in-aws-kms/

AWS Key Management Service (AWS KMS) now supports three new hybrid post-quantum key exchange algorithms for the Transport Layer Security (TLS) 1.2 encryption protocol that’s used when connecting to AWS KMS API endpoints. These new hybrid post-quantum algorithms combine the proven security of a classical key exchange with the potential quantum-safe properties of new post-quantum key exchanges undergoing evaluation for standardization. The fastest of these algorithms adds approximately 0.3 milliseconds of overheard compared to a classical TLS handshake. The new post-quantum key exchange algorithms added are Round 2 versions of Kyber, Bit Flipping Key Encapsulation (BIKE), and Supersingular Isogeny Key Encapsulation (SIKE). Each organization has submitted their algorithms to the National Institute of Standards and Technology (NIST) as part of NIST’s post-quantum cryptography standardization process. This process spans several rounds of evaluation over multiple years, and is likely to continue beyond 2021.

In our previous hybrid post-quantum TLS blog post, we announced that AWS KMS had launched hybrid post-quantum TLS 1.2 with Round 1 versions of BIKE and SIKE. The Round 1 post-quantum algorithms are still supported by AWS KMS, but at a lower priority than the Round 2 algorithms. You can choose to upgrade your client to enable negotiation of Round 2 algorithms.

Why post-quantum TLS is important

A large-scale quantum computer would be able to break the current public-key cryptography that’s used for key exchange in classical TLS connections. While a large-scale quantum computer isn’t available today, it’s still important to think about and plan for your long-term security needs. TLS traffic using classical algorithms recorded today could be decrypted by a large-scale quantum computer in the future. If you’re developing applications that rely on the long-term confidentiality of data passed over a TLS connection, you should consider a plan to migrate to post-quantum cryptography before the lifespan of the sensitivity of your data would be susceptible to an unauthorized user with a large-scale quantum computer. As an example, this means that if you believe that a large-scale quantum computer is 25 years away, and your data must be secure for 20 years, you should migrate to post-quantum schemes within the next 5 years. AWS is working to prepare for this future, and we want you to be prepared too.

We’re offering this feature now instead of waiting for standardization efforts to be complete so you have a way to measure the potential performance impact to your applications. Offering this feature now also gives you the protection afforded by the proposed post-quantum schemes today. While we believe that the use of this feature raises the already high security bar for connecting to AWS KMS endpoints, these new cipher suites will impact bandwidth utilization and latency. However, using these new algorithms could also create connection failures for intermediate systems that proxy TLS connections. We’d like to get feedback from you on the effectiveness of our implementation or any issues found so we can improve it over time.

Hybrid post-quantum TLS 1.2

Hybrid post-quantum TLS is a feature that provides the security protections of both the classical and post-quantum key exchange algorithms in a single TLS handshake. Figure 1 shows the differences in the connection secret derivation process between classical and hybrid post-quantum TLS 1.2. Hybrid post-quantum TLS 1.2 has three major differences from classical TLS 1.2:

  • The negotiated post-quantum key is appended to the ECDHE key before being used as the hash-based message authentication code (HMAC) key.
  • The text hybrid in its ASCII representation is prepended to the beginning of the HMAC message.
  • The entire client key exchange message from the TLS handshake is appended to the end of the HMAC message.
Figure 1: Differences in the connection secret derivation process between classical and hybrid post-quantum TLS 1.2

Figure 1: Differences in the connection secret derivation process between classical and hybrid post-quantum TLS 1.2

Some background on post-quantum TLS

Today, all requests to AWS KMS use TLS with key exchange algorithms that provide perfect forward secrecy and use one of the following classical schemes:

While existing FFDHE and ECDHE schemes use perfect forward secrecy to protect against the compromise of the server’s long-term secret key, these schemes don’t protect against large-scale quantum computers. In the future, a sufficiently capable large-scale quantum computer could run Shor’s Algorithm to recover the TLS session key of a recorded classical session, and thereby gain access to the data inside. Using a post-quantum key exchange algorithm during the TLS handshake protects against attacks from a large-scale quantum computer.

The possibility of large-scale quantum computing has spurred the development of new quantum-resistant cryptographic algorithms. NIST has started the process of standardizing post-quantum key encapsulation mechanisms (KEMs). A KEM is a type of key exchange that’s used to establish a shared symmetric key. AWS has chosen three NIST KEM submissions to adopt in our post-quantum efforts:

Hybrid mode ensures that the negotiated key is as strong as the weakest key agreement scheme. If one of the schemes is broken, the communications remain confidential. The Internet Engineering Task Force (IETF) Hybrid Post-Quantum Key Encapsulation Methods for Transport Layer Security 1.2 draft describes how to combine post-quantum KEMs with ECDHE to create new cipher suites for TLS 1.2.

These cipher suites use a hybrid key exchange that performs two independent key exchanges during the TLS handshake. The key exchange then cryptographically combines the keys from each into a single TLS session key. This strategy combines the proven security of a classical key exchange with the potential quantum-safe properties of new post-quantum key exchanges being analyzed by NIST.

The effect of hybrid post-quantum TLS on performance

Post-quantum cipher suites have a different performance profile and bandwidth usage from traditional cipher suites. AWS has measured bandwidth and latency across 2,000 TLS handshakes between an Amazon Elastic Compute Cloud (Amazon EC2) C5n.4xlarge client and the public AWS KMS endpoint, which were both in the us-west-2 Region. Your own performance characteristics might differ, and will depend on your environment, including your:

  • Hardware–CPU speed and number of cores.
  • Existing workloads–how often you call AWS KMS and what other work your application performs.
  • Network–location and capacity.

The following graphs and table show latency measurements performed by AWS for all newly supported Round 2 post-quantum algorithms, in addition to the classical ECDHE key exchange algorithm currently used by most customers.

Figure 2 shows the latency differences of all hybrid post-quantum algorithms compared with classical ECDHE alone, and shows that compared to ECDHE alone, SIKE adds approximately 101 milliseconds of overhead, BIKE adds approximately 9.5 milliseconds of overhead, and Kyber adds approximately 0.3 milliseconds of overhead.
 

Figure 2: TLS handshake latency at varying percentiles for four key exchange algorithms

Figure 2: TLS handshake latency at varying percentiles for four key exchange algorithms

Figure 3 shows the latency differences between ECDHE with Kyber, and ECDHE alone. The addition of Kyber adds approximately 0.3 milliseconds of overhead.
 

Figure 3: TLS handshake latency at varying percentiles, with only top two performing key exchange algorithms

Figure 3: TLS handshake latency at varying percentiles, with only top two performing key exchange algorithms

The following table shows the total amount of data (in bytes) needed to complete the TLS handshake for each cipher suite, the average latency, and latency at varying percentiles. All measurements were gathered from 2,000 TLS handshakes. The time was measured on the client from the start of the handshake until the handshake was completed, and includes all network transfer time. All connections used RSA authentication with a 2048-bit key, and ECDHE used the secp256r1 curve. All hybrid post-quantum tests used the NIST Round 2 versions. The Kyber test used the Kyber-512 parameter, the BIKE test used the BIKE-1 Level 1 parameter, and the SIKE test used the SIKEp434 parameter.

Item Bandwidth
(bytes)
Total
handshakes
Average
(ms)
p0
(ms)
p50
(ms)
p90
(ms)
p99
(ms)
ECDHE (classic) 3,574 2,000 3.08 2.07 3.02 3.95 4.71
ECDHE + Kyber R2 5,898 2,000 3.36 2.38 3.17 4.28 5.35
ECDHE + BIKE R2 12,456 2,000 14.91 11.59 14.16 18.27 23.58
ECDHE + SIKE R2 4,628 2,000 112.40 103.22 108.87 126.80 146.56

By default, the AWS SDK client performs a TLS handshake once to set up a new TLS connection, and then reuses that TLS connection for multiple requests. This means that the increased cost of a hybrid post-quantum TLS handshake is amortized over multiple requests sent over the TLS connection. You should take the amortization into account when evaluating the overall additional cost of using post-quantum algorithms; otherwise performance data could be skewed.

AWS KMS has chosen Kyber Round 2 to be KMS’s highest prioritized post-quantum algorithm, with BIKE Round 2, and SIKE Round 2 next in priority order for post-quantum algorithms. This is because Kyber’s performance is closest to the classical ECDHE performance that most AWS KMS customers are using today and are accustomed to.

How to use hybrid post-quantum cipher suites

To use the post-quantum cipher suites with AWS KMS, you need the preview release of the AWS Common Runtime (CRT) HTTP client for the AWS SDK for Java 2.x. Also, you will need to configure the AWS CRT HTTP client to use the s2n post-quantum hybrid cipher suites. Post-quantum TLS for AWS KMS is available in all AWS Regions except for AWS GovCloud (US-East), AWS GovCloud (US-West), AWS China (Beijing) Region operated by Beijing Sinnet Technology Co. Ltd (“Sinnet”), and AWS China (Ningxia) Region operated by Ningxia Western Cloud Data Technology Co. Ltd. (“NWCD”). Since NIST has not yet standardized post-quantum cryptography, connections that require Federal Information Processing Standards (FIPS) compliance cannot use the hybrid key exchange. For example, kms.<region>.amazonaws.com supports the use of post-quantum cipher suites, while kms-fips.<region>.amazonaws.com does not.

  1. If you’re using the AWS SDK for Java 2.x, you must add the preview release of the AWS Common Runtime client to your Maven dependencies.
    <dependency>
        <groupId>software.amazon.awssdk</groupId>
        <artifactId>aws-crt-client</artifactId>
        <version>2.14.13-PREVIEW</version>
    </dependency>
    

  2. You then must configure the new SDK and cipher suite in the existing initialization code of your application:
    if(!TLS_CIPHER_PREF_KMS_PQ_TLSv1_0_2020_07.isSupported()){
        throw new RuntimeException("Post Quantum Ciphers not supported on this Platform");
    }
    
    SdkAsyncHttpClient awsCrtHttpClient = AwsCrtAsyncHttpClient.builder()
              .tlsCipherPreference(TLS_CIPHER_PREF_KMS_PQ_TLSv1_0_2020_07)
              .build();
              
    KmsAsyncClient kms = KmsAsyncClient.builder()
             .httpClient(awsCrtHttpClient)
             .build();
             
    ListKeysResponse response = kms.listKeys().get();
    

Now, all connections made to AWS KMS in supported Regions will use the new hybrid post-quantum cipher suites! To see a complete example of everything set up, check out the example application here.

Things to try

Here are some ideas about how to use this post-quantum-enabled client:

  • Run load tests and benchmarks. These new cipher suites perform differently than traditional key exchange algorithms. You might need to adjust your connection timeouts to allow for the longer handshake times or, if you’re running inside an AWS Lambda function, extend the execution timeout setting.
  • Try connecting from different locations. Depending on the network path your request takes, you might discover that intermediate hosts, proxies, or firewalls with deep packet inspection (DPI) block the request. This could be due to the new cipher suites in the ClientHello or the larger key exchange messages. If this is the case, you might need to work with your security team or IT administrators to update the relevant configuration to unblock the new TLS cipher suites. We’d like to hear from you about how your infrastructure interacts with this new variant of TLS traffic. If you have questions or feedback, please start a new thread on the AWS KMS discussion forum.

Conclusion

In this blog post, I announced support for Round 2 hybrid post-quantum algorithms in AWS KMS, and showed you how to begin experimenting with hybrid post-quantum key exchange algorithms for TLS when connecting to AWS KMS endpoints.

More info

If you’d like to learn more about post-quantum cryptography check out:

If you have feedback about this post, submit comments in the Comments section below.

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

Author

Alex Weibel

Alex is a Senior Software Engineer on the AWS Crypto Algorithms team. He’s one of the maintainers for Amazon’s TLS Library s2n. Previously, Alex worked on TLS termination and request proxying for S3 and the Elastic Load Balancing Service developing new features for customers. Alex holds a Bachelor of Science degree in Computer Science from the University of Texas at Austin.

More on NIST’s Post-Quantum Cryptography

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2020/09/more_on_nists_p.html

Back in July, NIST selected third-round algorithms for its post-quantum cryptography standard.

Recently, Daniel Apon of NIST gave a talk detailing the selection criteria. Interesting stuff.

NOTE: We’re in the process of moving this blog to WordPress. Comments will be disabled until the move it complete. The management thanks you for your cooperation and support.

More on NIST’s Post-Quantum Cryptography

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2020/09/more_on_nists_p.html

Back in July, NIST selected third-round algorithms for its post-quantum cryptography standard.

Recently, Daniel Apon of NIST gave a talk detailing the selection criteria. Interesting stuff.

NOTE: We’re in the process of moving this blog to WordPress. Comments will be disabled until the move is complete. The management thanks you for your cooperation and support.

Privacy conscious cloud migrations: mapping the AWS Cloud Adoption Framework to the NIST Privacy Framework

Post Syndicated from Mark Becker original https://aws.amazon.com/blogs/security/privacy-conscious-cloud-migrations-mapping-aws-cloud-adoption-framework-to-nist-privacy-framework/

This post will help you make privacy-conscious cloud migration decisions by mapping the National Institute of Standards and Technology (NIST) Privacy Framework: A Tool for Improving Privacy Through Enterprise Risk Management (NIST Privacy Framework) to the AWS Cloud Adoption Framework (AWS CAF).

AWS Professional Services created the AWS CAF to help organizations successfully migrate to the cloud. The CAF’s guidance and best practices provide a comprehensive approach to cloud computing across your organization. For those already in the cloud, AWS offers our recently updated AWS Well-Architected Framework (AWS WAF), which provides a way for you to consistently measure your cloud architectures against best practices and identify areas for improvement. A forthcoming AWS blog will highlight how the AWS CAF, AWS WAF, and NIST’s globally-recognized Cybersecurity Framework (NIST CSF) are complementary tools in building a cloud security program. For example, the Well-Architected Security pillar is comprised of five best practices (Identity and Access Management, Detection, Infrastructure Protection, Data Protection, and Incident Response) that may also be adopted to address the management of your privacy risks. You can also use the AWS Well-Architected Tool in the AWS Console to review the state of your workloads. The tool will then provide a plan on how to architect for the cloud using established best practices.

While you have an opportunity to raise the security bar when moving your organization to the cloud, you also need to consider how best to protect privacy in the cloud. Depending on your organization’s cloud maturity, cloud adoption might require fundamental changes across your organization. These possible changes are detailed in An Overview of the AWS Cloud Adoption Framework. The AWS CAF helps you create an actionable, enterprise-wide cloud migration plan for your organization. Similarly, the NIST Privacy Framework is a voluntary and customizable tool that encourages cross-organizational coordination in managing privacy risks by creating equivalence between privacy risks and other risks within your organization. The NIST Privacy Framework, used in conjunction with the AWS CAF, should make it easier for you to move your privacy practices to the cloud.

In particular, the NIST Privacy Framework—which is agnostic to law and technology—helps you manage your organization’s privacy risks by:

  1. Considering privacy when designing and deploying systems, products, and services;
  2. Communicating your privacy practices within your organization and to your external stakeholders; and
  3. Encouraging enterprise-wide collaboration.

The following is a high-level overview of the two frameworks and a table mapping their similar attributes to aid you in your journey.

A familiar structure

The NIST Privacy Framework is modeled after NIST’s CSF, first released in 2014, so the two frameworks can be used in tandem when managing cybersecurity and privacy risks in preparation for your cloud migration journey. Similar to the NIST CSF, the three primary components of the NIST Privacy Framework are the Core, Profile, and Implementation Tiers. The NIST Privacy Framework Core, which is different from the NIST CSF Core, contains five functions each designated by a P to distinguish it from CSF functions.

  • Identify-P: Develop the organizational understanding to manage privacy risk for individuals arising from data processing.
  • Govern-P: Develop and implement the organizational governance structure to enable an ongoing understanding of the organization’s risk management priorities that are informed by privacy risk.
  • Control-P: Develop and implement appropriate activities to enable organizations or individuals to manage data with sufficient granularity to manage privacy risks.
  • Communicate-P: Develop and implement appropriate activities to enable organizations and individuals to have a reliable understanding and engage in a dialogue about how data are processed and associated privacy risks.
  • Protect-P: Develop and implement appropriate data processing safeguards.

Note: You can learn more about NIST CSF and AWS by reading AWS’s NIST Cybersecurity Framework (CSF), Aligning to the NIST CSF in the AWS Cloud.

AWS Cloud Adoption Framework

Using the AWS CAF in tandem with the NIST Privacy Framework will help your organization make better privacy-conscious decisions about how to manage data in the cloud during migration. Both frameworks encourage you to evaluate the current state, identify a target state, and then make changes to support your privacy risk management program as you begin or complete your cloud migration. Similar to the five functions of the NIST Privacy Framework, AWS CAF is divided into six business and technical focus areas or perspectives.

AWS CAF business perspectives

  1. Business perspective: Helps you move from separate strategies for business and IT to a business model that integrates IT strategy.
  2. Governance perspective: Provides guidance on identifying and implementing best practices for IT governance, and on supporting business processes with technology.
  3. People perspective: Assists human resources (HR) and personnel management prepare their teams for cloud adoption by updating staff skills and organizational processes to include cloud-based competencies.

AWS CAF technical perspectives

  1. Platform perspective: Helps you design, implement, and optimize the architecture of AWS technology based on business goals and objectives.
  2. Operations perspective: Helps you to run, use, operate, and recover IT workloads to levels that meet the requirements of your business stakeholders.
  3. Security perspective: Helps you structure the selection and implementation of controls.

Aligning the NIST Privacy Framework to the AWS Cloud Adoption Framework

The following tables map the five functions of the NIST Privacy Framework and their categories, to the six perspectives of AWS CAF and their capabilities. We encourage all organizations moving to the cloud to establish a privacy risk management strategy that supports your business objectives. Your approach may be based on the NIST Privacy Framework, or another framework. You might even choose to create your own approach that combines attributes from different frameworks and standards, if that best serves your data protection and privacy needs.

NIST Identify-P categories and AWS CAF Business perspective capabilities

NIST Privacy Framework AWS CAF
Inventory and mapping (ID.IM-P)
Data processing by systems, products, or services is understood and informs the management of privacy risks.Business environment (ID.BE-P)
The organization’s mission, objectives, stakeholders, and activities are understood and prioritized. This information is used to inform privacy roles, responsibilities, and risk management decisions.Risk assessment (ID.RA-P)
The organization understands the privacy risks to individuals and how such privacy risks may create follow-on impacts on organizational operations, including mission, functions, other risk management priorities (e.g., compliance, financial), reputation, workforce, and culture.

Data processing ecosystem risk management (ID.DE-P)
The organization’s priorities, constraints, risk tolerance, and assumptions are established and used to support risk decisions associated with managing privacy risk and third parties within the data processing ecosystem.

IT finance
Addresses your capacity to plan, allocate, and manage the budget for IT expenses with the use-based cost model of cloud services.IT strategy
Helps you take advantage of cloud-based IT approach to deliver value and end-user adoption.Benefits realization
Assists you to measure the benefits of your IT investments using methods for a cloud-based IT operating model.

Business risk management
Helps you estimate the potential business impact of preventable, strategic, and/or external risks.

NIST Govern-P (GV-P) categories and AWS CAF People perspective capabilities

NIST Privacy Framework AWS CAF
Governance policies, processes, and procedures (GV.PO-P)
The policies, processes, and procedures to manage and monitor the organization’s regulatory, legal, risk, environmental, and operational requirements are understood and inform the management of privacy risk.Risk management strategy (GV.RM-P)
The organization’s priorities, constraints, risk tolerances, and assumptions are established and used to support operational risk decisions.Awareness and training (GV.AT-P)
The organization’s workforce and third parties engaged in data processing are provided privacy awareness education and are trained to perform their privacy-related duties and responsibilities consistent with related policies, processes, procedures, and agreements and organizational privacy values.

Monitoring and review (GV.MT-P)
The policies, processes, and procedures for ongoing review of the organization’s privacy posture are understood and inform the management of privacy risk.

Incentive management
Helps you implement a compensation program that will attract and retain the personnel required to operate a cloud-based IT model.Training management
Provides guidance on how to develop or acquire training for your employees so they can perform their roles in a cloud environment.

NIST Communicate-P (CM-P) categories and AWS CAF People perspective capabilities

NIST Privacy Framework AWS CAF
Communication policies, processes, and procedures (CM.PO-P)
Policies, processes, and procedures are maintained and used to increase transparency of the organization’s data processing practices (e.g., purpose, scope, roles and responsibilities in the data processing ecosystem, and management commitment) and associated privacy risks.Data processing awareness (CM.AW-P)
Individuals and organizations have reliable knowledge about data processing practices and associated privacy risks, and effective mechanisms are used and maintained to increase predictability consistent with the organization’s risk strategy to protect individuals’ privacy.
Resource management
Helps you understand and forecast new personnel needs for a cloud-based model.Career management
Assists you to identify, acquire, and retain the skills needed for your cloud migration and ongoing operating model.Organizational change management
Helps you manage the impact of business, structural, and cultural changes caused by cloud adoption.

NIST Govern-P (GV-P) categories and AWS CAF Governance perspective capabilities

NIST Privacy Framework AWS CAF
Governance policies, processes, and procedures (GV.PO-P)
The policies, processes, and procedures to manage and monitor the organization’s regulatory, legal, risk, environmental, and operational requirements are understood and inform the management of privacy risk.Risk management strategy (GV.RM-P)
The organization’s priorities, constraints, risk tolerances, and assumptions are established and used to support operational risk decisions.Awareness and training (GV.AT-P)
The organization’s workforce and third parties engaged in data processing are provided privacy awareness education and are trained to perform their privacy-related duties and responsibilities consistent with related policies, processes, procedures, and agreements and organizational privacy values.

Monitoring and review (GV.MT-P)
The policies, processes, and procedures for ongoing review of the organization’s privacy posture are understood and inform the management of privacy risk.

Portfolio management
Provides a mechanism to manage it based on desired business outcomes. It can help to determine cloud-eligibility for workloads when prioritizing which services to move to the cloud.Program and project management
Helps you manage technology projects using methodologies that take advantage of the agility and cost management benefits inherent to cloud services.Business performance measurement
Assists you measure the impact of the cloud on business objectives.

License management
Defines methods to procure, distribute, and manage the licenses needed for IT systems, services, and software.

NIST Control-P (CT-P) categories and AWS CAF Platform perspective capabilities

NIST Privacy Framework AWS CAF
Data processing policies, processes, and procedures (CT.PO-P)
Policies, processes, and procedures are maintained and used to manage data processing (e.g., purpose, scope, roles and responsibilities in the data processing ecosystem, and management commitment) consistent with the organization’s risk strategy to protect individuals’ privacy.Data processing management (CT.DM-P)
Data are managed consistent with the organization’s risk strategy to protect individuals’ privacy, increase manageability, and enable the implementation of privacy principles (e.g., individual participation, data quality, data minimization).Disassociated processing (CT.DP-P)
Data processing solutions increase disassociability consistent with the organization’s risk strategy to protect individuals’ privacy and enable implementation of privacy principles (e.g., data minimization).
Systems and solution architecture
Assists you to define and describe the system design and your architectural standards.Compute, network, storage, and database provisioning
Helps you develop new processes for provisioning infrastructure in a cloud environment. Provisioning shifts from an operational focus aligning supply with demand, to an architectural focus aligning services with requirements.Application development
Addresses your ability to support business goals with new or updated applications, and helps implement new skills and processes for software development that take advantage of the agility gained by cloud computing.

NIST Protect-P (PR-P) categories and AWS CAF Security perspective capabilities

NIST Privacy Framework AWS CAF
Data protection, policies, processes, and procedures (PR.PO-P)
Security and privacy policies (e.g., purpose, scope, roles and responsibilities in the data processing ecosystem, and management commitment), processes, and procedures are maintained and used to manage the protection of data.Identity management, authentication, and access control (PR.AC-P)
Access to data and devices is limited to authorized individuals, processes, and devices, and is managed consistent with the assessed risk of unauthorized access.Data security (PR.DS-P)
Data are managed consistent with the organization’s risk strategy to protect individuals’ privacy and maintain data confidentiality, integrity, and availability.

Maintenance (PR.MA-P)
System maintenance and repairs are performed in a way that’s consistent with policies, processes, and procedures.

Protective technology (PR.PT-P)
Technical security solutions are managed to ensure the security and resilience of systems, products, and services and associated data, consistent with related policies, processes, procedures, and agreements.

Identity and access management
Helps you integrate AWS into your identity management lifecycle, and sources of authentication and authorization.Detective control
Provides guidance to help identify potential security incidents within your AWS environment.Infrastructure security
Helps you implement control methodologies necessary to comply with best practices as well as meet industry or regulatory obligations.

Data protection
Helps you to implement appropriate safeguards that protect data in transit and at rest.

Incident response
Assists you define and execute a response to security incidents.

NIST Control-P (CT-P) categories and AWS CAF Operations perspective capabilities

NIST Privacy Framework AWS CAF
Data processing policies, processes, and procedures (CT.PO-P)
Policies, processes, and procedures are maintained and used to manage data processing (e.g., purpose, scope, roles and responsibilities in the data processing ecosystem, and management commitment) consistent with the organization’s risk strategy to protect individuals’ privacy.Data processing management (CT.DM-P)
Data are managed consistent with the organization’s risk strategy to protect individuals’ privacy, increase manageability, and enable the implementation of privacy principles (e.g., individual participation, data quality, data minimization).Disassociated processing (CT.DP-P)
Data processing solutions increase disassociability consistent with the organization’s risk strategy to protect individuals’ privacy and enable implementation of privacy principles (e.g., data minimization).
Service monitoring
Focuses on detecting and responding to IT operations health indicators, to meet your service level agreements and operating level agreements.Application performance monitoring
Provides you with new approaches for monitoring application performance in a cloud environment to ensure that application health meets defined requirements.Resource inventory management
Helps you manage virtual IT assets to provide services that are both high performing and cost efficient.

Release management and change management
Assists your teams adopt software development best practices such as automation and Continuous Integration/Continuous Delivery (CI/CD) techniques, increasing the pace of your innovations.

Reporting and analytics
Helps you monitor the health of cloud assets and provide insights to help you reach the desired level of performance.

Business continuity and disaster recovery (BC/DR)
Helps you implement processes to keep your business running during a catastrophic event.

IT service catalog
Helps you to offer cloud services to the business using a model that can help to improve efficiency of providing IT services as well as the productivity of consuming them.

Conclusion

NIST’s Privacy Framework is a useful companion to the CAF, but whether you choose NIST’s framework or another framework or approach, we recommend having a privacy risk management strategy as you migrate to the cloud.

Learn more about AWS Privacy, Cloud Adoption Framework, and Well-Architected Framework

If you have feedback about this post, submit comments in the Comments section below. If you have questions about this post, contact AWS Support.

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Author

Mark Becker

As the Senior Privacy Lead, Mark works across AWS to provide privacy solutions and guidance to help customers navigate global privacy challenges. Before joining AWS, he worked on privacy and civil liberties issues at the U.S. Department of Homeland Security. Mark is a Certified Information Privacy Professional who has authored book chapters and articles on privacy and telecommunications law.

Update on NIST’s Post-Quantum Cryptography Program

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2020/07/update_on_nists.html

NIST has posted an update on their post-quantum cryptography program:

After spending more than three years examining new approaches to encryption and data protection that could defeat an assault from a quantum computer, the National Institute of Standards and Technology (NIST) has winnowed the 69 submissions it initially received down to a final group of 15. NIST has now begun the third round of public review. This “selection round” will help the agency decide on the small subset of these algorithms that will form the core of the first post-quantum cryptography standard.

[…]

For this third round, the organizers have taken the novel step of dividing the remaining candidate algorithms into two groups they call tracks. The first track contains the seven algorithms that appear to have the most promise.

“We’re calling these seven the finalists,” Moody said. “For the most part, they’re general-purpose algorithms that we think could find wide application and be ready to go after the third round.”

The eight alternate algorithms in the second track are those that either might need more time to mature or are tailored to more specific applications. The review process will continue after the third round ends, and eventually some of these second-track candidates could become part of the standard. Because all of the candidates still in play are essentially survivors from the initial group of submissions from 2016, there will also be future consideration of more recently developed ideas, Moody said.

“The likely outcome is that at the end of this third round, we will standardize one or two algorithms for encryption and key establishment, and one or two others for digital signatures,” he said. “But by the time we are finished, the review process will have been going on for five or six years, and someone may have had a good idea in the interim. So we’ll find a way to look at newer approaches too.”

Details are here. This is all excellent work, and exemplifies NIST at its best. The quantum-resistant algorithms will be standardized far in advance of any practical quantum computer, which is how we all want this sort of thing to go.

Round 2 Hybrid Post-Quantum TLS Benchmarks

Post Syndicated from Alex Weibel original https://aws.amazon.com/blogs/security/round-2-hybrid-post-quantum-tls-benchmarks/

AWS Cryptography has completed benchmarks of Round 2 Versions of the Bit Flipping Key Encapsulation (BIKE) and Supersingular Isogeny Key Encapsulation (SIKE) hybrid post-quantum Transport Layer Security (TLS) Algorithms. Both of these algorithms have been submitted to the National Institute of Standards and Technology (NIST) as part of NIST’s Post-Quantum Cryptography standardization process.

In the first hybrid post-quantum TLS blog, we announced that AWS Key Management Service (KMS) had launched support for hybrid post-quantum TLS 1.2 using Round 1 versions of BIKE and SIKE. In this blog, we are announcing AWS Cryptography’s benchmark results of using Round 2 versions of BIKE and SIKE with hybrid post-quantum TLS 1.2 against an HTTP webservice. Round 2 versions of BIKE and SIKE include performance improvements, parameter tuning, and algorithm updates in response to NIST’s comments on Round 1 versions. I’ll give a refresher on hybrid post-quantum TLS 1.2, go over our Round 2 hybrid post-quantum TLS 1.2 benchmark results, and then describe our benchmarking methodology.

This blog post is intended to inform software developers, AWS customers, and cryptographic researchers about the potential upcoming performance differences between classical and hybrid post-quantum TLS.

Refresher on Hybrid Post-Quantum TLS 1.2

Some of this section is repeated from the previous hybrid post-quantum TLS 1.2 launch announcement for KMS. If you are already familiar with hybrid post-quantum TLS, feel free to skip to the Benchmark Results section.

What is Hybrid Post-Quantum TLS 1.2?

Hybrid post-quantum TLS 1.2 is a proposed extension to the TLS 1.2 Protocol implemented by Amazon’s open source TLS library s2n that provides the security protections of both the classical and post-quantum schemes. It does this by performing two independent key exchanges (one classical and one post-quantum), and then cryptographically combining both keys into a single TLS master secret.

Why is Post-Quantum TLS Important?

Hybrid post-quantum TLS allows connections to remain secure even if one of the key exchanges (either classical or post-quantum) performed during the TLS Handshake is compromised in the future. For example, if a sufficiently large-scale quantum computer were to be built, it could break the current classical public-key cryptography that is used for key exchange in every TLS connection today. Encrypted TLS traffic recorded today could be decrypted in the future with a large-scale quantum computer if post-quantum TLS is not used to protect it.

Round 2 Hybrid Post-Quantum TLS Benchmark Results

Figure 2: Latency in relation to HTTP request count for four key exchange algorithms

Figure 2: Latency in relation to HTTP request count for four key exchange algorithms

Key Exchange Algorithm Server PQ Implementation TLS Handshake
+ 1 HTTP Request
TLS Handshake
+ 2 HTTP Requests
TLS Handshake
+ 10 HTTP Requests
TLS Handshake
+ 25 HTTP Requests
ECDHE Only N/A 10.8 ms 15.1 ms 52.6 ms 124.2 ms
ECDHE + BIKE1‑CCA‑L1‑R2 C 19.9 ms 24.4 ms 61.4 ms 133.2 ms
ECDHE + SIKE‑P434‑R2 C 169.6 ms 180.3 ms 219.1 ms 288.1 ms
ECDHE + SIKE‑P434‑R2 x86-64
Assembly
20.1 ms 24.5 ms 62.0 ms 133.3 ms

Table 1 shows the time (in milliseconds) that a client and server in the same region take to complete a TCP Handshake, a TLS Handshake, and complete varying numbers of HTTP Requests sent to an HTTP web service running on an i3en.12xlarge host.

Key Exchange Algorithm Client Hello Server Key Exchange Client Key Exchange Other TLS Handshake Total
ECDHE Only 218 338 75 2430 3061
ECDHE + BIKE1‑CCA‑L1‑R2 220 3288 3023 2430 8961
ECDHE + SIKE‑P434‑R2 214 672 423 2430 3739

Table 2 shows the amount of data (in bytes) used by different messages in the TLS Handshake for each Key Exchange algorithm.

1 HTTP
Request
2 HTTP Requests 10 HTTP
Requests
25 HTTP Requests
HTTP Request Bytes 878 1,761 8,825 22,070
HTTP Response Bytes 698 1,377 6,809 16,994
Total HTTP Bytes 1576 3,138 15,634 39,064

Table 3 shows the amount of data (in bytes) sent and received through each TLS connection for varying numbers of HTTP requests.

Benchmark Results Analysis

In general, we find that the major trade off between BIKE and SIKE is data usage versus processing time, with BIKE needing to send more bytes but requiring less time processing them, and SIKE making the opposite trade off of needing to send fewer bytes but requiring more time processing them. At the time of integration for our benchmarks, an x86-64 assembly optimized implementation of BIKE1-CCA-L1-R2 was not available in s2n.

Our results show that when only a single HTTP request is sent, completing a BIKE1-CCA-L1-R2 hybrid TLS 1.2 handshake takes approximately 84% more time compared to a non-hybrid TLS connection, and completing an x86-64 assembly optimized SIKE-P434-R2 hybrid TLS 1.2 handshake takes approximately 86% more time than non-hybrid. However, at 25 HTTP Requests per TLS connection, when using the fastest available implementation for both BIKE and SIKE, the increased TLS Handshake latency is amortized, and only 7% more total time is needed for both BIKE and SIKE compared to a classical TLS connection.

Our results also show that BIKE1-CCA-L1-R2 hybrid TLS Handshakes used 5900 more bytes than a classical TLS Handshake, while SIKE-P434-R2 hybrid TLS Handshakes used 678 more bytes than classical TLS.

In the AWS EC2 network, using modern x86-64 CPU’s with the fastest available algorithm implementations, we found that BIKE and SIKE performed similarly, with their maximum latency difference being only 0.6 milliseconds apart, and BIKE being the faster of the two in every benchmark. However when compared to SIKE’s C implementation, which would be used on hosts without the ADX and MULX x86-64 instructions used by SIKE’s assembly implementation, BIKE performed significantly better, seeing a maximum improvement of 157 milliseconds over SIKE.

Hybrid Post-Quantum TLS Benchmark Details and Methodology

Hybrid Post-Quantum TLS Client

Figure 3: Architecture diagram of the AWS SDK Java Client using Java Native Interface (JNI) to communicate with the native AWS Common Runtime (CRT)

Figure 3: Architecture diagram of the AWS SDK Java Client using Java Native Interface (JNI) to communicate with the native AWS Common Runtime (CRT)

Our post-quantum TLS Client is using the aws-crt-dev-preview branch of the AWS SDK Java v2 Client, that has Java Native Interface Bindings to the AWS Common Runtime (AWS CRT) written in C. The AWS Common Runtime uses s2n for TLS negotiation on Linux platforms.

Our client was a single EC2 i3en.6xlarge host, using v0.5.1 of the AWS Common Runtime (AWS CRT) Java Bindings, with commit f3abfaba of s2n and used the x86-64 Assembly implementation for all SIKE-P434-R2 benchmarks.

Hybrid Post-Quantum TLS Server

Our server was a single EC2 i3en.12xlarge host running a REST-ful HTTP web service which used s2n to terminate TLS connections. In order to measure the latency of the SIKE-P434-R2 C implementation on these hosts, we used an s2n compile time flag to build a 2nd version of s2n with SIKE’s x86-64 assembly optimization disabled, and reran our benchmarks with that version.

We chose i3en.12xlarge as our host type because it is optimized for high IO usage, provides high levels of network bandwidth, has a high number of vCPU’s that is typical for many web service endpoints, and has a modern x86-64 CPU with the ADX and MULX instructions necessary to use the high performance Round 2 SIKE x86-64 assembly implementation. Additional TLS Handshake benchmarks performed on other modern types of EC2 hosts, such as the C5 family and M5 family of EC2 instances, also showed similar latency results to those generated on i3en family of EC2 instances.

Post-Quantum Algorithm Implementation Details

The implementations of the post-quantum algorithms used in these benchmarks can be found in the pq-crypto directory of the s2n GitHub Repository. Our Round 2 BIKE implementation uses portable optimized C code, and our Round 2 SIKE implementation uses an optimized implementation in x86-64 assembly when available, and falls back to a portable optimized C implementation otherwise.

Key Exchange Algorithm s2n Client Cipher Preference s2n Server Cipher Preference Negotiated Cipher
ECDHE Only ELBSecurityPolicy-TLS-1-1-2017-01 KMS-PQ-TLS-1-0-2020-02 ECDHE-RSA-AES256-GCM-SHA384
ECDHE + BIKE1‑CCA‑L1‑R2 KMS-PQ-TLS-1-0-2020-02 KMS-PQ-TLS-1-0-2020-02 ECDHE-BIKE-RSA-AES256-GCM-SHA384
ECDHE + SIKE‑P434‑R2 PQ-SIKE-TEST-TLS-1-0-2020-02 KMS-PQ-TLS-1-0-2020-02 ECDHE-SIKE-RSA-AES256-GCM-SHA384

Table 4 shows the Clients and Servers TLS Cipher Config name used in order to negotiate each Key Exchange Algorithm.

Hybrid Post-Quantum TLS Benchmark Methodology

Figure 4: Benchmarking Methodology Client/Server Architecture Diagram

Figure 4: Benchmarking Methodology Client/Server Architecture Diagram

Our Benchmarks were run with a single client host connecting to a single host running a HTTP web service in a different availability zone within the same AWS Region (us-east-1), through a TCP Load Balancer.

We chose to include varying numbers of HTTP requests in our latency benchmarks, rather than TLS Handshakes alone, because customers are unlikely to establish a secure TLS connection and let the connection sit idle performing no work. Customers use TLS connections in order to send and receive data securely, and HTTP web services are one of the most common types of data being secured by TLS. We also chose to place our EC2 server behind a TCP Load Balancer to more closely approximate how an HTTP web service would be deployed in a typical setup.

Latency was measured at the client in Java starting from before a TCP connection was established, until after the final HTTP Response was received, and includes all network transfer time. All connections used RSA Certificate Authentication with a 2048-bit key, and ECDHE Key Exchange used the secp256r1 curve. All latency values listed in Tables 1 above were calculated from the median value (50th percentile) from 60 minutes of continuous single-threaded measurements between the EC2 Client and Server.

More Info

If you’re interested to learn more about post-quantum cryptography check out the following links:

Conclusion

In this blog post, I gave a refresher on hybrid post-quantum TLS, I went over our hybrid post-quantum TLS 1.2 benchmark results, and went over our hybrid post-quantum benchmarking methodology. Our benchmark results found that BIKE and SIKE performed similarly when using s2n’s fastest available implementation on modern CPU’s, but that BIKE performed better than SIKE when both were using their generic C implementation.

If you have feedback about this blog post, submit comments in the Comments section below.

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Author

Alex Weibel

Alex is a Senior Software Development Engineer on the AWS Crypto Algorithms team. He’s one of the maintainers for Amazon’s TLS Library s2n. Previously, Alex worked on TLS termination and request proxying for S3 and the Elastic Load Balancing Service developing new features for customers. Alex holds a Bachelor of Science degree in Computer Science from the University of Texas at Austin.

Calculating the Benefits of the Advanced Encryption Standard

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2019/10/calculating_the.html

NIST has completed a study — it was published last year, but I just saw it recently — calculating the costs and benefits of the Advanced Encryption Standard.

From the conclusion:

The result of performing that operation on the series of cumulated benefits extrapolated for the 169 survey respondents finds that present value of benefits from today’s perspective is approximately $8.9 billion. On the other hand, the present value of NIST’s costs from today’s perspective is $127 million. Thus, the NPV from today’s perspective is $8,772,000,000; the B/C ratio is therefore 70.2/1; and a measure (explained in detail in Section 6.1) of the IRR for the alternative investment perspective is 31%; all are indicators of a substantial economic impact.

Extending the approach of looking back from 2017 to the larger national economy required the selection of economic sectors best represented by the 169 survey respondents. The economic sectors represented by ten or more survey respondents include the following: agriculture; construction; manufacturing; retail trade; transportation and warehousing; information; real estate rental and leasing; professional, scientific, and technical services; management services; waste management; educational services; and arts and entertainment. Looking at the present value of benefits and costs from 2017’s perspective for these economic sectors finds that the present value of benefits rises to approximately $251 billion while the present value of NIST’s costs from today’s perspective remains the same at $127 million. Therefore, the NPV of the benefits of the AES program to the national economy from today’s perspective is $250,473,200,000; the B/C ratio is roughly 1976/1; and the appropriate, alternative (explained in Section 6.1) IRR and investing proceeds at the social rate of return is 53.6%.

The report contains lots of facts and figures relevant to crypto policy debates, including the chaotic nature of crypto markets in the mid-1990s, the number of approved devices and libraries of various kinds since then, other standards that invoke AES, and so on.

There’s a lot to argue with about the methodology and the assumptions. I don’t know if I buy that the benefits of AES to the economy are in the billions of dollars, mostly because we in the cryptographic community would have come up with alternative algorithms to triple-DES that would have been accepted and used. Still, I like seeing this kind of analysis about security infrastructure. Security is an enabling technology; it doesn’t do anything by itself, but instead allows all sorts of things to be done. And I certainly agree that the benefits of a standardized encryption algorithm that we all trust and use outweigh the cost by orders of magnitude.

And this isn’t the first time NIST has conducted economic impact studies. It released a study of the economic impact of DES in 2001.

Updated whitepaper now available: Aligning to the NIST Cybersecurity Framework in the AWS Cloud

Post Syndicated from Min Hyun original https://aws.amazon.com/blogs/security/updated-whitepaper-now-available-aligning-to-the-nist-cybersecurity-framework-in-the-aws-cloud/

I’m proud to announce an updated resource that is designed to provide guidance to help your organization align to the National Institute of Standards and Technology (NIST) Cybersecurity Framework Version 1.1, which was released in 2018. The updated guide, NIST Cybersecurity Framework (CSF): Aligning to the NIST CSF in the AWS Cloud, is designed to help commercial and public sector entities of any size and in any part of the world align with the CSF by leveraging AWS services and resources.

In addition to mapping CSF updates to the latest AWS services and resources, we’ve also renewed our independent third-party assessor’s validation that the AWS services that have undergone FedRAMP Moderate and ISO 9001/27001/27017/27018 accreditations align with the CSF.

If you’re new to the NIST CSF, it’s a voluntary, risk-based, outcome-focused framework. It helps you establish a foundational set of security activities organized around five functions—Identify, Protect, Detect, Respond, Recover—to help you improve the security, risk management, and resilience of your organization. The CSF was originally intended for the critical infrastructure sector, but they’ve been endorsed by governments and industries worldwide as a recommended baseline for organizations of all types and sizes. Sectors as diverse as health care, financial services, and manufacturing are using the NIST CSF, and the list of early global adopters includes Japan, Israel, the UK, and Uruguay, among others.

In short, the NIST CSF is broadly applicable. In fact, in February 2018, the International Standards Organization released “ISO/IEC 27103:2018 — Information technology — Security techniques,” a standard that provides guidance for implementing a cybersecurity framework leveraging existing standards. ISO 27103 promotes the same concepts and best practices reflected in the NIST CSF; specifically, it encourages a framework focused on security outcomes organized around five functions (Identify, Protect, Detect, Respond, Recover) and foundational activities that map to existing standards, accreditations and frameworks. Adopting a versatile framework like the NIST CSF can help your organization achieve security outcomes while benefiting from the efficiencies of reusing instead of redoing.

You can use our updated whitepaper and workbook to learn how AWS services and resources can help enable your organization’s alignment to the CSF. If you’d like support in how to implement the CSF in your organization using AWS services and resources, contact an AWS Solutions Architect.

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Author

Min Hyun

Min is the Global Lead for Growth Strategies at AWS. Her team’s mission is to set the industry bar in thought leadership for security and data privacy assurance in emerging technology, trends and strategy to advance customers’ journeys to AWS. View her other Security Blog publications here.

New – Pay-per-Session Pricing for Amazon QuickSight, Another Region, and Lots More

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/new-pay-per-session-pricing-for-amazon-quicksight-another-region-and-lots-more/

Amazon QuickSight is a fully managed cloud business intelligence system that gives you Fast & Easy to Use Business Analytics for Big Data. QuickSight makes business analytics available to organizations of all shapes and sizes, with the ability to access data that is stored in your Amazon Redshift data warehouse, your Amazon Relational Database Service (RDS) relational databases, flat files in S3, and (via connectors) data stored in on-premises MySQL, PostgreSQL, and SQL Server databases. QuickSight scales to accommodate tens, hundreds, or thousands of users per organization.

Today we are launching a new, session-based pricing option for QuickSight, along with additional region support and other important new features. Let’s take a look at each one:

Pay-per-Session Pricing
Our customers are making great use of QuickSight and take full advantage of the power it gives them to connect to data sources, create reports, and and explore visualizations.

However, not everyone in an organization needs or wants such powerful authoring capabilities. Having access to curated data in dashboards and being able to interact with the data by drilling down, filtering, or slicing-and-dicing is more than adequate for their needs. Subscribing them to a monthly or annual plan can be seen as an unwarranted expense, so a lot of such casual users end up not having access to interactive data or BI.

In order to allow customers to provide all of their users with interactive dashboards and reports, the Enterprise Edition of Amazon QuickSight now allows Reader access to dashboards on a Pay-per-Session basis. QuickSight users are now classified as Admins, Authors, or Readers, with distinct capabilities and prices:

Authors have access to the full power of QuickSight; they can establish database connections, upload new data, create ad hoc visualizations, and publish dashboards, all for $9 per month (Standard Edition) or $18 per month (Enterprise Edition).

Readers can view dashboards, slice and dice data using drill downs, filters and on-screen controls, and download data in CSV format, all within the secure QuickSight environment. Readers pay $0.30 for 30 minutes of access, with a monthly maximum of $5 per reader.

Admins have all authoring capabilities, and can manage users and purchase SPICE capacity in the account. The QuickSight admin now has the ability to set the desired option (Author or Reader) when they invite members of their organization to use QuickSight. They can extend Reader invites to their entire user base without incurring any up-front or monthly costs, paying only for the actual usage.

To learn more, visit the QuickSight Pricing page.

A New Region
QuickSight is now available in the Asia Pacific (Tokyo) Region:

The UI is in English, with a localized version in the works.

Hourly Data Refresh
Enterprise Edition SPICE data sets can now be set to refresh as frequently as every hour. In the past, each data set could be refreshed up to 5 times a day. To learn more, read Refreshing Imported Data.

Access to Data in Private VPCs
This feature was launched in preview form late last year, and is now available in production form to users of the Enterprise Edition. As I noted at the time, you can use it to implement secure, private communication with data sources that do not have public connectivity, including on-premises data in Teradata or SQL Server, accessed over an AWS Direct Connect link. To learn more, read Working with AWS VPC.

Parameters with On-Screen Controls
QuickSight dashboards can now include parameters that are set using on-screen dropdown, text box, numeric slider or date picker controls. The default value for each parameter can be set based on the user name (QuickSight calls this a dynamic default). You could, for example, set an appropriate default based on each user’s office location, department, or sales territory. Here’s an example:

To learn more, read about Parameters in QuickSight.

URL Actions for Linked Dashboards
You can now connect your QuickSight dashboards to external applications by defining URL actions on visuals. The actions can include parameters, and become available in the Details menu for the visual. URL actions are defined like this:

You can use this feature to link QuickSight dashboards to third party applications (e.g. Salesforce) or to your own internal applications. Read Custom URL Actions to learn how to use this feature.

Dashboard Sharing
You can now share QuickSight dashboards across every user in an account.

Larger SPICE Tables
The per-data set limit for SPICE tables has been raised from 10 GB to 25 GB.

Upgrade to Enterprise Edition
The QuickSight administrator can now upgrade an account from Standard Edition to Enterprise Edition with a click. This enables provisioning of Readers with pay-per-session pricing, private VPC access, row-level security for dashboards and data sets, and hourly refresh of data sets. Enterprise Edition pricing applies after the upgrade.

Available Now
Everything I listed above is available now and you can start using it today!

You can try QuickSight for 60 days at no charge, and you can also attend our June 20th Webinar.

Jeff;

 

The devil wears Pravda

Post Syndicated from Robert Graham original https://blog.erratasec.com/2018/05/the-devil-wears-pravda.html

Classic Bond villain, Elon Musk, has a new plan to create a website dedicated to measuring the credibility and adherence to “core truth” of journalists. He is, without any sense of irony, going to call this “Pravda”. This is not simply wrong but evil.

Musk has a point. Journalists do suck, and many suck consistently. I see this in my own industry, cybersecurity, and I frequently criticize them for their suckage.

But what he’s doing here is not correcting them when they make mistakes (or what Musk sees as mistakes), but questioning their legitimacy. This legitimacy isn’t measured by whether they follow established journalism ethics, but whether their “core truths” agree with Musk’s “core truths”.

An example of the problem is how the press fixates on Tesla car crashes due to its “autopilot” feature. Pretty much every autopilot crash makes national headlines, while the press ignores the other 40,000 car crashes that happen in the United States each year. Musk spies on Tesla drivers (hello, classic Bond villain everyone) so he can see the dip in autopilot usage every time such a news story breaks. He’s got good reason to be concerned about this.

He argues that autopilot is safer than humans driving, and he’s got the statistics and government studies to back this up. Therefore, the press’s fixation on Tesla crashes is illegitimate “fake news”, titillating the audience with distorted truth.

But here’s the thing: that’s still only Musk’s version of the truth. Yes, on a mile-per-mile basis, autopilot is safer, but there’s nuance here. Autopilot is used primarily on freeways, which already have a low mile-per-mile accident rate. People choose autopilot only when conditions are incredibly safe and drivers are unlikely to have an accident anyway. Musk is therefore being intentionally deceptive comparing apples to oranges. Autopilot may still be safer, it’s just that the numbers Musk uses don’t demonstrate this.

And then there is the truth calling it “autopilot” to begin with, because it isn’t. The public is overrating the capabilities of the feature. It’s little different than “lane keeping” and “adaptive cruise control” you can now find in other cars. In many ways, the technology is behind — my Tesla doesn’t beep at me when a pedestrian walks behind my car while backing up, but virtually every new car on the market does.

Yes, the press unduly covers Tesla autopilot crashes, but Musk has only himself to blame by unduly exaggerating his car’s capabilities by calling it “autopilot”.

What’s “core truth” is thus rather difficult to obtain. What the press satisfies itself with instead is smaller truths, what they can document. The facts are in such cases that the accident happened, and they try to get Tesla or Musk to comment on it.

What you can criticize a journalist for is therefore not “core truth” but whether they did journalism correctly. When such stories criticize “autopilot”, but don’t do their diligence in getting Tesla’s side of the story, then that’s a violation of journalistic practice. When I criticize journalists for their poor handling of stories in my industry, I try to focus on which journalistic principles they get wrong. For example, the NYTimes reporters do a lot of stories quoting anonymous government sources in clear violation of journalistic principles.

If “credibility” is the concern, then it’s the classic Bond villain here that’s the problem: Musk himself. His track record on business statements is abysmal. For example, when he announced the Model 3 he claimed production targets that every Wall Street analyst claimed were absurd. He didn’t make those targets, he didn’t come close. Model 3 production is still lagging behind Musk’s twice adjusted targets.

https://www.bloomberg.com/graphics/2018-tesla-tracker/

So who has a credibility gap here, the press, or Musk himself?

Not only is Musk’s credibility problem ironic, so is the name he chose, “Pravada”, the Russian word for truth that was the name of the Soviet Union Communist Party’s official newspaper. This is so absurd this has to be a joke, yet Musk claims to be serious about all this.

Yes, the press has a lot of problems, and if Musk were some journalism professor concerned about journalists meeting the objective standards of their industry (e.g. abusing anonymous sources), then this would be a fine thing. But it’s not. It’s Musk who is upset the press’s version of “core truth” does not agree with his version — a version that he’s proven time and time again differs from “real truth”.

Just in case Musk is serious, I’ve already registered “www.antipravda.com” to start measuring the credibility of statements by billionaire playboy CEOs. Let’s see who blinks first.


I stole the title, with permission, from this tweet:

The Benefits of Side Projects

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

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

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

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

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

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

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

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

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

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

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

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

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

Japan’s Directorate for Signals Intelligence

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

The Intercept has a long article on Japan’s equivalent of the NSA: the Directorate for Signals Intelligence. Interesting, but nothing really surprising.

The directorate has a history that dates back to the 1950s; its role is to eavesdrop on communications. But its operations remain so highly classified that the Japanese government has disclosed little about its work ­ even the location of its headquarters. Most Japanese officials, except for a select few of the prime minister’s inner circle, are kept in the dark about the directorate’s activities, which are regulated by a limited legal framework and not subject to any independent oversight.

Now, a new investigation by the Japanese broadcaster NHK — produced in collaboration with The Intercept — reveals for the first time details about the inner workings of Japan’s opaque spy community. Based on classified documents and interviews with current and former officials familiar with the agency’s intelligence work, the investigation shines light on a previously undisclosed internet surveillance program and a spy hub in the south of Japan that is used to monitor phone calls and emails passing across communications satellites.

The article includes some new documents from the Snowden archive.

AWS IoT 1-Click – Use Simple Devices to Trigger Lambda Functions

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-iot-1-click-use-simple-devices-to-trigger-lambda-functions/

We announced a preview of AWS IoT 1-Click at AWS re:Invent 2017 and have been refining it ever since, focusing on simplicity and a clean out-of-box experience. Designed to make IoT available and accessible to a broad audience, AWS IoT 1-Click is now generally available, along with new IoT buttons from AWS and AT&T.

I sat down with the dev team a month or two ago to learn about the service so that I could start thinking about my blog post. During the meeting they gave me a pair of IoT buttons and I started to think about some creative ways to put them to use. Here are a few that I came up with:

Help Request – Earlier this month I spent a very pleasant weekend at the HackTillDawn hackathon in Los Angeles. As the participants were hacking away, they occasionally had questions about AWS, machine learning, Amazon SageMaker, and AWS DeepLens. While we had plenty of AWS Solution Architects on hand (decked out in fashionable & distinctive AWS shirts for easy identification), I imagined an IoT button for each team. Pressing the button would alert the SA crew via SMS and direct them to the proper table.

Camera ControlTim Bray and I were in the AWS video studio, prepping for the first episode of Tim’s series on AWS Messaging. Minutes before we opened the Twitch stream I realized that we did not have a clean, unobtrusive way to ask the camera operator to switch to a closeup view. Again, I imagined that a couple of IoT buttons would allow us to make the request.

Remote Dog Treat Dispenser – My dog barks every time a stranger opens the gate in front of our house. While it is great to have confirmation that my Ring doorbell is working, I would like to be able to press a button and dispense a treat so that Luna stops barking!

Homes, offices, factories, schools, vehicles, and health care facilities can all benefit from IoT buttons and other simple IoT devices, all managed using AWS IoT 1-Click.

All About AWS IoT 1-Click
As I said earlier, we have been focusing on simplicity and a clean out-of-box experience. Here’s what that means:

Architects can dream up applications for inexpensive, low-powered devices.

Developers don’t need to write any device-level code. They can make use of pre-built actions, which send email or SMS messages, or write their own custom actions using AWS Lambda functions.

Installers don’t have to install certificates or configure cloud endpoints on newly acquired devices, and don’t have to worry about firmware updates.

Administrators can monitor the overall status and health of each device, and can arrange to receive alerts when a device nears the end of its useful life and needs to be replaced, using a single interface that spans device types and manufacturers.

I’ll show you how easy this is in just a moment. But first, let’s talk about the current set of devices that are supported by AWS IoT 1-Click.

Who’s Got the Button?
We’re launching with support for two types of buttons (both pictured above). Both types of buttons are pre-configured with X.509 certificates, communicate to the cloud over secure connections, and are ready to use.

The AWS IoT Enterprise Button communicates via Wi-Fi. It has a 2000-click lifetime, encrypts outbound data using TLS, and can be configured using BLE and our mobile app. It retails for $19.99 (shipping and handling not included) and can be used in the United States, Europe, and Japan.

The AT&T LTE-M Button communicates via the LTE-M cellular network. It has a 1500-click lifetime, and also encrypts outbound data using TLS. The device and the bundled data plan is available an an introductory price of $29.99 (shipping and handling not included), and can be used in the United States.

We are very interested in working with device manufacturers in order to make even more shapes, sizes, and types of devices (badge readers, asset trackers, motion detectors, and industrial sensors, to name a few) available to our customers. Our team will be happy to tell you about our provisioning tools and our facility for pushing OTA (over the air) updates to large fleets of devices; you can contact them at [email protected].

AWS IoT 1-Click Concepts
I’m eager to show you how to use AWS IoT 1-Click and the buttons, but need to introduce a few concepts first.

Device – A button or other item that can send messages. Each device is uniquely identified by a serial number.

Placement Template – Describes a like-minded collection of devices to be deployed. Specifies the action to be performed and lists the names of custom attributes for each device.

Placement – A device that has been deployed. Referring to placements instead of devices gives you the freedom to replace and upgrade devices with minimal disruption. Each placement can include values for custom attributes such as a location (“Building 8, 3rd Floor, Room 1337”) or a purpose (“Coffee Request Button”).

Action – The AWS Lambda function to invoke when the button is pressed. You can write a function from scratch, or you can make use of a pair of predefined functions that send an email or an SMS message. The actions have access to the attributes; you can, for example, send an SMS message with the text “Urgent need for coffee in Building 8, 3rd Floor, Room 1337.”

Getting Started with AWS IoT 1-Click
Let’s set up an IoT button using the AWS IoT 1-Click Console:

If I didn’t have any buttons I could click Buy devices to get some. But, I do have some, so I click Claim devices to move ahead. I enter the device ID or claim code for my AT&T button and click Claim (I can enter multiple claim codes or device IDs if I want):

The AWS buttons can be claimed using the console or the mobile app; the first step is to use the mobile app to configure the button to use my Wi-Fi:

Then I scan the barcode on the box and click the button to complete the process of claiming the device. Both of my buttons are now visible in the console:

I am now ready to put them to use. I click on Projects, and then Create a project:

I name and describe my project, and click Next to proceed:

Now I define a device template, along with names and default values for the placement attributes. Here’s how I set up a device template (projects can contain several, but I just need one):

The action has two mandatory parameters (phone number and SMS message) built in; I add three more (Building, Room, and Floor) and click Create project:

I’m almost ready to ask for some coffee! The next step is to associate my buttons with this project by creating a placement for each one. I click Create placements to proceed. I name each placement, select the device to associate with it, and then enter values for the attributes that I established for the project. I can also add additional attributes that are peculiar to this placement:

I can inspect my project and see that everything looks good:

I click on the buttons and the SMS messages appear:

I can monitor device activity in the AWS IoT 1-Click Console:

And also in the Lambda Console:

The Lambda function itself is also accessible, and can be used as-is or customized:

As you can see, this is the code that lets me use {{*}}include all of the placement attributes in the message and {{Building}} (for example) to include a specific placement attribute.

Now Available
I’ve barely scratched the surface of this cool new service and I encourage you to give it a try (or a click) yourself. Buy a button or two, build something cool, and let me know all about it!

Pricing is based on the number of enabled devices in your account, measured monthly and pro-rated for partial months. Devices can be enabled or disabled at any time. See the AWS IoT 1-Click Pricing page for more info.

To learn more, visit the AWS IoT 1-Click home page or read the AWS IoT 1-Click documentation.

Jeff;

 

Седем мита за GDPR

Post Syndicated from Bozho original https://blog.bozho.net/blog/3105

GDPR, или новият Общ регламент относно защитата на данните, е гореща тема, тъй като влиза в сила на 25-ти май. И разбира се, публичното пространство е пълно с мнения и заключения по въпроса. За съжаление повечето от тях са грешни. На база на наблюденията ми от последните месеци реших да извадя 7 мита за Регламента.

От края на миналата година активно консултирам малки и големи компании относно регламента, водя обучения и семинари и пиша технически разяснения. И не, не съм юрист, но Регламентът изисква познаване както на правните, така и на технологичните аспекти на защитата на данните.

1. „GDPR ми е ясен, разбрал съм го“

Най-опасното е човек да мисли, че разбира нещо след като само е чувал за него или е прочел две статии в новинарски сайт (както за GDPR така и в по-общ смисъл). Аз самият все още не твърдя, че познавам всички ъгълчета на Регламента. Но по конференции, кръгли маси, обучения, срещи, форуми и фейсбук групи съм чул и прочел твърде много глупости относно GDPR. И то такива, които могат да се оборят с „Не е вярно, виж чл. Х“. В тази категория за съжаление влизат и юристи, и IT специалисти, и хора на ръководни позиции.

От мита, че познаваме GDPR, произлизат и всички останали митове. Част от вината за това е и на самия Регламент. Дълъг е, чете се трудно, има лоши законодателни практики (3 различни хипотези в едно изречение??) и нито Европейската Комисия, нито някоя друга европейска институция си е направила труда да го разясни за хората, за които се отнася – а именно, за почти всички. Т.нар. „работна група по чл. 29 (от предишната Директива)“ има разяснения по някои въпроси, но те са също толкова дълги и трудно четими ако човек няма контекст. При толкова широкообхватно законодателство е голяма грешка то да се остави нерязяснено. Да, в него има много нюанси и много условности (което е друг негов минус), но е редно поне общите положения да бъдат разказани ясно и то от практическа гледна точка.

Така че не – да не си мислим, че сме разбрали GDPR.

2. „Личните данни са тайна“

Определението за лични данни в Регламента може би характеризира целия Регламент – трудно четима и „увъртяно“:

„лични данни“ означава всяка информация, свързана с идентифицирано физическо лице или физическо лице, което може да бъде идентифицирано („субект на данни“); физическо лице, което може да бъде идентифицирано, е лице, което може да бъде идентифицирано, пряко или непряко, по-специално чрез идентификатор като име, идентификационен номер, данни за местонахождение, онлайн идентификатор или по един или повече признаци, специфични за физическата, физиологичната, генетичната, психическата, умствената, икономическата, културната или социална идентичност на това физическо лице;

Всъщност лични данни са всичко, което се отнася за нас. Включително съвсем очевидни неща като цвят на очи и коса, ръст и т.н. И не, личните данни не са тайна. Имената ни не са тайна, ръстът ни не е тайна. ЕГН-то ни не е тайна (да, не е). Има специални категории лични данни, които могат да бъдат тайна (напр. медицински данни), но за тях има специален ред.

Разграничаването, което GDPR не прави ясно, за разлика от едно разяснение на NIST – има лични данни, на база на които хората могат да бъдат идентифицирани, и такива, с които не могат, но се отнасят за тях. По цвят на косата не можем да бъдем идентифицирани. Но цветът на косата представлява лични данни. По професия не можем да бъдем идентифицирани. (По три имена и професия обаче – евентуално може и да можем). И тук едно много важно нещо, посочено в последните изречения на съображение 26 – данни, които са лични, но не могат да бъдат отнесени към конкретно лице, и на база на които не може да бъде идентифицирано такова, не попадат в обхвата на регламента. И съвсем не са тайна – „имаме 120 клиента на възраст 32 години, които са си купили телефон Sony между Април и Юли“ е напълно окей.

Та, личните данни не са та тайни – някои даже са съвсем явни и видни. Целта на GDPR е да уреди тяхната обработка с автоматизирани средства (или полуавтоматизирани в структуриран вид, т.е. тетрадки). С други думи – кой има право да ги съхранява, за какво има право да ги използва и как трябва да ги съхранява и използва.

3. „GDPR не се отнася за мен“

Няма почти никакви изключения в Регламента. Компании под 250 души не са длъжни да водят едни регистри, а компании, които нямат мащабна обработка и наблюдение на субекти на данни нямат задължение за длъжностно лице по защита на данните (Data protection officer; тази точка е дискусионна с оглед на предложенията за изменения на българския закон за защита на личните данни, които разширяват прекалено много изискванията за DPO). Всичко останало важи за всички, които обработват лични данни. И всички граждани на ЕС имат всички права, посочени в Регламента.

4. „Ще ни глобят 20 милиона евро“

Тези глоби са единствената причина GDPR да е популярен. Ако не бяха те, на никого нямаше да му дреме за поредното европейско законодателство. Обаче заради плашещите глоби всякакви консултанти ходят и обясняват как „ами те глобите, знаете, са до 20 милиона“.

Но колкото и да се повтарят тези 20 милиона (или както някои пресоляват манджата „глоби над 20 милиона евро“), това не ги прави реалистични. Първо, има процес, който всички регулатори ще следват, и който включва няколко стъпки на „препоръки“ преди налагане на глоба. Идва комисията, установява несъответствие, прави препоръки, идва пак, установява взети ли са мерки. И ако сте съвсем недобросъвестни и не направите нищо, тогава идват глобите. И тези глоби са пропорционални на риска и на количеството данни. Не е „добър ден, 20 милиона“. Според мен 20-те милиона ще са само за огромни международни компании, като Google и Facebook, които обработват данни на милиони хора. За тетрадката с вересиите глоба няма да има (правото да бъдеш забравен се реализира със задраскване, но само ако магазинерът няма легитимен интерес да ги съхранява, а именно – да му върнете парите :)).

Тук една скоба за българското законодателство – то предвижда доста високи минимуми на глобите (10 хил. лева). Това се оспорва в рамките на общественото обсъждане и е несъразмерно на минимумите в други европейски държави и се надявам да спадне значително.

5. „Трябва да спрем да обработваме лични данни“

В никакъв случай. GDPR не забранява обработката на лични данни, просто урежда как и кога те да се обработват. Имате право да обработвате всички данни, които са ви нужни, за да си свършите работата.

Някои интернет компании напоследък обявиха, че спират работа заради GDPR, защото не им позволявал да обработват данни. И това в общия случай са глупости. Или те така или иначе са били на загуба и сега си търсят оправдание, или са били такъв разграден двор и са продавали данните ви наляво и надясно без ваше знание и съгласие, че GDPR представлява риск. Но то това му е идеята – да няма такива практики. Защото (както твърди Регламентът) това представлява риск за правата и свободите на субектите на данни (субект на данните – това звучи гордо).

6. „Трябва да искаме съгласие за всичко“

Съгласието на потребителите е само едно от основанията за обработка на данните. Има доста други и те дори са по-често срещани в реалния бизнес. Както отбелязах по-горе, ако можете да докажете легитимен интерес да обработвате данните, за да си свършите работата, може да го правите без съгласие. Имате ли право да събирате адреса и телефона на клиента, ако доставяте храна? Разбира се, иначе не може да му я доставите. Няма нужда от съгласие в този случай (би имало нужда от съгласие ако освен за доставката, ползвате данните му и за други цели). Нужно ли е съгласие за обработка на лични данни в рамките на трудово правоотношение? Не, защото Кодекса на труда изисква работодателят да води трудово досие. Има ли нужда банката да поиска съгласие, за да ви обработва личните данни за кредита? Не, защото те са нужни за изпълнението на договора за кредит (и не, не можете да кажете на банката да ви „забрави“ кредита; правото да бъдеш забравен важи само в някои случаи).

Усещането ми обаче е, че ще плъзнат едни декларации и чекбоксове за съгласие, които ще са напълно излишни…но вж. т.1. А дори когато трябва да ги има, ще бъдат прекалено общи, а не за определени цели (съгласявам се да ми обработвате данните, ама за какво точно?).

7. „Съответсвието с GDPR е трудно и скъпо“

…и съответно Регламентът е голяма административна тежест, излишно натоварване на бизнеса и т.н. Ами не, не е. Съответствието с GDPR изисква осъзната обработка на личните данни. Да, изисква и няколко хартии – политики и процедури, с които да докажете, че знаете какви лични данни обработвате и че ги обработвате съвестно, както и че знаете, че гражданите имат някакви права във връзка с данните си (и че всъщност не вие, а те са собственици на тези данни), но извън това съответствието не е тежко. Е, ако хал хабер си нямате какви данни и бизнес процеси имате, може и да отнеме време да ги вкарате в ред, но това е нещо, което по принцип e добре да се случи, със или без GDPR.

Ако например досега в една болница данните за пациентите са били на незащитен по никакъв начин сървър и всеки е имал достъп до него, без това да оставя следа, и също така е имало още 3-4 сървъра, на които никой не е знаел, че има данни (щото „IT-то“ е напуснало преди 2 години), то да, ще трябват малко усилия.

Но почти всичко в GDPR са „добри практики“ така или иначе. Неща, които са полезни и за самия бизнес, не само за гражданите.

Разбира се, синдромът „по-светец и от Папата“ започва да се наблюдава. Освен компаниите, които са изсипали милиони на юристи, консултанти, доставчици (и което накрая е имало плачевен резултат и се е оказало, че за един месец няколко човека могат да я свършат цялата тая работа) има и такива, които четат Регламента като „по-добре да не даваме никакви данни никъде, за всеки случай“. Презастраховането на големи компании, като Twitter и Facebook например, има риск да „удари“ компании, които зависят от техните данни. Но отново – вж. т.1.


В заключение, GDPR не е нещо страшно, не е нещо лошо и не е „измислица на бюрократите в Брюксел“. Има много какво да се желае откъм яснотата му и предполагам ще има какво да се желае откъм приложението му, но „по принцип“ е окей.

И както става винаги със законодателства, обхващащи много хора и бизнеси – в началото ще има не само 7, а 77 мита, които с времето и с практиката ще се изяснят. Ще има грешки на растежа, има риск (особено в по-малки и корумпирани държави) някой „да го отнесе“, но гледайки голямата картинка, смятам, че с този Регламент след 5 години ще сме по-добре откъм защита на данните и откъм последици от липсата на на такава защита.

Analyze Apache Parquet optimized data using Amazon Kinesis Data Firehose, Amazon Athena, and Amazon Redshift

Post Syndicated from Roy Hasson original https://aws.amazon.com/blogs/big-data/analyzing-apache-parquet-optimized-data-using-amazon-kinesis-data-firehose-amazon-athena-and-amazon-redshift/

Amazon Kinesis Data Firehose is the easiest way to capture and stream data into a data lake built on Amazon S3. This data can be anything—from AWS service logs like AWS CloudTrail log files, Amazon VPC Flow Logs, Application Load Balancer logs, and others. It can also be IoT events, game events, and much more. To efficiently query this data, a time-consuming ETL (extract, transform, and load) process is required to massage and convert the data to an optimal file format, which increases the time to insight. This situation is less than ideal, especially for real-time data that loses its value over time.

To solve this common challenge, Kinesis Data Firehose can now save data to Amazon S3 in Apache Parquet or Apache ORC format. These are optimized columnar formats that are highly recommended for best performance and cost-savings when querying data in S3. This feature directly benefits you if you use Amazon Athena, Amazon Redshift, AWS Glue, Amazon EMR, or any other big data tools that are available from the AWS Partner Network and through the open-source community.

Amazon Connect is a simple-to-use, cloud-based contact center service that makes it easy for any business to provide a great customer experience at a lower cost than common alternatives. Its open platform design enables easy integration with other systems. One of those systems is Amazon Kinesis—in particular, Kinesis Data Streams and Kinesis Data Firehose.

What’s really exciting is that you can now save events from Amazon Connect to S3 in Apache Parquet format. You can then perform analytics using Amazon Athena and Amazon Redshift Spectrum in real time, taking advantage of this key performance and cost optimization. Of course, Amazon Connect is only one example. This new capability opens the door for a great deal of opportunity, especially as organizations continue to build their data lakes.

Amazon Connect includes an array of analytics views in the Administrator dashboard. But you might want to run other types of analysis. In this post, I describe how to set up a data stream from Amazon Connect through Kinesis Data Streams and Kinesis Data Firehose and out to S3, and then perform analytics using Athena and Amazon Redshift Spectrum. I focus primarily on the Kinesis Data Firehose support for Parquet and its integration with the AWS Glue Data Catalog, Amazon Athena, and Amazon Redshift.

Solution overview

Here is how the solution is laid out:

 

 

The following sections walk you through each of these steps to set up the pipeline.

1. Define the schema

When Kinesis Data Firehose processes incoming events and converts the data to Parquet, it needs to know which schema to apply. The reason is that many times, incoming events contain all or some of the expected fields based on which values the producers are advertising. A typical process is to normalize the schema during a batch ETL job so that you end up with a consistent schema that can easily be understood and queried. Doing this introduces latency due to the nature of the batch process. To overcome this issue, Kinesis Data Firehose requires the schema to be defined in advance.

To see the available columns and structures, see Amazon Connect Agent Event Streams. For the purpose of simplicity, I opted to make all the columns of type String rather than create the nested structures. But you can definitely do that if you want.

The simplest way to define the schema is to create a table in the Amazon Athena console. Open the Athena console, and paste the following create table statement, substituting your own S3 bucket and prefix for where your event data will be stored. A Data Catalog database is a logical container that holds the different tables that you can create. The default database name shown here should already exist. If it doesn’t, you can create it or use another database that you’ve already created.

CREATE EXTERNAL TABLE default.kfhconnectblog (
  awsaccountid string,
  agentarn string,
  currentagentsnapshot string,
  eventid string,
  eventtimestamp string,
  eventtype string,
  instancearn string,
  previousagentsnapshot string,
  version string
)
STORED AS parquet
LOCATION 's3://your_bucket/kfhconnectblog/'
TBLPROPERTIES ("parquet.compression"="SNAPPY")

That’s all you have to do to prepare the schema for Kinesis Data Firehose.

2. Define the data streams

Next, you need to define the Kinesis data streams that will be used to stream the Amazon Connect events.  Open the Kinesis Data Streams console and create two streams.  You can configure them with only one shard each because you don’t have a lot of data right now.

3. Define the Kinesis Data Firehose delivery stream for Parquet

Let’s configure the Data Firehose delivery stream using the data stream as the source and Amazon S3 as the output. Start by opening the Kinesis Data Firehose console and creating a new data delivery stream. Give it a name, and associate it with the Kinesis data stream that you created in Step 2.

As shown in the following screenshot, enable Record format conversion (1) and choose Apache Parquet (2). As you can see, Apache ORC is also supported. Scroll down and provide the AWS Glue Data Catalog database name (3) and table names (4) that you created in Step 1. Choose Next.

To make things easier, the output S3 bucket and prefix fields are automatically populated using the values that you defined in the LOCATION parameter of the create table statement from Step 1. Pretty cool. Additionally, you have the option to save the raw events into another location as defined in the Source record S3 backup section. Don’t forget to add a trailing forward slash “ / “ so that Data Firehose creates the date partitions inside that prefix.

On the next page, in the S3 buffer conditions section, there is a note about configuring a large buffer size. The Parquet file format is highly efficient in how it stores and compresses data. Increasing the buffer size allows you to pack more rows into each output file, which is preferred and gives you the most benefit from Parquet.

Compression using Snappy is automatically enabled for both Parquet and ORC. You can modify the compression algorithm by using the Kinesis Data Firehose API and update the OutputFormatConfiguration.

Be sure to also enable Amazon CloudWatch Logs so that you can debug any issues that you might run into.

Lastly, finalize the creation of the Firehose delivery stream, and continue on to the next section.

4. Set up the Amazon Connect contact center

After setting up the Kinesis pipeline, you now need to set up a simple contact center in Amazon Connect. The Getting Started page provides clear instructions on how to set up your environment, acquire a phone number, and create an agent to accept calls.

After setting up the contact center, in the Amazon Connect console, choose your Instance Alias, and then choose Data Streaming. Under Agent Event, choose the Kinesis data stream that you created in Step 2, and then choose Save.

At this point, your pipeline is complete.  Agent events from Amazon Connect are generated as agents go about their day. Events are sent via Kinesis Data Streams to Kinesis Data Firehose, which converts the event data from JSON to Parquet and stores it in S3. Athena and Amazon Redshift Spectrum can simply query the data without any additional work.

So let’s generate some data. Go back into the Administrator console for your Amazon Connect contact center, and create an agent to handle incoming calls. In this example, I creatively named mine Agent One. After it is created, Agent One can get to work and log into their console and set their availability to Available so that they are ready to receive calls.

To make the data a bit more interesting, I also created a second agent, Agent Two. I then made some incoming and outgoing calls and caused some failures to occur, so I now have enough data available to analyze.

5. Analyze the data with Athena

Let’s open the Athena console and run some queries. One thing you’ll notice is that when we created the schema for the dataset, we defined some of the fields as Strings even though in the documentation they were complex structures.  The reason for doing that was simply to show some of the flexibility of Athena to be able to parse JSON data. However, you can define nested structures in your table schema so that Kinesis Data Firehose applies the appropriate schema to the Parquet file.

Let’s run the first query to see which agents have logged into the system.

The query might look complex, but it’s fairly straightforward:

WITH dataset AS (
  SELECT 
    from_iso8601_timestamp(eventtimestamp) AS event_ts,
    eventtype,
    -- CURRENT STATE
    json_extract_scalar(
      currentagentsnapshot,
      '$.agentstatus.name') AS current_status,
    from_iso8601_timestamp(
      json_extract_scalar(
        currentagentsnapshot,
        '$.agentstatus.starttimestamp')) AS current_starttimestamp,
    json_extract_scalar(
      currentagentsnapshot, 
      '$.configuration.firstname') AS current_firstname,
    json_extract_scalar(
      currentagentsnapshot,
      '$.configuration.lastname') AS current_lastname,
    json_extract_scalar(
      currentagentsnapshot, 
      '$.configuration.username') AS current_username,
    json_extract_scalar(
      currentagentsnapshot, 
      '$.configuration.routingprofile.defaultoutboundqueue.name') AS               current_outboundqueue,
    json_extract_scalar(
      currentagentsnapshot, 
      '$.configuration.routingprofile.inboundqueues[0].name') as current_inboundqueue,
    -- PREVIOUS STATE
    json_extract_scalar(
      previousagentsnapshot, 
      '$.agentstatus.name') as prev_status,
    from_iso8601_timestamp(
      json_extract_scalar(
        previousagentsnapshot, 
       '$.agentstatus.starttimestamp')) as prev_starttimestamp,
    json_extract_scalar(
      previousagentsnapshot, 
      '$.configuration.firstname') as prev_firstname,
    json_extract_scalar(
      previousagentsnapshot, 
      '$.configuration.lastname') as prev_lastname,
    json_extract_scalar(
      previousagentsnapshot, 
      '$.configuration.username') as prev_username,
    json_extract_scalar(
      previousagentsnapshot, 
      '$.configuration.routingprofile.defaultoutboundqueue.name') as current_outboundqueue,
    json_extract_scalar(
      previousagentsnapshot, 
      '$.configuration.routingprofile.inboundqueues[0].name') as prev_inboundqueue
  from kfhconnectblog
  where eventtype <> 'HEART_BEAT'
)
SELECT
  current_status as status,
  current_username as username,
  event_ts
FROM dataset
WHERE eventtype = 'LOGIN' AND current_username <> ''
ORDER BY event_ts DESC

The query output looks something like this:

Here is another query that shows the sessions each of the agents engaged with. It tells us where they were incoming or outgoing, if they were completed, and where there were missed or failed calls.

WITH src AS (
  SELECT
     eventid,
     json_extract_scalar(currentagentsnapshot, '$.configuration.username') as username,
     cast(json_extract(currentagentsnapshot, '$.contacts') AS ARRAY(JSON)) as c,
     cast(json_extract(previousagentsnapshot, '$.contacts') AS ARRAY(JSON)) as p
  from kfhconnectblog
),
src2 AS (
  SELECT *
  FROM src CROSS JOIN UNNEST (c, p) AS contacts(c_item, p_item)
),
dataset AS (
SELECT 
  eventid,
  username,
  json_extract_scalar(c_item, '$.contactid') as c_contactid,
  json_extract_scalar(c_item, '$.channel') as c_channel,
  json_extract_scalar(c_item, '$.initiationmethod') as c_direction,
  json_extract_scalar(c_item, '$.queue.name') as c_queue,
  json_extract_scalar(c_item, '$.state') as c_state,
  from_iso8601_timestamp(json_extract_scalar(c_item, '$.statestarttimestamp')) as c_ts,
  
  json_extract_scalar(p_item, '$.contactid') as p_contactid,
  json_extract_scalar(p_item, '$.channel') as p_channel,
  json_extract_scalar(p_item, '$.initiationmethod') as p_direction,
  json_extract_scalar(p_item, '$.queue.name') as p_queue,
  json_extract_scalar(p_item, '$.state') as p_state,
  from_iso8601_timestamp(json_extract_scalar(p_item, '$.statestarttimestamp')) as p_ts
FROM src2
)
SELECT 
  username,
  c_channel as channel,
  c_direction as direction,
  p_state as prev_state,
  c_state as current_state,
  c_ts as current_ts,
  c_contactid as id
FROM dataset
WHERE c_contactid = p_contactid
ORDER BY id DESC, current_ts ASC

The query output looks similar to the following:

6. Analyze the data with Amazon Redshift Spectrum

With Amazon Redshift Spectrum, you can query data directly in S3 using your existing Amazon Redshift data warehouse cluster. Because the data is already in Parquet format, Redshift Spectrum gets the same great benefits that Athena does.

Here is a simple query to show querying the same data from Amazon Redshift. Note that to do this, you need to first create an external schema in Amazon Redshift that points to the AWS Glue Data Catalog.

SELECT 
  eventtype,
  json_extract_path_text(currentagentsnapshot,'agentstatus','name') AS current_status,
  json_extract_path_text(currentagentsnapshot, 'configuration','firstname') AS current_firstname,
  json_extract_path_text(currentagentsnapshot, 'configuration','lastname') AS current_lastname,
  json_extract_path_text(
    currentagentsnapshot,
    'configuration','routingprofile','defaultoutboundqueue','name') AS current_outboundqueue,
FROM default_schema.kfhconnectblog

The following shows the query output:

Summary

In this post, I showed you how to use Kinesis Data Firehose to ingest and convert data to columnar file format, enabling real-time analysis using Athena and Amazon Redshift. This great feature enables a level of optimization in both cost and performance that you need when storing and analyzing large amounts of data. This feature is equally important if you are investing in building data lakes on AWS.

 


Additional Reading

If you found this post useful, be sure to check out Analyzing VPC Flow Logs with Amazon Kinesis Firehose, Amazon Athena, and Amazon QuickSight and Work with partitioned data in AWS Glue.


About the Author

Roy Hasson is a Global Business Development Manager for AWS Analytics. He works with customers around the globe to design solutions to meet their data processing, analytics and business intelligence needs. Roy is big Manchester United fan cheering his team on and hanging out with his family.