Tag Archives: Foundational (100)

AWS completes Police-Assured Secure Facilities (PASF) audit in Europe (London) Region

2023-06-22 Vishal Pabari

Post Syndicated from Vishal Pabari original https://aws.amazon.com/blogs/security/aws-completes-police-assured-secure-facilities-pasf-audit-in-europe-london-region/

We’re excited to announce that our Europe (London) Region has renewed our accreditation for United Kingdom (UK) Police-Assured Secure Facilities (PASF) for Official-Sensitive data. Since 2017, the Amazon Web Services (AWS) Europe (London) Region has been assured under the PASF program. This demonstrates our continuous commitment to adhere to the heightened expectations of customers with UK law enforcement workloads. Our UK law enforcement customers who require PASF can continue to run their applications in the PASF-assured Europe (London) Region in confidence.

The PASF is a long-established assurance process, used by UK law enforcement, as a method for assuring the security of facilities such as data centers or other locations that house critical business applications that process or hold police data. PASF consists of a control set of security requirements, an on-site inspection, and an audit interview with representatives of the facility.

The Police Digital Service (PDS) confirmed the renewal for AWS on May 5, 2023. The UK police force and law enforcement organizations can obtain confirmation of the compliance status of AWS through the Police Digital Service.

To learn more about our compliance and security programs, see AWS Compliance Programs. As always, we value your feedback and questions; reach out to the AWS Compliance team through the Contact Us page.

Please reach out to your AWS account team if you have questions or feedback about PASF compliance.

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

Want more AWS Security news? Follow us on Twitter.

Amazon OpenSearch Service’s vector database capabilities explained

2023-06-22 Jon Handler

Post Syndicated from Jon Handler original https://aws.amazon.com/blogs/big-data/amazon-opensearch-services-vector-database-capabilities-explained/

OpenSearch is a scalable, flexible, and extensible open-source software suite for search, analytics, security monitoring, and observability applications, licensed under the Apache 2.0 license. It comprises a search engine, OpenSearch, which delivers low-latency search and aggregations, OpenSearch Dashboards, a visualization and dashboarding tool, and a suite of plugins that provide advanced capabilities like alerting, fine-grained access control, observability, security monitoring, and vector storage and processing. Amazon OpenSearch Service is a fully managed service that makes it simple to deploy, scale, and operate OpenSearch in the AWS Cloud.

As an end-user, when you use OpenSearch’s search capabilities, you generally have a goal in mind—something you want to accomplish. Along the way, you use OpenSearch to gather information in support of achieving that goal (or maybe the information is the original goal). We’ve all become used to the “search box” interface, where you type some words, and the search engine brings back results based on word-to-word matching. Let’s say you want to buy a couch in order to spend cozy evenings with your family around the fire. You go to Amazon.com, and you type “a cozy place to sit by the fire.” Unfortunately, if you run that search on Amazon.com, you get items like fire pits, heating fans, and home decorations—not what you intended. The problem is that couch manufacturers probably didn’t use the words “cozy,” “place,” “sit,” and “fire” in their product titles or descriptions.

In recent years, machine learning (ML) techniques have become increasingly popular to enhance search. Among them are the use of embedding models, a type of model that can encode a large body of data into an n-dimensional space where each entity is encoded into a vector, a data point in that space, and organized such that similar entities are closer together. An embedding model, for instance, could encode the semantics of a corpus. By searching for the vectors nearest to an encoded document — k-nearest neighbor (k-NN) search — you can find the most semantically similar documents. Sophisticated embedding models can support multiple modalities, for instance, encoding the image and text of a product catalog and enabling similarity matching on both modalities.

A vector database provides efficient vector similarity search by providing specialized indexes like k-NN indexes. It also provides other database functionality like managing vector data alongside other data types, workload management, access control and more. OpenSearch’s k-NN plugin provides core vector database functionality for OpenSearch, so when your customer searches for “a cozy place to sit by the fire” in your catalog, you can encode that prompt and use OpenSearch to perform a nearest neighbor query to surface that 8-foot, blue couch with designer arranged photographs in front of fireplaces.

Using OpenSearch Service as a vector database

With OpenSearch Service’s vector database capabilities, you can implement semantic search, Retrieval Augmented Generation (RAG) with LLMs, recommendation engines, and search rich media.

Semantic search

With semantic search, you improve the relevance of retrieved results using language-based embeddings on search documents. You enable your search customers to use natural language queries, like “a cozy place to sit by the fire” to find their 8-foot-long blue couch. For more information, refer to Building a semantic search engine in OpenSearch to learn how semantic search can deliver a 15% relevance improvement, as measured by normalized discounted cumulative gain (nDCG) metrics compared with keyword search. For a concrete example, our Improve search relevance with ML in Amazon OpenSearch Service workshop explores the difference between keyword and semantic search, based on a Bidirectional Encoder Representations from Transformers (BERT) model, hosted by Amazon SageMaker to generate vectors and store them in OpenSearch. The workshop uses product question answers as an example to show how keyword search using the keywords/phrases of the query leads to some irrelevant results. Semantic search is able to retrieve more relevant documents by matching the context and semantics of the query. The following diagram shows an example architecture for a semantic search application with OpenSearch Service as the vector database.

Architecture diagram showing how to use Amazon OpenSearch Service to perform semantic search to improve relevance

Retrieval Augmented Generation with LLMs

RAG is a method for building trustworthy generative AI chatbots using generative LLMs like OpenAI, ChatGPT, or Amazon Titan Text. With the rise of generative LLMs, application developers are looking for ways to take advantage of this innovative technology. One popular use case involves delivering conversational experiences through intelligent agents. Perhaps you’re a software provider with knowledge bases for product information, customer self-service, or industry domain knowledge like tax reporting rules or medical information about diseases and treatments. A conversational search experience provides an intuitive interface for users to sift through information through dialog and Q&A. Generative LLMs on their own are prone to hallucinations—a situation where the model generates a believable but factually incorrect response. RAG solves this problem by complementing generative LLMs with an external knowledge base that is typically built using a vector database hydrated with vector-encoded knowledge articles.

As illustrated in the following diagram, the query workflow starts with a question that is encoded and used to retrieve relevant knowledge articles from the vector database. Those results are sent to the generative LLM whose job is to augment those results, typically by summarizing the results as a conversational response. By complementing the generative model with a knowledge base, RAG grounds the model on facts to minimize hallucinations. You can learn more about building a RAG solution in the Retrieval Augmented Generation module of our semantic search workshop.

Architecture diagram showing how to use Amazon OpenSearch Service to perform retrieval-augmented generation

Recommendation engine

Recommendations are a common component in the search experience, especially for ecommerce applications. Adding a user experience feature like “more like this” or “customers who bought this also bought that” can drive additional revenue through getting customers what they want. Search architects employ many techniques and technologies to build recommendations, including Deep Neural Network (DNN) based recommendation algorithms such as the two-tower neural net model, YoutubeDNN. A trained embedding model encodes products, for example, into an embedding space where products that are frequently bought together are considered more similar, and therefore are represented as data points that are closer together in the embedding space. Another possibility
is that product embeddings are based on co-rating similarity instead of purchase activity. You can employ this affinity data through calculating the vector similarity between a particular user’s embedding and vectors in the database to return recommended items. The following diagram shows an example architecture of building a recommendation engine with OpenSearch as a vector store.

Architecture diagram showing how to use Amazon OpenSearch Service as a recommendation engine

Media search

Media search enables users to query the search engine with rich media like images, audio, and video. Its implementation is similar to semantic search—you create vector embeddings for your search documents and then query OpenSearch Service with a vector. The difference is you use a computer vision deep neural network (e.g. Convolutional Neural Network (CNN)) such as ResNet to convert images into vectors. The following diagram shows an example architecture of building an image search with OpenSearch as the vector store.

Architecture diagram showing how to use Amazon OpenSearch Service to search rich media like images, videos, and audio files

Understanding the technology

OpenSearch uses approximate nearest neighbor (ANN) algorithms from the NMSLIB, FAISS, and Lucene libraries to power k-NN search. These search methods employ ANN to improve search latency for large datasets. Of the three search methods the k-NN plugin provides, this method offers the best search scalability for large datasets. The engine details are as follows:

Non-Metric Space Library (NMSLIB) – NMSLIB implements the HNSW ANN algorithm
Facebook AI Similarity Search (FAISS) – FAISS implements both HNSW and IVF ANN algorithms
Lucene – Lucene implements the HNSW algorithm

Each of the three engines used for approximate k-NN search has its own attributes that make one more sensible to use than the others in a given situation. You can follow the general information in this section to help determine which engine will best meet your requirements.

In general, NMSLIB and FAISS should be selected for large-scale use cases. Lucene is a good option for smaller deployments, but offers benefits like smart filtering where the optimal filtering strategy—pre-filtering, post-filtering, or exact k-NN—is automatically applied depending on the situation. The following table summarizes the differences between each option.

.	NMSLIB-HNSW	FAISS-HNSW	FAISS-IVF	Lucene-HNSW
Max Dimension	16,000	16,000	16,000	1024
Filter	Post filter	Post filter	Post filter	Filter while search
Training Required	No	No	Yes	No
Similarity Metrics	l2, innerproduct, cosinesimil, l1, linf	l2, innerproduct	l2, innerproduct	l2, cosinesimil
Vector Volume	Tens of billions	Tens of billions	Tens of billions	< Ten million
Indexing latency	Low	Low	Lowest	Low
Query Latency & Quality	Low latency & high quality	Low latency & high quality	Low latency & low quality	High latency & high quality
Vector Compression	Flat	Flat Product Quantization	Flat Product Quantization	Flat
Memory Consumption	High	High Low with PQ	Medium Low with PQ	High

Approximate and exact nearest-neighbor search

The OpenSearch Service k-NN plugin supports three different methods for obtaining the k-nearest neighbors from an index of vectors: approximate k-NN, score script (exact k-NN), and painless extensions (exact k-NN).

Approximate k-NN

The first method takes an approximate nearest neighbor approach—it uses one of several algorithms to return the approximate k-nearest neighbors to a query vector. Usually, these algorithms sacrifice indexing speed and search accuracy in return for performance benefits such as lower latency, smaller memory footprints, and more scalable search. Approximate k-NN is the best choice for searches over large indexes (that is, hundreds of thousands of vectors or more) that require low latency. You should not use approximate k-NN if you want to apply a filter on the index before the k-NN search, which greatly reduces the number of vectors to be searched. In this case, you should use either the score script method or painless extensions.

Score script

The second method extends the OpenSearch Service score script functionality to run a brute force, exact k-NN search over knn_vector fields or fields that can represent binary objects. With this approach, you can run k-NN search on a subset of vectors in your index (sometimes referred to as a pre-filter search). This approach is preferred for searches over smaller bodies of documents or when a pre-filter is needed. Using this approach on large indexes may lead to high latencies.

Painless extensions

The third method adds the distance functions as painless extensions that you can use in more complex combinations. Similar to the k-NN score script, you can use this method to perform a brute force, exact k-NN search across an index, which also supports pre-filtering. This approach has slightly slower query performance compared to the k-NN score script. If your use case requires more customization over the final score, you should use this approach over score script k-NN.

Vector search algorithms

The simple way to find similar vectors is to use k-nearest neighbors (k-NN) algorithms, which compute the distance between a query vector and the other vectors in the vector database. As we mentioned earlier, the score script k-NN and painless extensions search methods use the exact k-NN algorithms under the hood. However, in the case of extremely large datasets with high dimensionality, this creates a scaling problem that reduces the efficiency of the search. Approximate nearest neighbor (ANN) search methods can overcome this by employing tools that restructure indexes more efficiently and reduce the dimensionality of searchable vectors. There are different ANN search algorithms; for example, locality sensitive hashing, tree-based, cluster-based, and graph-based. OpenSearch implements two ANN algorithms: Hierarchical Navigable Small Worlds (HNSW) and Inverted File System (IVF). For a more detailed explanation of how the HNSW and IVF algorithms work in OpenSearch, see blog post “Choose the k-NN algorithm for your billion-scale use case with OpenSearch”.

Hierarchical Navigable Small Worlds

The HNSW algorithm is one of the most popular algorithms out there for ANN search. The core idea of the algorithm is to build a graph with edges connecting index vectors that are close to each other. Then, on search, this graph is partially traversed to find the approximate nearest neighbors to the query vector. To steer the traversal towards the query’s nearest neighbors, the algorithm always visits the closest candidate to the query vector next.

Inverted File

The IVF algorithm separates your index vectors into a set of buckets, then, to reduce your search time, only searches through a subset of these buckets. However, if the algorithm just randomly split up your vectors into different buckets, and only searched a subset of them, it would yield a poor approximation. The IVF algorithm uses a more elegant approach. First, before indexing begins, it assigns each bucket a representative vector. When a vector is indexed, it gets added to the bucket that has the closest representative vector. This way, vectors that are closer to each other are placed roughly in the same or nearby buckets.

Vector similarity metrics

All search engines use a similarity metric to rank and sort results and bring the most relevant results to the top. When you use a plain text query, the similarity metric is called TF-IDF, which measures the importance of the terms in the query and generates a score based on the number of textual matches. When your query includes a vector, the similarity metrics are spatial in nature, taking advantage of proximity in the vector space. OpenSearch supports several similarity or distance measures:

Euclidean distance – The straight-line distance between points.
L1 (Manhattan) distance – The sum of the differences of all of the vector components. L1 distance measures how many orthogonal city blocks you need to traverse from point A to point B.
L-infinity (chessboard) distance – The number of moves a King would make on an n-dimensional chessboard. It’s different than Euclidean distance on the diagonals—a diagonal step on a 2-dimensional chessboard is 1.41 Euclidean units away, but 2 L-infinity units away.
Inner product – The product of the magnitudes of two vectors and the cosine of the angle between them. Usually used for natural language processing (NLP) vector similarity.
Cosine similarity – The cosine of the angle between two vectors in a vector space.
Hamming distance – For binary-coded vectors, the number of bits that differ between the two vectors.

Advantage of OpenSearch as a vector database

When you use OpenSearch Service as a vector database, you can take advantage of the service’s features like usability, scalability, availability, interoperability, and security. More importantly, you can use OpenSearch’s search features to enhance the search experience. For example, you can use Learning to Rank in OpenSearch to integrate user clickthrough behavior data into your search application and improve search relevance. You can also combine OpenSearch text search and vector search capabilities to search documents with keyword and semantic similarity. You can also use other fields in the index to filter documents to improve relevance. For advanced users, you can use a hybrid scoring model to combine OpenSearch’s text-based relevance score, computed with the Okapi BM25 function and its vector search score to improve the ranking of your search results.

Scale and limits

OpenSearch as vector database support billions of vector records. Keep in mind the following calculator regarding number of vectors and dimensions to size your cluster.

Number of vectors

OpenSearch VectorDB takes advantage of the sharding capabilities of OpenSearch and can scale to billions of vectors at single-digit millisecond latencies by sharding vectors and scale horizontally by adding more nodes. The number of vectors that can fit in a single machine is a function of the off-heap memory availability on the machine. The number of nodes required will depend on the amount of memory that can be used for the algorithm per node and the total amount of memory required by the algorithm. The more nodes, the more memory and better performance. The amount of memory available per node is computed as memory_available = (node_memory – jvm_size) * circuit_breaker_limit, with the following parameters:

node_memory – The total memory of the instance.
jvm_size – The OpenSearch JVM heap size. This is set to half of the instance’s RAM, capped at approximately 32 GB.
circuit_breaker_limit – The native memory usage threshold for the circuit breaker. This is set to 0.5.

Total cluster memory estimation depends on total number of vector records and algorithms. HNSW and IVF have different memory requirements. You can refer to Memory Estimation for more details.

Number of dimensions

OpenSearch’s current dimension limit for the vector field knn_vector is 16,000 dimensions. Each dimension is represented as a 32-bit float. The more dimensions, the more memory you’ll need to index and search. The number of dimensions is usually determined by the embedding models that translate the entity to a vector. There are a lot of options to choose from when building your knn_vector field. To determine the correct methods and parameters to choose, refer to Choosing the right method.

Customer stories:

Amazon Music

Amazon Music is always innovating to provide customers with unique and personalized experiences. One of Amazon Music’s approaches to music recommendations is a remix of a classic Amazon innovation, item-to-item collaborative filtering, and vector databases. Using data aggregated based on user listening behavior, Amazon Music has created an embedding model that encodes music tracks and customer representations into a vector space where neighboring vectors represent tracks that are similar. 100 million songs are encoded into vectors, indexed into OpenSearch, and served across multiple geographies to power real-time recommendations. OpenSearch currently manages 1.05 billion vectors and supports a peak load of 7,100 vector queries per second to power Amazon Music recommendations.

The item-to-item collaborative filter continues to be among the most popular methods for online product recommendations because of its effectiveness at scaling to large customer bases and product catalogs. OpenSearch makes it easier to operationalize and further the scalability of the recommender by providing scale-out infrastructure and k-NN indexes that grow linearly with respect to the number of tracks and similarity search in logarithmic time.

The following figure visualizes the high-dimensional space created by the vector embedding.

A visualization of the vector encoding of Amazon Music entries in the large vector space

Brand protection at Amazon

Amazon strives to deliver the world’s most trustworthy shopping experience, offering customers the widest possible selection of authentic products. To earn and maintain our customers’ trust, we strictly prohibit the sale of counterfeit products, and we continue to invest in innovations that ensure only authentic products reach our customers. Amazon’s brand protection programs build trust with brands by accurately representing and completely protecting their brand. We strive to ensure that public perception mirrors the trustworthy experience we deliver. Our brand protection strategy focuses on four pillars: (1) Proactive Controls (2) Powerful Tools to Protect Brands (3) Holding Bad Actors Accountable (4) Protecting and Educating Customers. Amazon OpenSearch Service is a key part of Amazon’s Proactive Controls.

In 2022, Amazon’s automated technology scanned more than 8 billion attempted changes daily to product detail pages for signs of potential abuse. Our proactive controls found more than 99% of blocked or removed listings before a brand ever had to find and report it. These listings were suspected of being fraudulent, infringing, counterfeit, or at risk of other forms of abuse. To perform these scans, Amazon created tooling that uses advanced and innovative techniques, including the use of advanced machine learning models to automate the detection of intellectual property infringements in listings across Amazon’s stores globally. A key technical challenge in implementing such automated system is the ability to search for protected intellectual property within a vast billion-vector corpus in a fast, scalable and cost effective manner. Leveraging Amazon OpenSearch Service’s scalable vector database capabilities and distributed architecture, we successfully developed an ingestion pipeline that has indexed a total of 68 billion, 128- and 1024-dimension vectors into OpenSearch Service to enable brands and automated systems to conduct infringement detection, in real-time, through a highly available and fast (sub-second) search API.

Conclusion

Whether you’re building a generative AI solution, searching rich media and audio, or bringing more semantic search to your existing search-based application, OpenSearch is a capable vector database. OpenSearch supports a variety of engines, algorithms, and distance measures that you can employ to build the right solution. OpenSearch provides a scalable engine that can support vector search at low latency and up to billions of vectors. With OpenSearch and its vector DB capabilities, your users can find that 8-foot-blue couch easily, and relax by a cozy fire.

About the Authors

Jon Handler is a Senior Principal Solutions Architect at Amazon Web Services based in Palo Alto, CA. Jon works closely with OpenSearch and Amazon OpenSearch Service, providing help and guidance to a broad range of customers who have search and log analytics workloads that they want to move to the AWS Cloud. Prior to joining AWS, Jon’s career as a software developer included four years of coding a large-scale, eCommerce search engine. Jon holds a Bachelor of the Arts from the University of Pennsylvania, and a Master of Science and a Ph. D. in Computer Science and Artificial Intelligence from Northwestern University.

Jianwei Li is a Principal Analytics Specialist TAM at Amazon Web Services. Jianwei provides consultant service for customers to help customer design and build modern data platform. Jianwei has been working in big data domain as software developer, consultant and tech leader.

Dylan Tong is a Senior Product Manager at AWS. He works with customers to help drive their success on the AWS platform through thought leadership and guidance on designing well architected solutions. He has spent most of his career building on his expertise in data management and analytics by working for leaders and innovators in the space.

Vamshi Vijay Nakkirtha is a Software Engineering Manager working on the OpenSearch Project and Amazon OpenSearch Service. His primary interests include distributed systems. He is an active contributor to various plugins, like k-NN, GeoSpatial, and dashboard-maps.

Accelerate onboarding and seamless integration with ThoughtSpot using Amazon Redshift partner integration

2023-06-21 Antony Prasad Thevaraj

Post Syndicated from Antony Prasad Thevaraj original https://aws.amazon.com/blogs/big-data/accelerate-onboarding-and-seamless-integration-with-thoughtspot-using-amazon-redshift-partner-integration/

Amazon Redshift is a fast, petabyte-scale cloud data warehouse that makes it simple and cost-effective to analyze all of your data using standard SQL. Tens of thousands of customers today rely on Amazon Redshift to analyze exabytes of data and run complex analytical queries, making it the most widely used cloud data warehouse. You can run and scale analytics in seconds on all your data without having to manage your data warehouse infrastructure.

Today, we are excited to announce ThoughtSpot as a new BI partner available through Amazon Redshift partner integration. You can onboard with ThoughtSpot in minutes directly from the Amazon Redshift console and gain faster data-driven insights. Businesses typically look at ways to derive business insights. This is where modern analytics providers such as ThoughtSpot provide value. With its powerful AI-based search, live visualizations, and developer tools and APIs for sharing embedded analytics, ThoughtSpot democratizes access to data by providing self-service tools for all users.

In this post, you will learn how to integrate seamlessly with ThoughtSpot from the Amazon Redshift console. With the loosely coupled nature of the modern data stack, it’s simple to connect Amazon Redshift with ThoughtSpot. No data movement or replication is required.

ThoughtSpot: Live analytics for your modern data stack

Static dashboards cannot deliver consistent and reliable insights at the speed and global scale that customers demand. They lack the following:

Opportunities for collaboration
Discovery and reusability
Secure remote data and insight access
Rapid use case development with single-touch insight provisioning

ThoughtSpot empowers everyone to create, consume, and operationalize data-driven insights. ThoughtSpot consumer-grade search and AI technology delivers true self-service analytics that anyone can use, while its developer-friendly platform ThoughtSpot Everywhere makes it easy to build interactive data apps that integrate with your existing cloud provider.

As organizations increasingly move to the cloud, ThoughtSpot helps them quickly unlock value from their investment. ThoughtSpot’s simple search functionality enables you to easily ask and answer data questions in seconds to unearth impactful insights directly in Amazon Redshift. ThoughtSpot for AWS provides enterprises with more freedom and flexibility by eliminating the need to move data between cloud sources so that businesses can immediately benefit from data-driven decision-making.

ThoughtSpot is an AWS Data and Analytics Competency Partner with the Amazon Redshift Ready product designation. ThoughtSpot is also part of the Powered by Amazon Redshift program.

Integrate ThoughtSpot using Amazon Redshift partner integration

Complete the following steps to integrate ThoughtSpot with Amazon Redshift:

On the Amazon Redshift console, choose Clusters in the navigation pane.
Select your cluster and on the Actions menu, choose Add AWS Partner integration.

Alternatively, you can choose your individual cluster and on its details page, choose Add partner integration.

Select ThoughtSpot as your desired BI partner.
Choose Next.

Choose Add partner.

Select Continue to Setup.

On the Amazon Redshift connection details page, enter your Amazon Redshift database password for Password.
Choose Continue.

To connect to your Amazon Redshift cluster, make sure to enable the Publicly accessible option and allow list the ThoughtSpot IP in your Amazon Redshift cluster’s security group.

Select your desired tables and choose Update.
If a prompt appears, choose Update again.

After you have successfully integrated with ThoughtSpot, you will see an Active status in the Integrations section on the Amazon Redshift console.

Congratulations! You’re now ready to start visualizing data using ThoughtSpot. The following example shows you trends in sales growth YTD, current sales trends across regions, and a comparison between product type sales between the current year and the previous year. You can slice and dice the dataset based on the granularity defined by the user.

Partner feedback

“ThoughtSpot is thrilled to expand our long-time cooperation with AWS with the announcement of our Amazon Redshift partner integration. Leading organizations are already extracting value from their data using AI-powered analytics on Amazon Redshift, and today we are making it even more frictionless for Amazon Redshift users to launch ThoughtSpot’s free trial to solve real problems quickly.”

– Kuntal Vahalia, SVP of WW Partners & APAC

Conclusion

In this post, we discussed how Amazon Redshift partner integration provides a fast-onboarding experience and allows you to create valuable business insights by integrating with ThoughtSpot. ThoughtSpot enables you to unlock the value of your modern data stack by empowering your entire organization with live analytics and data search, while Amazon Redshift provides a modern data warehouse experience for you to manage analytics at scale.

If you’re an AWS Partner and would like to integrate your product into the Amazon Redshift console, contact [email protected] for additional information and guidance. This console partner integration functionality is available to new and existing customers at no additional cost. To get started and learn more, see Integrating Amazon Redshift with an AWS Partner.

About the Authors

Antony Prasad Thevaraj is a Sr. Partner Solutions Architect in Data and Analytics at AWS. He has over 12 years of experience as a Big Data Engineer, and has worked on building complex ETL and ELT pipelines for various business units.

Maneesh Sharma is a Senior Database Engineer at AWS with more than a decade of experience designing and implementing large-scale data warehouse and analytics solutions. He collaborates with various Amazon Redshift Partners and customers to drive better integration.

CISPE Code of Conduct Public Register now has 107 compliant AWS services

2023-06-19 Gokhan Akyuz

Post Syndicated from Gokhan Akyuz original https://aws.amazon.com/blogs/security/cispe-code-of-conduct-public-register-now-has-107-compliant-aws-services/

We continue to expand the scope of our assurance programs at Amazon Web Services (AWS) and are pleased to announce that 107 services are now certified as compliant with the Cloud Infrastructure Services Providers in Europe (CISPE) Data Protection Code of Conduct. This alignment with the CISPE requirements demonstrates our ongoing commitment to adhere to the heightened expectations for data protection by cloud service providers. AWS customers who use AWS certified services can be confident that their data is processed in adherence with the European Union’s General Data Protection Regulation (GDPR).

The CISPE Code of Conduct is the first pan-European, sector-specific code for cloud infrastructure service providers, which received a favorable opinion that it complies with the GDPR. It helps organizations across Europe accelerate the development of GDPR compliant, cloud-based services for consumers, businesses, and institutions.

The accredited monitoring body EY CertifyPoint evaluated AWS on January 26, 2023, and successfully audited 100 certified services. AWS added seven additional services to the current scope in June 2023. As of the date of this blog post, 107 services are in scope of this certification. The Certificate of Compliance that illustrates AWS compliance status is available on the CISPE Public Register. For up-to-date information, including when additional services are added, search the CISPE Public Register by entering AWS as the Seller of Record; or see the AWS CISPE page.

AWS strives to bring additional services into the scope of its compliance programs to help you meet your architectural and regulatory needs. If you have questions or feedback about AWS compliance with CISPE, reach out to your AWS account team.

To learn more about our compliance and security programs, see AWS Compliance Programs, AWS General Data Protection Regulation (GDPR) Center, and the EU data protection section of the AWS Cloud Security site. As always, we value your feedback and questions; reach out to the AWS Compliance team through the Contact Us page.

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.

Prevent account creation fraud with AWS WAF Fraud Control – Account Creation Fraud Prevention

2023-06-14 David MacDonald

Post Syndicated from David MacDonald original https://aws.amazon.com/blogs/security/prevent-account-creation-fraud-with-aws-waf-fraud-control-account-creation-fraud-prevention/

Threat actors use sign-up pages and login pages to carry out account fraud, including taking unfair advantage of promotional and sign-up bonuses, publishing fake reviews, and spreading malware.

In 2022, AWS released AWS WAF Fraud Control – Account Takeover Prevention (ATP) to help protect your application’s login page against credential stuffing attacks, brute force attempts, and other anomalous login activities.

Today, we introduce AWS WAF Fraud Control – Account Creation Fraud Prevention (ACFP) to help protect your application’s sign-up pages against fake account creation by detecting and blocking fake account creation requests.

You can now get comprehensive account fraud prevention by combining AWS WAF Account Creation Fraud Prevention and Account Takeover Prevention in your AWS WAF web access control list (web ACL). In this post, we will show you how to set up AWS WAF with ACFP for your application sign-up pages.

Overview of Account Creation Fraud Prevention for AWS WAF

ACFP helps protect your account sign-up pages by continuously monitoring requests for anomalous digital activity and automatically blocking suspicious requests based on request identifiers, behavioral analysis, and machine learning.

ACFP uses multiple capabilities to help detect and block fake account creation requests at the network edge before they reach your application. An automated vetting process for account creation requests uses rules based on reputation and risk to protect your registration pages against use of stolen credentials and disposable email domains. ACFP uses silent challenges and CAPTCHA challenges to identify and respond to sophisticated bots that are designed to actively evade detection.

ACFP is an AWS Managed Rules rule group. If you already use AWS WAF, you can configure ACFP without making architectural changes. On a single configuration page, you specify the registration page request inspection parameters that ACFP uses to detect fake account creation requests, including user identity, address, and phone number.

ACFP uses session tokens to separate legitimate client sessions from those that are not. These tokens allow ACFP to verify that the client applications that sign up for an account are legitimate. The AWS WAF Javascript SDK automatically generates these tokens during the frontend application load. We recommend that you integrate the AWS WAF Javascript SDK into your application, particularly for single-page applications where you don’t want page refreshes.

Walkthrough

In this walkthrough, we will show you how to set up ACFP for AWS WAF to help protect your account sign-up pages against account creation fraud. This walkthrough has two main steps:

Set up an AWS managed rule group for ACFP in the AWS WAF console.
Add the AWS WAF JavaScript SDK to your application pages.

Set up Account Creation Fraud Prevention

The first step is to set up ACFP by creating a web ACL or editing an existing one. You will add the ACFP rule group to this web ACL.

The ACFP rule group requires that you provide your registration page path, account creation path, and optionally the sign-up request fields that map to user identity, address, and phone number. ACFP uses this configuration to detect fraudulent sign-up requests and then decide an appropriate action, including blocking, challenging interstitial during the frontend application load, or requiring a CAPTCHA.

To set up ACFP

Open the AWS WAF console, and then do one of the following:
- To create a new web ACL, choose Create web ACL.
- To edit an existing web ACL, choose the name of the ACL.
On the Rules tab, for the Add Rules dropdown, select Add managed rule groups.
Add the Account creation fraud prevention rule set to the web ACL. Then, choose Edit to edit the rule configuration.
For Rule group configuration, provide the following information that the ACFP rule group requires to inspect account creation requests, as shown in Figure 1.
- For Registration page path, enter the path for the registration page website for your application.
- For Account creation path, enter the path of the endpoint that accepts the completed registration form.
- For Request inspection, select whether the endpoint that you specified in Account creation path accepts JSON or FORM_ENCODED payload types.
Figure 1: Account creation fraud prevention – Add account creation paths
(Optional): Provide Field names used in submitted registration forms, as shown in Figure 2. This helps ACFP more accurately identify requests that contain information that is considered stolen, or with a bad reputation. For each field, provide the relevant information that was included in your account creation request. For this walkthrough, we use JSON pointer syntax.

Figure 2: Account creation fraud prevention – Add optional field names
For Account creation fraud prevention rules, review the actions taken on each category of account creation fraud, and optionally customize them for your web applications. For this walkthrough, we leave the default rule action for each category set to the default action, as shown in Figure 3. If you want to customize the rules, you can select different actions for each category based on your application security needs:
- Allow — Allows the request to be sent to the protected resource.
- Block — Blocks the request, returning an HTTP 403 (Forbidden) response.
- Count — Allows the request to be sent to the protected resource while counting detections. The count shows you bot activity that is occurring without blocking or challenging. When you turn on rules for the first time, this information can help you see what the detections are, before you change the actions.
- CAPTCHA and Challenge — use CAPTCHA puzzles and silent challenges with tokens to track successful client responses.
Figure 3: Account creation fraud prevention – Select actions for each category
To save the configuration, choose Save.
To add the ACFP rule group to your web ACL, choose Add rules.
(Optional) Include additional rules in your web ACL, as described in the Best practices section that follows.
To create or edit your web ACL, proceed through the remaining configuration pages.

Add the AWS WAF JavaScript SDK to your application pages

The next step is to find the AWS WAF JavaScript SDK and add it to your application pages.

The SDK injects a token in the requests that you send to your protected resources. You must use the SDK integration to fully enable ACFP detections.

To add the SDK to your application pages

In the AWS WAF console, in the left navigation pane, choose Application integration.
Under Web ACLs that are enabled for application integration, choose the name of the web ACL that you created previously.
Under JavaScript SDK, copy the provided code snippet. This code snippet allows for creation of the cryptographic token in the background when the application loads for the first time. Figure 4 shows the SDK link.

Figure 4: Application integration – Add JavaScript SDK link to application pages
Add the code snippet to your pages. For example, paste the provided script code within the <head> section of the HTML. For ACFP, you only need to add the code snippet to the registration page, but if you are using other AWS WAF managed rules such as Account Takeover Protection or Targeted Bots on other pages, you will also need to add the code snippet to those pages.
To validate that your application obtains tokens correctly, load your application in a browser and verify that a cookie named aws-waf-token has been set during page load.

Review metrics

Now that you’ve set up the web ACL and integrated the SDK with the application, you can use the bot visualization dashboard in AWS WAF to review fraudulent account creation traffic patterns. ACFP rules emit metrics that correspond to their labels, helping you identify which rule within the ACFP rule group initiated an action. You can also use labels and rule actions to filter AWS WAF logs so that you can further examine a request.

To view AWS WAF metrics for the distribution

In the AWS WAF console, in the left navigation pane, select Web ACLs.
Select the web ACL for which ACFP is enabled, and then choose the Bot Control tab to view the metrics.
In the Filter metrics by dropdown, select Account creation fraud prevention to see the ACFP metrics for your web ACL.

Figure 5: Account creation fraud prevention – Review web ACL metrics

Best practices

In this section, we share best practices for your ACFP rule group setup.

Limit the requests that ACFP evaluates to help lower costs

ACFP evaluates web ACL rules in priority order and takes the action associated with the first rule that a request matches. Requests that match and are blocked by a rule will not be evaluated against lower priority rules. ACFP only evaluates an ACFP rule group if a request matches the registration and account creation URI paths that are specified in the configuration.

You will incur additional fees for requests that ACFP evaluates. To help reduce ACFP costs, use higher priority rules to block requests before the ACFP rule group evaluates them. For example, you can add a higher priority AWS Managed Rules IP reputation rule group to block account creation requests from bots and other threats before ACFP evaluates them. Rate-based rules with a higher priority than the ACFP rule group can help mitigate volumetric account creation attempts by limiting the number of requests that a single IP can make in a five-minute period. For further guidance on rate-based rules, see The three most important AWS WAF rate-based rules.

If you are using the AWS WAF Bot Control rule group, give it a higher priority than the ACFP rule group because it’s less expensive to evaluate.

Use SDK integration

ACFP requires the tokens that the SDK generates. The SDK can generate these tokens silently rather than requiring a redirect or CAPTCHA. Both AWS WAF Bot Control and AWS WAF Fraud Control use the same SDK if both rule groups are in the same web ACL.

These tokens have a default immunity time (otherwise knowns as a timeout) of 5 minutes, after which AWS WAF requires the client to be challenged again. You can use the AWS WAF integration fetch wrapper in your single-pane application to help ensure that the token retrieval completes before the client sends requests to your account creation API without requiring a page refresh. Alternatively, you can use the getToken operation if you are not using fetch.

You can continue to use the CAPTCHA JavaScript API instead if you’ve already integrated this into your application.

Use both ACFP and ATP for comprehensive account fraud prevention

You can help prevent account fraud for both sign-up and login pages by enabling the ATP rule group in the same web ACL as ACFP.

Test ACFP before you deploy it to production

Test and tune your ACFP implementation in a staging or testing environment to help avoid negatively impacting legitimate users. We recommend that you start by deploying your rules in count mode in production to understand potential impact to your traffic before switching them back to the default rule actions. Use the default ACFP rule group actions when you deploy the web ACL to production. For further guidance, see Testing and Deploying ACFP.

Pricing and availability

ACFP is available today on Amazon CloudFront and in 22 AWS Regions. For information on availability and pricing, see AWS WAF Pricing.

Conclusion

In this post, we showed you how to use ACFP to protect your application’s sign-up pages against fake account creation. You can now combine ACFP with ATP managed rules in a single web ACL for comprehensive account fraud prevention. For more information and to get started today, see the AWS WAF Developer Guide.

If you have feedback about this post, submit comments in the Comments section below. If you have questions about this post, start a new thread on the AWS Security, Identity, & Compliance re:Post or contact AWS Support.

Want more AWS Security news? Follow us on Twitter.

AWS Security Profile: Matthew Campagna, Senior Principal, Security Engineering, AWS Cryptography

2023-06-08 Roger Park

Post Syndicated from Roger Park original https://aws.amazon.com/blogs/security/security-profile-matthew-campagna-aws-cryptography/

In the AWS Security Profile series, we interview Amazon Web Services (AWS) thought leaders who help keep our customers safe and secure. This interview features Matt Campagna, Senior Principal, Security Engineering, AWS Cryptography, and re:Inforce 2023 session speaker, who shares thoughts on data protection, cloud security, post-quantum cryptography, and more. Matthew was first profiled on the AWS Security Blog in 2019. This is part 1 of 3 in a series of interviews with our AWS Cryptography team.

What do you do in your current role and how long have you been at AWS?

I started at Amazon in 2013 as the first cryptographer at AWS. Today, my focus is on the cryptographic security of our customers’ data. I work across AWS to make sure that our cryptographic engineering meets our most sensitive customer needs. I lead our migration to quantum-resistant cryptography, and help make privacy-preserving cryptography techniques part of our security model.

How did you get started in the data protection and cryptography space? What about it piqued your interest?

I first learned about public-key cryptography (for example, RSA) during a math lesson about group theory. I found the mathematics intriguing and the idea of sending secret messages using only a public value astounding. My undergraduate and graduate education focused on group theory, and I started my career at the National Security Agency (NSA) designing and analyzing cryptologics. But what interests me most about cryptography is its ability to enable business by reducing risks. I look at cryptography as a financial instrument that affords new business cases, like e-commerce, digital currency, and secure collaboration. What enables Amazon to deliver for our customers is rooted in cryptography; our business exists because cryptography enables trust and confidentiality across the internet. I find this the most intriguing aspect of cryptography.

AWS has invested in the migration to post-quantum cryptography by contributing to post-quantum key agreement and post-quantum signature schemes to protect the confidentiality, integrity, and authenticity of customer data. What should customers do to prepare for post-quantum cryptography?

Our focus at AWS is to help ensure that customers can migrate to post-quantum cryptography as fast as prudently possible. This work started with inventorying our dependencies on algorithms that aren’t known to be quantum-resistant, like integer-factorization-based cryptography, and discrete-log-based cryptography, like ECC. Customers can rely on AWS to assist with transitioning to post-quantum cryptography for their cloud computing needs.

We recommend customers begin inventorying their dependencies on algorithms that aren’t quantum-resistant, and consider developing a migration plan, to understand if they can migrate directly to new post-quantum algorithms or if they should re-architect them. For the systems that are provided by a technology provider, customers should ask what their strategy is for post-quantum cryptography migration.

AWS offers post-quantum TLS endpoints in some security services. Can you tell us about these endpoints and how customers can use them?

Our open source TLS implementation, s2n-TLS, includes post-quantum hybrid key exchange (PQHKEX) in its mainline. It’s deployed everywhere that s2n is deployed. AWS Key Management Service, AWS Secrets Manager, and AWS Certificate Manager have enabled PQHKEX cipher suites in our commercial AWS Regions. Today customers can use the AWS SDK for Java 2.0 to enable PQHKEX on their connection to AWS, and on the services that also have it enabled, they will negotiate a post-quantum key exchange method. As we enable these cipher suites on additional services, customers will also be able to connect to these services using PQHKEX.

You are a frequent contributor to the Amazon Science Blog. What were some of your recent posts about?

In 2022, we published a post on preparing for post-quantum cryptography, which provides general information on the broader industry development and deployment of post-quantum cryptography. The post links to a number of additional resources to help customers understand post-quantum cryptography. The AWS Post-Quantum Cryptography page and the Science Blog are great places to start learning about post-quantum cryptography.

We also published a post highlighting the security of post-quantum hybrid key exchange. Amazon believes in evidencing the cryptographic security of the solutions that we vend. We are actively participating in cryptographic research to validate the security that we provide in our services and tools.

What’s been the most dramatic change you’ve seen in the data protection and post-quantum cryptography landscape since we talked to you in 2019?

Since 2019, there have been two significant advances in the development of post-quantum cryptography.

First, the National Institute of Standards and Technology (NIST) announced their selection of PQC algorithms for standardization. NIST expects to finish the standardization of a post-quantum key encapsulation mechanism (Kyber) and digital signature scheme (Dilithium) by 2024 as part of the Federal Information Processing Standard (FIPS). NIST will also work on standardization of two additional signature standards (FALCON and SPHINCS+), and continue to consider future standardization of the key encapsulation mechanisms BIKE, HQC, and Classical McEliece.

Second, the NSA announced their Commercial National Security Algorithm (CNSA) Suite 2.0, which includes their timelines for National Security Systems (NSS) to migrate to post-quantum algorithms. The NSA will begin preferring post-quantum solutions in 2025 and expect that systems will have completed migration by 2033. Although this timeline might seem far away, it’s an aggressive strategy. Experience shows that it can take 20 years to develop and deploy new high-assurance cryptographic algorithms. If technology providers are not already planning to migrate their systems and services, they will be challenged to meet this timeline.

What makes cryptography exciting to you?

Cryptography is a dynamic area of research. In addition to the business applications, I enjoy the mathematics of cryptography. The state-of-the-art is constantly progressing in terms of new capabilities that cryptography can enable, and the potential risks to existing cryptographic primitives. This plays out in the public sphere of cryptographic research across the globe. These advancements are made public and are accessible for companies like AWS to innovate on behalf of our customers, and protect our systems in advance of the development of new challenges to our existing crypto algorithms. This is happening now as we monitor the advancements of quantum computing against our ability to define and deploy new high-assurance quantum-resistant algorithms. For me, it doesn’t get more exciting than this.

Where do you see the cryptography and post-quantum cryptography space heading to in the future?

While NIST transitions from their selection process to standardization, the broader cryptographic community will be more focused on validating the cryptographic assurances of these proposed schemes for standardization. This is a critical part of the process. I’m optimistic that we will enter 2025 with new cryptographic standards to deploy.

There is a lot of additional cryptographic research and engineering ahead of us. Applying these new primitives to the cryptographic applications that use classical asymmetric schemes still needs to be done. Some of this work is happening in parallel, like in the IETF TLS working group, and in the ETSI Quantum-Safe Cryptography Technical Committee. The next five years should see the adoption of PQHKEX in protocols like TLS, SSH, and IKEv2 and certification of new FIPS hardware security modules (HSMs) for establishing new post-quantum, long-lived roots of trust for code-signing and entity authentication.

I expect that the selected primitives for standardization will also be used to develop novel uses in fields like secure multi-party communication, privacy preserving machine learning, and cryptographic computing.

With AWS re:Inforce 2023 around the corner, what will your session focus on? What do you hope attendees will take away from your session?

Session DAP302 – “Post-quantum cryptography migration strategy for cloud services” is about the challenge quantum computers pose to currently used public-key cryptographic algorithms and how the industry is responding. Post-quantum cryptography (PQC) offers a solution to this challenge, providing security to help protect against quantum computer cybersecurity events. We outline current efforts in PQC standardization and migration strategies. We want our customers to leave with a better understanding of the importance of PQC and the steps required to migrate to it in a cloud environment.

Is there something you wish customers would ask you about more often?

The question I am most interested in hearing from our customers is, “when will you have a solution to my problem?” If customers have a need for a novel cryptographic solution, I’m eager to try to solve that with them.

How about outside of work, any hobbies?

My main hobbies outside of work are biking and running. I wish I was as consistent attending to my hobbies as I am to my work desk. I am happier being able to run every day for a constant speed and distance as opposed to running faster or further tomorrow or next week. Last year I was fortunate enough to do the Cycle Oregon ride. I had registered for it twice before without being able to find the time to do it.

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

Want more AWS Security news? Follow us on Twitter.

2023 ISO and CSA STAR certificates now available with 8 new services and 1 new Region

2023-06-08 Atul Patil

Post Syndicated from Atul Patil original https://aws.amazon.com/blogs/security/2023-iso-and-csa-star-certificates-now-available-with-8-new-services-and-1-new-region/

Amazon Web Services (AWS) successfully completed a special onboarding audit with no findings for ISO 9001, 27001, 27017, 27018, 27701, and 22301, and Cloud Security Alliance (CSA) STAR CCM v4.0. Ernst and Young Certify Point auditors conducted the audit and reissued the certificates on May 23, 2023. The objective of the audit was to assess the level of compliance with the requirements of the applicable international standards.

We added eight additional AWS services and one additional AWS Region to the scope of this special onboarding audit. The following are the eight additional services:

The additional Region is Asia Pacific (Melbourne).

For a full list of AWS services that are certified under ISO and CSA Star, see the AWS ISO and CSA STAR Certified page. Customers can also access the certifications in the console through AWS Artifact.

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.

Our commitment to shared cybersecurity goals

2023-06-07 Mark Ryland

Post Syndicated from Mark Ryland original https://aws.amazon.com/blogs/security/our-commitment-to-shared-cybersecurity-goals/

The United States Government recently launched its National Cybersecurity Strategy. The Strategy outlines the administration’s ambitious vision for building a more resilient future, both in the United States and around the world, and it affirms the key role cloud computing plays in realizing this vision.

Amazon Web Services (AWS) is broadly committed to working with customers, partners, and governments such as the United States to improve cybersecurity. That longstanding commitment aligns with the goals of the National Cybersecurity Strategy. In this blog post, we will summarize the Strategy and explain how AWS will help to realize its vision.

The Strategy identifies two fundamental shifts in how the United States allocates cybersecurity roles, responsibilities, and resources. First, the Strategy calls for a shift in cybersecurity responsibility away from individuals and organizations with fewer resources, toward larger technology providers that are the most capable and best-positioned to be successful. At AWS, we recognize that our success and scale bring broad responsibility. We are committed to improving cybersecurity outcomes for our customers, our partners, and the world at large.

Second, the Strategy calls for realigning incentives to favor long-term investments in a resilient future. As part of our culture of ownership, we are long-term thinkers, and we don’t sacrifice long-term value for short-term results. For more than fifteen years, AWS has delivered security, identity, and compliance services for millions of active customers around the world. We recognize that we operate in a complicated global landscape and dynamic threat environment that necessitates a dynamic approach to security. Innovation and long-term investments have been and will continue to be at the core of our approach, and we continue to innovate to build and improve on our security and the services we offer customers.

AWS is working to enhance cybersecurity outcomes in ways that align with each of the Strategy’s five pillars:

Defend Critical Infrastructure — Customers, partners, and governments need confidence that they are migrating to and building on a secure cloud foundation. AWS is architected to have the most flexible and secure cloud infrastructure available today, and our customers benefit from the data centers, networks, custom hardware, and secure software layers that we have built to satisfy the requirements of the most security-sensitive organizations. Our cloud infrastructure is secure by design and secure by default, and our infrastructure and services meet the high bar that the United States Government and other customers set for security.
Disrupt and Dismantle Threat Actors — At AWS, our paramount focus on security leads us to implement important measures to prevent abuse of our services and products. Some of the measures we undertake to deter, detect, mitigate, and prevent abuse of AWS products include examining new registrations for potential fraud or identity falsification, employing service-level containment strategies when we detect unusual behavior, and helping protect critical systems and sensitive data against ransomware. Amazon is also working with government to address these threats, including by serving as one of the first members of the Joint Cyber Defense Collaborative (JCDC). Amazon is also co-leading a study with the President’s National Security Telecommunications Advisory Committee on addressing the abuse of domestic infrastructure by foreign malicious actors.
Shape Market Forces to Drive Security and Resilience — At AWS, security is our top priority. We continuously innovate based on customer feedback, which helps customer organizations to accelerate their pace of innovation while integrating top-tier security architecture into the core of their business and operations. For example, AWS co-founded the Open Cybersecurity Schema Framework (OCSF) project, which facilitates interoperability and data normalization between security products. We are contributing to the quality and safety of open-source software both by direct contributions to open-source projects and also by an initial commitment of $10 million in a variety of open-source security improvement projects in and through the Open Source Security Foundation (OpenSSF).
Invest in a Resilient Future — Cybersecurity skills training, workforce development, and education on next-generation technologies are essential to addressing cybersecurity challenges. That’s why we are making significant investments to help make it simpler for people to gain the skills they need to grow their careers, including in cybersecurity. Amazon is committing more than $1.2 billion to provide no-cost education and skills training opportunities to more than 300,000 of our own employees in the United States, to help them secure new, high-growth jobs. Amazon is also investing hundreds of millions of dollars to provide no-cost cloud computing skills training to 29 million people around the world. We will continue to partner with the Cybersecurity and Infrastructure Security Agency (CISA) and others in government to develop the cybersecurity workforce.
Forge International Partnerships to Pursue Shared Goals — AWS is working with governments around the world to provide innovative solutions that advance shared goals such as bolstering cyberdefenses and combating security risks. For example, we are supporting international forums such as the Organization of American States to build security capacity across the hemisphere. We encourage the administration to look toward internationally recognized, risk-based cybersecurity frameworks and standards to strengthen consistency and continuity of security among interconnected sectors and throughout global supply chains. Increased adoption of these standards and frameworks, both domestically and internationally, will mitigate cyber risk while facilitating economic growth.

AWS shares the Biden administration’s cybersecurity goals and is committed to partnering with regulators and customers to achieve them. Collaboration between the public sector and industry has been central to US cybersecurity efforts, fueled by the recognition that the owners and operators of technology must play a key role. As the United States Government moves forward with implementation of the National Cybersecurity Strategy, we look forward to redoubling our efforts and welcome continued engagement with all stakeholders—including the United States Government, our international partners, and industry collaborators. Together, we can address the most difficult cybersecurity challenges, enhance security outcomes, and build toward a more secure and resilient future.

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

Want more AWS Security news? Follow us on Twitter.

Updated AWS Ramp-Up Guide available for security, identity, and compliance

2023-06-06 Anna McAbee

Post Syndicated from Anna McAbee original https://aws.amazon.com/blogs/security/updated-aws-ramp-up-guide-available-for-security-identity-and-compliance/

To support our customers in securing their Amazon Web Services (AWS) environment, AWS offers digital training, whitepapers, blog posts, videos, workshops, and documentation to learn about security in the cloud.

The AWS Ramp-Up Guide: Security is designed to help you quickly learn what is most important to you when it comes to security, identity, and compliance. The Ramp-Up Guide helps you get started with learning cloud foundations and then provides you with options for building skills in various security domains.

Recently, we have updated the AWS Ramp-Up Guide: Security. In this post, we will highlight some of the changes and discuss how to use the new guide.

Update highlights

Based on customer feedback, new service and feature releases, and our experience helping customers, we’ve updated the majority of the guide with new content. Some highlights of the new version include:

Focus on AWS security digital trainings — The new Ramp-Up Guide for security focuses on digital trainings provided by AWS Skill Builder. AWS Skill Builder is a learning center for AWS customers and partners to build cloud skills through digital trainings, self-paced labs, and other course types. AWS Skill Builder has a variety of AWS security content to help customers understand concepts and gain hands-on experience with AWS security.
Security focus areas — Because there are different roles and focuses within cybersecurity, we created sections for different focus areas of AWS security, including threat detection and incident response (TD/IR), compliance, data protection, and more. A Security Operations Center (SOC) analyst, for example, can choose to focus on TD/IR training, which is most relevant for that role.
Extensive additional resources — For each focus area, we added new resources, including whitepapers, blogs, re:Invent videos, and workshops. Customers can use these additional resources to supplement the AWS Skill Builder courses and labs.

How to use the new guide

The AWS Ramp-Up Guide: Security is designed to take you all the way from cloud foundations to the AWS Certified Security – Specialty certification. The guide takes the latest in digital training available from AWS Skill Builder and augments that with the latest resources aligned to foundational concepts and specialized areas within cloud security. Although you are free to use the learnings in any order, if you are new to the cloud, we recommend the following steps:

Sign up for your free AWS Skill Builder account, which provides you with more than 600 digital courses.

Note: You can optionally buy an AWS Skill Builder subscription if you’d like to complete the self-paced labs. See Pricing options for AWS Skill Builder for more details.
Review the “Learn the fundamentals of the AWS Cloud” section of the Ramp-Up Guide, choose a course name under Learning Resource, and search for that course in Skill Builder. If you are unsure of which course to start with, we recommend that you begin with “AWS Cloud Practitioner Essentials.”
After you’ve completed the “Learn the fundamentals of the AWS Cloud” section, proceed to the “AWS Cloud Security Fundamentals” section begin your security training.
After you complete the Security Fundamentals section, review the specialized security focus areas in the Ramp-Up Guide, choose a focus area, and complete the training items within that focus area.
After you’ve completed the training specific to your focus area, explore other focus areas beyond the scope of your immediate role. Security often requires knowledge across domains and focus areas, so we encourage you to explore the security focus areas beyond the immediate scope of your role.
Review the “Putting it all together” section to prepare for the AWS Certified Security – Specialization certification.
Go build, securely!

More information

For more information and to get started, see the updated AWS Ramp-Up Guide: Security.

We greatly value feedback and contributions from our community. To share your thoughts and insights about the AWS Ramp-Up Guide: Security and your experience using it, and what you want to see in future versions, please contact [email protected].

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

Want more AWS Security news? Follow us on Twitter.

New eBook: 5 Keys to Secure Enterprise Messaging

2023-06-02 Anne Grahn

Post Syndicated from Anne Grahn original https://aws.amazon.com/blogs/security/new-ebook-5-keys-to-secure-enterprise-messaging/

AWS is excited to announce a new eBook, 5 Keys to Secure Enterprise Messaging. The new eBook includes best practices for addressing the security and compliance risks associated with messaging apps.

An estimated 3.09 billion mobile phone users access messaging apps to communicate, and this figure is projected to grow to 3.51 billion users in 2025.

Legal and regulatory requirements for data protection, privacy, and data retention have made protecting business communications a priority for organizations across the globe. Although consumer messaging apps are convenient and support real-time communication with colleagues, customers, and partners, they often lack the robust security and administrative controls many businesses require.

The eBook details five keys to secure enterprise messaging that balance people, process, and technology.

We encourage you to read the eBook, and learn about:

Establishing messaging policies and guidelines that are effective for your workforce
Training employees to use messaging apps in a way that doesn’t increase organizational risk
Building a security-first culture
Using true end-to-end encryption (E2EE) to secure communications
Retaining data to help meet requirements, without exposing it to outside parties

Download 5 Keys to Secure Enterprise Messaging.

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

Want more AWS Security news? Follow us on Twitter.

Announcing the AWS Blueprint for Ransomware Defense

2023-06-01 Jeremy Ware

Post Syndicated from Jeremy Ware original https://aws.amazon.com/blogs/security/announcing-the-aws-blueprint-for-ransomware-defense/

In this post, Amazon Web Services (AWS) introduces the AWS Blueprint for Ransomware Defense, a new resource that both enterprise and public sector organizations can use to implement preventative measures to protect data from ransomware events. The AWS Blueprint for Ransomware Defense provides a mapping of AWS services and features as they align to aspects of the Center for Internet Security (CIS) Critical Security Controls (CIS Controls). This information can be used to help customers assess and protect their data from ransomware events.

The following is background on ransomware, CIS, and the initiatives that led to the publication of this new blueprint.

The Ransomware Task Force

In April of 2021, the U.S. government launched the Ransomware Task Force (RTF), which has the mission of uniting key stakeholders across industry, government, and civil society to create new solutions, break down silos, and find effective new methods of countering the ransomware threat. The RTF has since launched several progress reports with specific recommendations, including the development of the RTF Blueprint for Ransomware Defense, which provides a framework with practical steps to mitigate, respond to, and recover from ransomware. AWS is a member of the RTF, and we have taken action to create our own AWS Blueprint for Ransomware Defense that maps actionable and foundational security controls to AWS services and features that customers can use to implement those controls. The AWS Blueprint for Ransomware Defense is based on the CIS Controls framework.

Center for Internet Security

The Center for Internet Security (CIS) is a community-driven nonprofit, globally recognized for establishing best practices for securing IT systems and data. To help establish foundational defense mechanisms, a subset of the CIS Critical Security Controls (CIS Controls) have been identified as important first steps in the implementation of a robust program to prevent, respond to, and recover from ransomware events. This list of controls was established to provide safeguards against the most impactful and well-known internet security issues. The controls have been further prioritized into three implementation groups (IGs), to help guide their implementation. IG1, considered “essential cyber hygiene,” provides foundational safeguards. IG2 builds on IG1 by including the controls in IG1 plus a number of additional considerations. Finally, IG3 includes the controls in IG1 and IG2, with an additional layer of controls that protect against more sophisticated security issues.

CIS recommends that organizations use the CIS IG1 controls as basic preventative steps against ransomware events. We’ve produced a mapping of AWS services that can help you implement aspects of these controls in your AWS environment. Ransomware is a complex event, and the best course of action to mitigate risk is to apply a thoughtful strategy of defense in depth. The mitigations and controls outlined in this mapping document are general security best practices, but are a non-exhaustive list.

Because data is often vital to the operation of mission-critical services, ransomware can severely disrupt business processes and applications that depend on this data. For this reason, many organizations are looking for effective security controls that will improve their security posture against these types of events. We hope you find the information in the AWS Blueprint for Ransomware Defense helpful and incorporate it as a tool to provide additional layers of security to help keep your data safe.

Let us know if you have any feedback through the AWS Security Contact Us page. Please reach out if there is anything we can do to add to the usefulness of the blueprint or if you have any additional questions on security and compliance. You can find more information from the IST (Institute for Security and Technology) describing ransomware and how to protect yourself on the IST website.

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

Want more AWS Security news? Follow us on Twitter.

Updated whitepaper available: Architecting for PCI DSS Segmentation and Scoping on AWS

2023-06-01 Ted Tanner

Post Syndicated from Ted Tanner original https://aws.amazon.com/blogs/security/updated-whitepaper-available-architecting-for-pci-dss-segmentation-and-scoping-on-aws/

Amazon Web Services (AWS) has re-published the whitepaper Architecting for PCI DSS Scoping and Segmentation on AWS to provide guidance on how to properly define the scope of your Payment Card Industry (PCI) Data Security Standard (DSS) workloads that are running in the AWS Cloud. The whitepaper has been refreshed to include updated AWS best practices and technologies, and updates that are applicable to the new PCI DSS v4.0 requirements. The whitepaper looks at how to define segmentation boundaries between your in-scope and out-of-scope resources by using cloud-based AWS services.

The whitepaper is intended for engineers and solution builders, but it also serves as a guide for Qualified Security Assessors (QSAs) and internal security assessors (ISAs) to better understand the different segmentation controls that are available within AWS products and services, along with associated scoping considerations.

Compared to on-premises environments, software-defined networking on AWS transforms the scoping process for applications by providing additional segmentation controls beyond network segmentation. Thoughtful design of your applications and selection of security-impacting services for implementing your required controls can reduce the number of systems and services in your cardholder data environment (CDE).

The whitepaper is based on the PCI Council’s Information Supplement: Guidance for PCI DSS Scoping and Network Segmentation.

If you have questions or want to learn more, contact your account representative, or leave a comment below.

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

AWS Security Profile: Ritesh Desai, GM, AWS Secrets Manager

2023-05-31 Roger Park

Post Syndicated from Roger Park original https://aws.amazon.com/blogs/security/aws-security-profile-ritesh-desai-gm-aws-secrets-manager/

In the AWS Security Profile series, we interview Amazon Web Services (AWS) thought leaders who help keep our customers safe and secure. This interview features Ritesh Desai, General Manager, AWS Secrets Manager, and re:Inforce 2023 session speaker, who shares thoughts on data protection, cloud security, secrets management, and more.

What do you do in your current role and how long have you been at AWS?

I’ve been in the tech industry for more than 20 years and joined AWS about three years ago. Currently, I lead our Secrets Management organization, which includes the AWS Secrets Manager service.

How did you get started in the data protection and secrets management space? What about it piqued your interest?

I’ve always been excited at the prospect of solving complex customer problems with simple technical solutions. Working across multiple small to large organizations in the past, I’ve seen similar challenges with secret sprawl and lack of auditing and monitoring tools. Centralized secrets management is a challenge for customers. As organizations evolve from start-up to enterprise level, they can end up with multiple solutions across organizational units to manage their secrets.

Being part of the Secrets Manager team gives me the opportunity to learn about our customers’ unique needs and help them protect access to their most sensitive digital assets in the cloud, at scale.

Why does secrets management matter to customers today?

Customers use secrets like database passwords and API keys to protect their most sensitive data, so it’s extremely important for them to invest in a centralized secrets management solution. Through secrets management, customers can securely store, retrieve, rotate, and audit secrets.

What’s been the most dramatic change you’ve seen in the data protection and secrets management space?

Secrets management is becoming increasingly important for customers, but customers now have to deal with complex environments that include single cloud providers like AWS, multi-cloud setups, hybrid (cloud and on-premises) environments, and only on-premises instances.

Customers tell us that they want centralized secrets management solutions that meet their expectations across these environments. They have two distinct goals here. First, they want a central secrets management tool or service to manage their secrets. Second, they want their secrets to be available closer to the applications where they’re run. IAM Roles Anywhere provides a secure way for on-premises servers to obtain temporary AWS credentials and removes the need to create and manage long-term AWS credentials. Now, customers can use IAM Roles Anywhere to access their Secrets Manager secrets from workloads running outside of AWS. Secrets Manager also launched a program in which customers can manage secrets in third-party secrets management solutions to replicate secrets to Secrets Manager for their AWS workloads. We’re continuing to invest in these areas to make it simpler for customers to manage their secrets in their tools of choice, while providing access to their secrets closer to where their applications are run.

With AWS re:Inforce 2023 around the corner, what will your session focus on? What do you hope attendees will take away from your session?

I’m speaking in a session called “Using AWS data protection services for innovation and automation” (DAP305) alongside one of our senior security specialist solutions architects on the topic of secrets management at scale. In the session, we’ll walk through a sample customer use case that highlights how to use data protection services like AWS Key Management Service (AWS KMS), AWS Private Certificate Authority (AWS Private CA), and Secrets Manager to help build securely and help meet organizational security and compliance expectations. Attendees will walk away with a clear picture of the services that AWS offers to protect sensitive data, and how they can use these services together to protect secrets at scale.

I also encourage folks to check out the other sessions in the data protection track.

Where do you see the secrets management space heading in the future?

Traditionally, secrets management was addressed after development, rather than being part of the design and development process. This placement created an inherent clash between development teams who wanted to put the application in the hands of end users, and the security admins who wanted to verify that the application met security expectations. This resulted in longer timelines to get to market. Involving security in the mix only after development is complete, is simply too late. Security should enable business, not restrict it.

Organizations are slowly adopting the culture that “Security is everyone’s responsibility.” I expect more and more organizations will take the step to “shift-left” and embed security early in the development lifecycle. In the near future, I expect to see organizations prioritize the automation of security capabilities in the development process to help detect, remediate, and eliminate potential risks by taking security out of human hands.

Is there something you wish customers would ask you about more often?

I’m always happy to talk to customers to help them think through how to incorporate secure-by-design in their planning process. There are many situations where decisions could end up being expensive to reverse. AWS has a lot of experience working across a multitude of use cases for customers as they adopt secrets management solutions. I’d love to talk more to customers early in their cloud adoption journey, about the best practices that they should adopt and potential pitfalls to avoid, when they make decisions about secrets management and data protection.

How about outside of work—any hobbies?

I’m an avid outdoors person, and living in Seattle has given me and my family the opportunity to trek and hike through the beautiful landscapes of the Pacific Northwest. I’ve also been a consistent Tough Mudder-er for the last 5 years. The other thing that I spend my time on is working as an amateur actor for a friend’s nonprofit theater production, helping in any way I can.

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

Want more AWS Security news? Follow us on Twitter.

Amazon Security Lake is now generally available

2023-05-30 Ross Warren

Post Syndicated from Ross Warren original https://aws.amazon.com/blogs/security/amazon-security-lake-is-now-generally-available/

Today we are thrilled to announce the general availability of Amazon Security Lake, first announced in a preview release at 2022 re:Invent. Security Lake centralizes security data from Amazon Web Services (AWS) environments, software as a service (SaaS) providers, on-premises, and cloud sources into a purpose-built data lake that is stored in your AWS account. With Open Cybersecurity Schema Framework (OCSF) support, the service normalizes and combines security data from AWS and a broad range of security data sources. This helps provide your team of analysts and security engineers with broad visibility to investigate and respond to security events, which can facilitate timely responses and helps to improve your security across multicloud and hybrid environments.

Figure 1 shows how Security Lake works, step by step. In this post, we discuss these steps, highlight some of the most popular use cases for Security Lake, and share the latest enhancements and updates that we have made since the preview launch.

Figure 1: How Security Lake works

Target use cases

In this section, we showcase some of the use cases that customers have found to be most valuable while the service was in preview.

Facilitate your security investigations with elevated visibility

Amazon Security Lake helps to streamline security investigations by aggregating, normalizing, and optimizing data storage in a single security data lake. Security Lake automatically normalizes AWS logs and security findings to the OCSF schema. This includes AWS CloudTrail management events, Amazon Virtual Private Cloud (Amazon VPC) Flow Logs, Amazon Route 53 Resolver query logs, and AWS Security Hub security findings from Amazon security services, including Amazon GuardDuty, Amazon Inspector, and AWS IAM Access Analyzer, as well as security findings from over 50 partner solutions. By having security-related logs and findings in a centralized location, and in the same format, Security Operations teams can streamline their process and devote more time to investigating security issues. This centralization reduces the need to spend valuable time collecting and normalizing logs into a specific format.

Figure 2 shows the Security Lake activation page, which presents users with options to enable log sources, AWS Regions, and accounts.

Figure 2: Security Lake activation page with options to enable log sources, Regions, and accounts

Figure 3 shows another section of the Security Lake activation page, which presents users with options to set rollup Regions and storage classes.

Figure 3: Security Lake activation page with options to select a rollup Region and set storage classes

Simplify your compliance monitoring and reporting

With Security Lake, customers can centralize security data into one or more rollup Regions, which can help teams to simplify their regional compliance and reporting obligations. Teams often face challenges when monitoring for compliance across multiple log sources, Regions, and accounts. By using Security Lake to collect and centralize this evidence, security teams can significantly reduce the time spent on log discovery and allocate more time towards compliance monitoring and reporting.

Analyze multiple years of security data quickly

Security Lake offers integration with third-party security services such as security information and event management (SIEM) and extended detection and response (XDR) tools, as well as popular data analytics services like Amazon Athena and Amazon OpenSearch Service to quickly analyze petabytes of data. This enables security teams to gain deep insights into their security data and take nimble measures to help protect their organization. Security Lake helps enforce least-privilege controls for teams across organizations by centralizing data and implementing robust access controls, automatically applying policies that are scoped to the required subscribers and sources. Data custodians can use the built-in features to create and enforce granular access controls, such as to restrict access to the data in the security lake to only those who require it.

Figure 4 depicts the process of creating a data access subscriber within Security Lake.

Figure 4: Creating a data access subscriber in Security Lake

Unify security data management across hybrid environments

The centralized data repository in Security Lake provides a comprehensive view of security data across hybrid and multicloud environments, helping security teams to better understand and respond to threats. You can use Security Lake to store security-related logs and data from various sources, including cloud-based and on-premises systems, making it simpler to collect and analyze security data. Additionally, by using automation and machine learning solutions, security teams can help identify anomalies and potential security risks more efficiently. This can ultimately lead to better risk management and enhance the overall security posture for the organization. Figure 5 illustrates the process of querying AWS CloudTrail and Microsoft Azure audit logs simultaneously by using Amazon Athena.

Figure 5: Querying AWS CloudTrail and Microsoft Azure audit logs together in Amazon Athena

Updates since preview launch

Security Lake automatically normalizes logs and events from natively supported AWS services to the OCSF schema. With the general availability release, Security Lake now supports the latest version of OCSF, which is version 1 rc2. CloudTrail management events are now normalized into three distinct OCSF event classes: Authentication, Account Change, and API Activity.

We made various improvements to resource names and schema mapping to enhance the usability of logs. Onboarding is made simpler with automated AWS Identity and Access Management (IAM) role creation from the console. Additionally, you have the flexibility to collect CloudTrail sources independently including management events, Amazon Simple Storage Service (Amazon S3) data events, and AWS Lambda events.

To enhance query performance, we made a transition from hourly to daily time partitioning in Amazon S3, resulting in faster and more efficient data retrieval. Also, we added Amazon CloudWatch metrics to enable proactive monitoring of your log ingestion process to facilitate the identification of collection gaps or surges.

New Security Lake account holders are eligible for a 15-day free trial in supported Regions. Security Lake is now generally available in the following AWS Regions: US East (Ohio), US East (N. Virginia), US West (Oregon), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Europe (Frankfurt), Europe (Ireland), Europe (London), and South America (São Paolo).

Ecosystem integrations

We have expanded our support for third-party integrations and have added 23 new partners. This includes 10 source partners — Aqua Security, Claroty, Confluent, Darktrace, ExtraHop, Gigamon, Sentra, Torq, Trellix, and Uptycs — enabling them to send data directly to Security Lake. Additionally, we have integrated with nine new subscribing partners — ChaosSearch, New Relic, Ripjar, SOC Prime, Stellar Cyber, Swimlane, Tines, Torq, and Wazuh. We have also established six new services partners, including Booz Allen Hamilton, CMD Solutions, part of Mantel Group, Infosys, Insbuilt, Leidos, and Tata Consultancy Services.

In addition, Security Lake supports third-party sources that provide OCSF security data. Notable partners include Barracuda Networks, Cisco, Cribl, CrowdStrike, CyberArk, Lacework, Laminar, NETSCOUT, Netskope, Okta, Orca, Palo Alto Networks, Ping Identity, Tanium, The Falco Project, Trend Micro, Vectra AI, VMware, Wiz, and Zscaler. We have integrated with various third-party security, automation, and analytics tools. This includes Datadog, IBM, Rapid7, SentinelOne, Splunk, Sumo Logic, and Trellix. Lastly, we have partnered with service partners such as Accenture, Eviden , Deloitte, DXC Technology, Kyndryl, PwC, and Wipro, that can work with you and Security Lake to deliver comprehensive solutions.

Get help from AWS Professional Services

The AWS Professional Services organization is a global team of experts that can help customers realize their desired business outcomes when using AWS. Our teams of data architects and security engineers engage with customer Security, IT, and business leaders to develop enterprise solutions. We follow current recommendations to support customers in their journey to integrate data into Security Lake. We integrate ready-built data transformations, visualizations, and AI/machine learning (ML) workflows that help Security Operations teams rapidly realize value. If you are interested in learning more, reach out to your AWS Professional Services account representative.

Summary

We invite you to explore the benefits of using Amazon Security Lake by taking advantage of our 15-day free trial and providing your feedback on your experiences, use cases, and solutions. We have several resources to help you get started and build your first data lake, including comprehensive documentation, demo videos, and webinars. By giving Security Lake a try, you can experience firsthand how it helps you centralize, normalize, and optimize your security data, and ultimately streamline your organization’s security incident detection and response across multicloud and hybrid environments.

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

Want more AWS Security news? Follow us on Twitter.

Faster AWS cloud connections with TLS 1.3

2023-05-23 Kate Rodgers

Post Syndicated from Kate Rodgers original https://aws.amazon.com/blogs/security/faster-aws-cloud-connections-with-tls-1-3/

At Amazon Web Services (AWS), we strive to continuously improve customer experience by delivering a cloud computing environment that supports the most modern security technologies. To improve the overall performance of your connections, we have already started to enable TLS version 1.3 globally across our AWS service API endpoints, and will complete this process by December 31, 2023. By using TLS 1.3, you can decrease your connection time by removing one network round trip for every connection request, and can benefit from some of the most modern and secure cryptographic cipher suites available today.

If you are using current software tools (2014 or later) including our AWS SDKs or AWS Command Line Interface (AWS CLI), you will automatically receive the benefits of TLS 1.3 with no action required on your part. This is because AWS services will negotiate the highest TLS protocol version that your client software supports. If you want to continue using TLS 1.2, you will still have full control through your client configurations. AWS will retain support for TLS 1.2, in addition to TLS 1.3, into the foreseeable future. Meanwhile, here’s the latest information on the on-going deprecation of TLS 1.0/1.1.

If you have any questions, start a new thread on AWS re:Post, or contact AWS Support or your technical account manager. 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.

Stronger together: Highlights from RSA Conference 2023

2023-05-19 Anne Grahn

Post Syndicated from Anne Grahn original https://aws.amazon.com/blogs/security/stronger-together-highlights-from-rsa-conference-2023/

Golden Gate bridge

RSA Conference 2023 brought thousands of cybersecurity professionals to the Moscone Center in San Francisco, California from April 24 through 27.

The keynote lineup was eclectic, with more than 30 presentations across two stages featuring speakers ranging from renowned theoretical physicist and futurist Dr. Michio Kaku to Grammy-winning musician Chris Stapleton. Topics aligned with this year’s conference theme, “Stronger Together,” and focused on actions that can be taken by everyone, from the C-suite to those of us on the front lines of security, to strengthen collaboration, establish new best practices, and make our defenses more diverse and effective.

With over 400 sessions and 500 exhibitors discussing the latest trends and technologies, it’s impossible to recap every highlight. Now that the dust has settled and we’ve had time to reflect, here’s a glimpse of what caught our attention.

Noteworthy announcements

Hundreds of companies — including Amazon Web Services (AWS) — made new product and service announcements during the conference.

We announced three new capabilities for our Amazon GuardDuty threat detection service to help customers secure container, database, and serverless workloads. These include GuardDuty Elastic Kubernetes Service (EKS) Runtime Monitoring, GuardDuty RDS Protection for data stored in Amazon Aurora, and GuardDuty Lambda Protection for serverless applications. The new capabilities are designed to provide actionable, contextual, and timely security findings with resource-specific details.

Artificial intelligence

It was hard to find a single keynote, session, or conversation that didn’t touch on the impact of artificial intelligence (AI).

In “AI: Law, Policy and Common Sense Suggestions on How to Stay Out of Trouble,” privacy and gaming attorney Behnam Dayanim highlighted ambiguity around the definition of AI. Referencing a quote from University of Washington School of Law’s Ryan Calo, Dayanim pointed out that AI may be best described as “…a set of techniques aimed at approximating some aspect of cognition,” and should therefore be thought of differently than a discrete “thing” or industry sector.

Dayanim noted examples of skepticism around the benefits of AI. A recent Monmouth University poll, for example, found that 73% of Americans believe AI will make jobs less available and harm the economy, and a surprising 55% believe AI may one day threaten humanity’s existence.

Equally skeptical, he noted, is a joint statement made by the Federal Trade Commission (FTC) and three other federal agencies during the conference reminding the public that enforcement authority applies to AI. The statement takes a pessimistic view, saying that AI is “…often advertised as providing insights and breakthroughs, increasing efficiencies and cost-savings, and modernizing existing practices,” but has the potential to produce negative outcomes.

Dayanim covered existing and upcoming legal frameworks around the world that are aimed at addressing AI-related risks related to intellectual property (IP), misinformation, and bias, and how organizations can design AI governance mechanisms to promote fairness, competence, transparency, and accountability.

Many other discussions focused on the immense potential of AI to automate and improve security practices. RSA Security CEO Rohit Ghai explored the intersection of progress in AI with human identity in his keynote. “Access management and identity management are now table stakes features”, he said. In the AI era, we need an identity security solution that will secure the entire identity lifecycle—not just access. To be successful, he believes, the next generation of identity technology needs to be powered by AI, open and integrated at the data layer, and pursue a security-first approach. “Without good AI,” he said, “zero trust has zero chance.”

Mark Ryland, director at the Office of the CISO at AWS, spoke with Infosecurity about improving threat detection with generative AI.

“We’re very focused on meaningful data and minimizing false positives. And the only way to do that effectively is with machine learning (ML), so that’s been a core part of our security services,” he noted.

We recently announced several new innovations—including Amazon Bedrock, the Amazon Titan foundation model, the general availability of Amazon Elastic Compute Cloud (Amazon EC2) Trn1n instances powered by AWS Trainium, Amazon EC2 Inf2 instances powered by AWS Inferentia2, and the general availability of Amazon CodeWhisperer—that will make it practical for customers to use generative AI in their businesses.

“Machine learning and artificial intelligence will add a critical layer of automation to cloud security. AI/ML will help augment developers’ workstreams, helping them create more reliable code and drive continuous security improvement. — CJ Moses, CISO and VP of security engineering at AWS

The human element

Dozens of sessions focused on the human element of security, with topics ranging from the psychology of DevSecOps to the NIST Phish Scale. In “How to Create a Breach-Deterrent Culture of Cybersecurity, from Board Down,” Andrzej Cetnarski, founder, chairman, and CEO of Cyber Nation Central and Marcus Sachs, deputy director for research at Auburn University, made a data-driven case for CEOs, boards, and business leaders to set a tone of security in their organizations, so they can address “cyber insecure behaviors that lead to social engineering” and keep up with the pace of cybercrime.

Lisa Plaggemier, executive director of the National Cybersecurity Alliance, and Jenny Brinkley, director of Amazon Security, stressed the importance of compelling security awareness training in “Engagement Through Entertainment: How To Make Security Behaviors Stick.” Education is critical to building a strong security posture, but as Plaggemier and Brinkley pointed out, we’re “living through an epidemic of boringness” in cybersecurity training.

According to a recent report, just 28% of employees say security awareness training is engaging, and only 36% say they pay full attention during such training.

Citing a United Airlines preflight safety video and Amazon’s Protect and Connect public service announcement (PSA) as examples, they emphasized the need to make emotional connections with users through humor and unexpected elements in order to create memorable training that drives behavioral change.

Plaggemeier and Brinkley detailed five actionable steps for security teams to improve their awareness training:

Brainstorm with staff throughout the company (not just the security people)
Find ideas and inspiration from everywhere else (TV episodes, movies… anywhere but existing security training)
Be relatable, and include insights that are relevant to your company and teams
Start small; you don’t need a large budget to add interest to your training
Don’t let naysayers deter you — change often prompts resistance

“You’ve got to make people care. And so you’ve got to find out what their personal motivators are, and how to develop the type of content that can make them care to click through the training and…remember things as they’re walking through an office.” — Jenny Brinkley, director of Amazon Security

Cloud security

Cloud security was another popular topic. In “Architecting Security for Regulated Workloads in Hybrid Cloud,” Mark Buckwell, cloud security architect at IBM, discussed the architectural thinking practices—including zero trust—required to integrate security and compliance into regulated workloads in a hybrid cloud environment.

Mitiga co-founder and CTO Ofer Maor told real-world stories of SaaS attacks and incident response in “It’s Getting Real & Hitting the Fan 2023 Edition.”

Maor highlighted common tactics focused on identity theft, including MFA push fatigue, phishing, business email compromise, and adversary-in-the middle attacks. After detailing techniques that are used to establish persistence in SaaS environments and deliver ransomware, Maor emphasized the importance of forensic investigation and threat hunting to gaining the knowledge needed to reduce the impact of SaaS security incidents.

Sarah Currey, security practice manager, and Anna McAbee, senior solutions architect at AWS, provided complementary guidance in “Top 10 Ways to Evolve Cloud Native Incident Response Maturity.” Currey and McAbee highlighted best practices for addressing incident response (IR) challenges in the cloud — no matter who your provider is:

Define roles and responsibilities in your IR plan
Train staff on AWS (or your provider)
Develop cloud incident response playbooks
Develop account structure and tagging strategy
Run simulations (red team, purple team, tabletop)
Prepare access
Select and set up logs
Enable managed detection services in all available AWS Regions
Determine containment strategy for resource types
Develop cloud forensics capabilities

Speaking to BizTech, Clarke Rodgers, director of enterprise strategy at AWS, noted that tools and services such as Amazon GuardDuty and AWS Key Management Service (AWS KMS) are available to help advance security in the cloud. When organizations take advantage of these services and use partners to augment security programs, they can gain the confidence they need to take more risks, and accelerate digital transformation and product development.

Security takes a village

There are more highlights than we can mention on a variety of other topics, including post-quantum cryptography, data privacy, and diversity, equity, and inclusion. We’ve barely scratched the surface of RSA Conference 2023. If there is one key takeaway, it is that no single organization or individual can address cybersecurity challenges alone. By working together and sharing best practices as an industry, we can develop more effective security solutions and stay ahead of emerging threats.

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

Want more AWS Security news? Follow us on Twitter.

Your guide to the threat detection and incident response track at re:Inforce 2023

2023-05-18 Celeste Bishop

Post Syndicated from Celeste Bishop original https://aws.amazon.com/blogs/security/your-guide-to-the-threat-detection-and-incident-response-track-at-reinforce-2023/

reInforce 2023

A full conference pass is $1,099. Register today with the code secure150off to receive a limited time $150 discount, while supplies last.

AWS re:Inforce is back, and we can’t wait to welcome security builders to Anaheim, CA, on June 13 and 14. AWS re:Inforce is a security learning conference where you can gain skills and confidence in cloud security, compliance, identity, and privacy. As an attendee, you will have access to hundreds of technical and non-technical sessions, an Expo featuring AWS experts and security partners with AWS Security Competencies, and keynote and leadership sessions featuring Security leadership. re:Inforce 2023 features content across the following six areas:

Data protection
Governance, risk, and compliance
Identity and access management
Network and infrastructure security
Threat detection and incident response
Application security

The threat detection and incident response track is designed to showcase how AWS, customers, and partners can intelligently detect potential security risks, centralize and streamline security management at scale, investigate and respond quickly to security incidents across their environment, and unlock security innovation across hybrid cloud environments.

Breakout sessions, chalk talks, and lightning talks

TDR201 | Breakout session | How Citi advanced their containment capabilities through automation
Incident response is critical for maintaining the reliability and security of AWS environments. To support the 28 AWS services in their cloud environment, Citi implemented a highly scalable cloud incident response framework specifically designed for their workloads on AWS. Using AWS Step Functions and AWS Lambda, Citi’s automated and orchestrated incident response plan follows NIST guidelines and has significantly improved its response time to security events. In this session, learn from real-world scenarios and examples on how to use AWS Step Functions and other core AWS services to effectively build and design scalable incident response solutions.

TDR202 | Breakout session | Wix’s layered security strategy to discover and protect sensitive data
Wix is a leading cloud-based development platform that empowers users to get online with a personalized, professional web presence. In this session, learn how the Wix security team layers AWS security services including Amazon Macie, AWS Security Hub, and AWS Identity and Access Management Access Analyzer to maintain continuous visibility into proper handling and usage of sensitive data. Using AWS security services, Wix can discover, classify, and protect sensitive information across terabytes of data stored on AWS and in public clouds as well as SaaS applications, while empowering hundreds of internal developers to drive innovation on the Wix platform.

TDR203 | Breakout session | Vulnerability management at scale drives enterprise transformation
Automating vulnerability management at scale can help speed up mean time to remediation and identify potential business-impacting issues sooner. In this session, explore key challenges that organizations face when approaching vulnerability management across large and complex environments, and consider the innovative solutions that AWS provides to help overcome them. Learn how customers use AWS services such as Amazon Inspector to automate vulnerability detection, streamline remediation efforts, and improve compliance posture. Whether you’re just getting started with vulnerability management or looking to optimize your existing approach, gain valuable insights and inspiration to help you drive innovation and enhance your security posture with AWS.

TDR204 | Breakout session | Continuous innovation in AWS detection and response services
Join this session to learn about the latest advancements and most recent AWS launches in detection and response. This session focuses on use cases such as automated threat detection, continual vulnerability management, continuous cloud security posture management, and unified security data management. Through these examples, gain a deeper understanding of how you can seamlessly integrate AWS services into your existing security framework to gain greater control and insight, quickly address security risks, and maintain the security of your AWS environment.

TDR205 | Breakout session | Build your security data lake with Amazon Security Lake, featuring Interpublic Group
Security teams want greater visibility into security activity across their entire organizations to proactively identify potential threats and vulnerabilities. Amazon Security Lake automatically centralizes security data from cloud, on-premises, and custom sources into a purpose-built data lake stored in your account and allows you to use industry-leading AWS and third-party analytics and ML tools to gain insights from your data and identify security risks that require immediate attention. Discover how Security Lake can help you consolidate and streamline security logging at scale and speed, and hear from an AWS customer, Interpublic Group (IPG), on their experience.

TDR209 | Breakout session | Centralizing security at scale with Security Hub & Intuit’s experience
As organizations move their workloads to the cloud, it becomes increasingly important to have a centralized view of security across their cloud resources. AWS Security Hub is a powerful tool that allows organizations to gain visibility into their security posture and compliance status across their AWS accounts and Regions. In this session, learn about Security Hub’s new capabilities that help simplify centralizing and operationalizing security. Then, hear from Intuit, a leading financial software company, as they share their experience and best practices for setting up and using Security Hub to centralize security management.

TDR210 | Breakout session | Streamline security analysis with Amazon Detective
Join us to discover how to streamline security investigations and perform root-cause analysis with Amazon Detective. Learn how to leverage the graph analysis techniques in Detective to identify related findings and resources and investigate them together to accelerate incident analysis. Also hear a customer story about their experience using Detective to analyze findings automatically ingested from Amazon GuardDuty, and walk through a sample security investigation.

TDR310 | Breakout session | Developing new findings using machine learning in Amazon GuardDuty
Amazon GuardDuty provides threat detection at scale, helping you quickly identify and remediate security issues with actionable insights and context. In this session, learn how GuardDuty continuously enhances its intelligent threat detection capabilities using purpose-built machine learning models. Discover how new findings are developed for new data sources using novel machine learning techniques and how they are rigorously evaluated. Get a behind-the-scenes look at GuardDuty findings from ideation to production, and learn how this service can help you strengthen your security posture.

TDR311 | Breakout session | Securing data and democratizing the alert landscape with an event-driven architecture
Security event monitoring is a unique challenge for businesses operating at scale and seeking to integrate detections into their existing security monitoring systems while using multiple detection tools. Learn how organizations can triage and raise relevant cloud security findings across a breadth of detection tools and provide results to downstream security teams in a serverless manner at scale. We discuss how to apply a layered security approach to evaluate the security posture of your data, protect your data from potential threats, and automate response and remediation to help with compliance requirements.

TDR231 | Chalk talk | Operationalizing security findings at scale
You enabled AWS Security Hub standards and checks across your AWS organization and in all AWS Regions. What should you do next? Should you expect zero critical and high findings? What is your ideal state? Is achieving zero findings possible? In this chalk talk, learn about a framework you can implement to triage Security Hub findings. Explore how this framework can be applied to several common critical and high findings, and take away mechanisms to prioritize and respond to security findings at scale.

TDR232 | Chalk talk | Act on security findings using Security Hub’s automation capabilities
Alert fatigue, a shortage of skilled staff, and keeping up with dynamic cloud resources are all challenges that exist when it comes to customers successfully achieving their security goals in AWS. In order to achieve their goals, customers need to act on security findings associated with cloud-based resources. In this session, learn how to automatically, or semi-automatically, act on security findings aggregated in AWS Security Hub to help you secure your organization’s cloud assets across a diverse set of accounts and Regions.

TDR233 | Chalk talk | How LLA reduces incident response time with AWS Systems Manager
Liberty Latin America (LLA) is a leading telecommunications company operating in over 20 countries across Latin America and the Caribbean. LLA offers communications and entertainment services, including video, broadband internet, telephony, and mobile services. In this chalk talk, discover how LLA implemented a security framework to detect security issues and automate incident response in more than 180 AWS accounts accessed by internal stakeholders and third-party partners using AWS Systems Manager Incident Manager, AWS Organizations, Amazon GuardDuty, and AWS Security Hub.

TDR432 | Chalk talk | Deep dive into exposed credentials and how to investigate them
In this chalk talk, sharpen your detection and investigation skills to spot and explore common security events like unauthorized access with exposed credentials. Learn how to recognize the indicators of such events, as well as logs and techniques that unauthorized users use to evade detection. The talk provides knowledge and resources to help you immediately prepare for your own security investigations.

TDR332 | Chalk talk | Speed up zero-day vulnerability response
In this chalk talk, learn how to scale vulnerability management for Amazon EC2 across multiple accounts and AWS Regions. Explore how to use Amazon Inspector, AWS Systems Manager, and AWS Security Hub to respond to zero-day vulnerabilities, and leave knowing how to plan, perform, and report on proactive and reactive remediations.

TDR333 | Chalk talk | Gaining insights from Amazon Security Lake
You’ve created a security data lake, and you’re ingesting data. Now what? How do you use that data to gain insights into what is happening within your organization or assist with investigations and incident response? Join this chalk talk to learn how analytics services and security information and event management (SIEM) solutions can connect to and use data stored within Amazon Security Lake to investigate security events and identify trends across your organization. Leave with a better understanding of how you can integrate Amazon Security Lake with other business intelligence and analytics tools to gain valuable insights from your security data and respond more effectively to security events.

TDR431 | Chalk talk | The anatomy of a ransomware event
Ransomware events can cost governments, nonprofits, and businesses billions of dollars and interrupt operations. Early detection and automated responses are important steps that can limit your organization’s exposure. In this chalk talk, examine the anatomy of a ransomware event that targets data residing in Amazon RDS and get detailed best practices for detection, response, recovery, and protection.

TDR221 | Lightning talk | Streamline security operations and improve threat detection with OCSF
Security operations centers (SOCs) face significant challenges in monitoring and analyzing security telemetry data from a diverse set of sources. This can result in a fragmented and siloed approach to security operations that makes it difficult to identify and investigate incidents. In this lightning talk, get an introduction to the Open Cybersecurity Schema Framework (OCSF) and its taxonomy constructs, and see a quick demo on how this normalized framework can help SOCs improve the efficiency and effectiveness of their security operations.

TDR222 | Lightning talk | Security monitoring for connected devices across OT, IoT, edge & cloud
With the responsibility to stay ahead of cybersecurity threats, CIOs and CISOs are increasingly tasked with managing cybersecurity risks for their connected devices including devices on the operational technology (OT) side of the company. In this lightning talk, learn how AWS makes it simpler to monitor, detect, and respond to threats across the entire threat surface, which includes OT, IoT, edge, and cloud, while protecting your security investments in existing third-party security tools.

TDR223 | Lightning talk | Bolstering incident response with AWS Wickr enterprise integrations
Every second counts during a security event. AWS Wickr provides end-to-end encrypted communications to help incident responders collaborate safely during a security event, even on a compromised network. Join this lightning talk to learn how to integrate AWS Wickr with AWS security services such as Amazon GuardDuty and AWS WAF. Learn how you can strengthen your incident response capabilities by creating an integrated workflow that incorporates GuardDuty findings into a secure, out-of-band communication channel for dedicated teams.

TDR224 | Lightning talk | Securing the future of mobility: Automotive threat modeling
Many existing automotive industry cybersecurity threat intelligence offerings lack the connected mobility insights required for today’s automotive cybersecurity threat landscape. Join this lightning talk to learn about AWS’s approach to developing an automotive industry in-vehicle, domain-specific threat intelligence solution using AWS AI/ML services that proactively collect, analyze, and deduce threat intelligence insights for use and adoption across automotive value chains.

Hands-on sessions (builders’ sessions and workshops)

TDR251 | Builders’ session | Streamline and centralize security operations with AWS Security Hub
AWS Security Hub provides you with a comprehensive view of the security state of your AWS resources by collecting security data from across AWS accounts, Regions, and services. In this builders’ session, explore best practices for using Security Hub to manage security posture, prioritize security alerts, generate insights, automate response, and enrich findings. Come away with a better understanding of how to use Security Hub features and practical tips for getting the most out of this powerful service.

TDR351 | Builders’ session | Broaden your scope: Analyze and investigate potential security issues
In this builders’ session, learn how you can more efficiently triage potential security issues with a dynamic visual representation of the relationship between security findings and associated entities such as accounts, IAM principals, IP addresses, Amazon S3 buckets, and Amazon EC2 instances. With Amazon Detective finding groups, you can group related Amazon GuardDuty findings to help reduce time spent in security investigations and in understanding the scope of a potential issue. Leave this hands-on session knowing how to quickly investigate and discover the root cause of an incident.

TDR352 | Builders’ session | How to automate containment and forensics for Amazon EC2
In this builders’ session, learn how to deploy and scale the self-service Automated Forensics Orchestrator for Amazon EC2 solution, which gives you a standardized and automated forensics orchestration workflow capability to help you respond to Amazon EC2 security events. Explore the prerequisites and ways to customize the solution to your environment.

TDR353 | Builders’ session | Detecting suspicious activity in Amazon S3
Have you ever wondered how to uncover evidence of unauthorized activity in your AWS account? In this builders’ session, join the AWS Customer Incident Response Team (CIRT) for a guided simulation of suspicious activity within an AWS account involving unauthorized data exfiltration and Amazon S3 bucket and object data deletion. Learn how to detect and respond to this malicious activity using AWS services like AWS CloudTrail, Amazon Athena, Amazon GuardDuty, Amazon CloudWatch, and nontraditional threat detection services like AWS Billing to uncover evidence of unauthorized use.

TDR354 | Builders’ session | Simulate and detect unwanted IMDS access due to SSRF
Using appropriate security controls can greatly reduce the risk of unauthorized use of web applications. In this builders’ session, find out how the server-side request forgery (SSRF) vulnerability works, how unauthorized users may try to use it, and most importantly, how to detect it and prevent it from being used to access the instance metadata service (IMDS). Also, learn some of the detection activities that the AWS Customer Incident Response Team (CIRT) performs when responding to security events of this nature.

TDR341 | Code talk | Investigating incidents with Amazon Security Lake & Jupyter notebooks
In this code talk, watch as experts live code and build an incident response playbook for your AWS environment using Jupyter notebooks, Amazon Security Lake, and Python code. Leave with a better understanding of how to investigate and respond to a security event and how to use these technologies to more effectively and quickly respond to disruptions.

TDR441 | Code talk | How to run security incident response in your Amazon EKS environment
Join this Code Talk to get both an adversary’s and a defender’s point of view as AWS experts perform live exploitation of an application running on multiple Amazon EKS clusters, invoking an alert in Amazon GuardDuty. Experts then walk through incident response procedures to detect, contain, and recover from the incident in near real-time. Gain an understanding of how to respond and recover to Amazon EKS-specific incidents as you watch the events unfold.

TDR271-R | Workshop | Chaos Kitty: Gamifying incident response with chaos engineering
When was the last time you simulated an incident? In this workshop, learn to build a sandbox environment to gamify incident response with chaos engineering. You can use this sandbox to test out detection capabilities, play with incident response runbooks, and illustrate how to integrate AWS resources with physical devices. Walk away understanding how to get started with incident response and how you can use chaos engineering principles to create mechanisms that can improve your incident response processes.

TDR371-R | Workshop | Threat detection and response on AWS
Join AWS experts for a hands-on threat detection and response workshop using Amazon GuardDuty, AWS Security Hub, and Amazon Detective. This workshop simulates security events for different types of resources and behaviors and illustrates both manual and automated responses with AWS Lambda. Dive in and learn how to improve your security posture by operationalizing threat detection and response on AWS.

TDR372-R | Workshop | Container threat detection with AWS security services
Join AWS experts for a hands-on container security workshop using AWS threat detection and response services. This workshop simulates scenarios and security events while using Amazon EKS and demonstrates how to use different AWS security services to detect and respond to events and improve your security practices. Dive in and learn how to improve your security posture when running workloads on Amazon EKS.

Browse the full re:Inforce catalog to get details on additional sessions and content at the event, including gamified learning, leadership sessions, partner sessions, and labs.

If you want to learn the latest threat detection and incident response best practices and updates, join us in California by registering for re:Inforce 2023. We look forward to seeing you there!

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

Want more AWS Security news? Follow us on Twitter.

Spring 2023 SOC reports now available with 158 services in scope

2023-05-17 Andrew Najjar

Post Syndicated from Andrew Najjar original https://aws.amazon.com/blogs/security/spring-2023-soc-reports-now-available-with-158-services-in-scope/

At Amazon Web Services (AWS), we’re committed to providing our customers with continued assurance over the security, availability, confidentiality, and privacy of the AWS control environment.

We’re proud to deliver the Spring 2023 System and Organization Controls (SOC) 1, 2 and 3 reports, which cover October 1, 2022, to March 31, 2023, to support your confidence in AWS services. SOC reports are independent third-party examination reports that demonstrate how AWS achieves key compliance controls and objectives.

In the past, the Privacy SOC 2 report was issued separately from the other reports. However, starting with this Spring 2023 reporting cycle, the SOC 2 report is now consolidated and covers the Security, Availability, Confidentiality, and Privacy Trust Service Criteria.

The Spring 2023 SOC reports include four additional services in scope, for a total of 158 services. See the full list on our Services in Scope by Compliance Program page.

The following are the four additional services now in scope for the Spring 2023 SOC reports:

Five additional AWS Regions have been added to the scope, for a total of 29 Regions. The following are the five additional Regions now in scope for the Spring 2023 SOC reports:

Australia: Asia Pacific (Melbourne) (ap-southeast-4)
India: Asia Pacific (Hyderabad) (ap-south-2)
Spain: Europe (Spain) (eu-south-2)
Switzerland: Europe (Zurich) (eu-central-2)
United Arab Emirates: Middle East (UAE) (me-central-1)

Customers can download the Spring 2023 SOC reports through AWS Artifact in the AWS Management Console. You can also download the SOC 3 report as a PDF file from AWS.

AWS strives to bring services into the scope of its compliance programs to help you meet your architectural and regulatory needs. If there are additional AWS services you would like to see added to the scope of our SOC reports (or other compliance programs), reach out to your AWS representatives.

As always, we value your feedback and questions. Feel free to reach out to the team through the Contact Us page. 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.

AWS completes the 2023 Cyber Essentials Plus certification and NHS Data Security and Protection Toolkit assessment

2023-05-17 Tariro Dongo

Post Syndicated from Tariro Dongo original https://aws.amazon.com/blogs/security/aws-completes-the-2023-cyber-essentials-plus-certification-and-nhs-data-security-and-protection-toolkit-assessment/

Amazon Web Services (AWS) is pleased to announce the successful completion of the United Kingdom Cyber Essentials Plus certification and the National Health Service Data Security and Protection Toolkit (NHS DSPT) assessment. The Cyber Essentials Plus certificate and NHS DSPT assessment are valid for one year until March 28, 2024, and June 30, 2024, respectively.

Cyber Essentials Plus is a UK Government-backed, industry-supported certification scheme intended to help organizations demonstrate organizational cyber security against common cyber attacks. An independent third-party auditor certified by the Information Assurance for Small and Medium Enterprises (IASME) completed the audit. The scope of our Cyber Essentials Plus certificate covers AWS Europe (London), AWS Europe (Ireland), and AWS Europe (Frankfurt) Regions.

The NHS DSPT is a self-assessment that organizations use to measure their performance against data security and information governance requirements. The UK Department of Health and Social Care sets these requirements.

When customers move to the AWS Cloud, AWS is responsible for protecting the global infrastructure that runs our services offered in the AWS Cloud. AWS customers are the data controllers for patient health and care data, and are responsible for anything they put in the cloud or connect to the cloud. For more information, see the AWS Shared Security Responsibility Model.

AWS status is available on the AWS Cyber Essentials Plus compliance page, the NHS DSPT portal, and through AWS Artifact. AWS Artifact is a self-service portal for on-demand access to AWS compliance reports. Sign in to AWS Artifact in the AWS Management Console, or learn more at Getting Started with AWS Artifact.

As always, we value your feedback and questions. Reach out to the AWS Compliance team through the Contact Us page. If you have feedback about this post, submit a comment in the Comments section below. To learn more about our other compliance and security programs, see AWS Compliance Programs.

Want more AWS Security news? Follow us on Twitter.

Share and query encrypted data in AWS Clean Rooms

2023-05-16 Jonathan Herzog

Post Syndicated from Jonathan Herzog original https://aws.amazon.com/blogs/security/share-and-query-encrypted-data-in-aws-clean-rooms/

In this post, we’d like to introduce you to the cryptographic computing feature of AWS Clean Rooms. With AWS Clean Rooms, customers can run collaborative data-query sessions on sensitive data sets that live in different AWS accounts, and can do so without having to share, aggregate, or replicate the data. When customers also use the cryptographic computing feature, their data remains cryptographically protected even while it is being processed by an AWS Clean Rooms collaboration.

Where would AWS Clean Rooms be useful? Consider a scenario where two different insurance companies want to identify duplicate claims so they can identify potential fraud. This would be simple if they could compare their claims with each other, but they might not be able to do so due to privacy constraints.

Alternately, consider an advertising network and a client that want to measure the effectiveness of an advertising campaign. To that end, they would like to know how many of the people who saw the campaign (exposures) went on to make a purchase from the client (purchasers). However, confidentiality concerns might prevent the advertising network from sharing their list of exposures with the client or prevent the client from sharing their list of purchasers with the advertising network.

As these examples show, there can be many situations in which different organizations want to collaborate on a joint analysis of their pooled data, but cannot share their individual datasets directly. One solution to this problem is a data clean room, which is a service trusted by a collaboration’s participants to do the following:

Hold the data of individual parties
Enforce access-control rules that collaborators specify regarding their data
Perform analyses over the pooled data

To serve customers with these needs, AWS recently launched a new data clean-room service called AWS Clean Rooms. This service provides AWS customers with a way to collaboratively analyze data (stored in other AWS services as SQL tables) without having to replicate the data, move the data outside of the AWS Cloud, or allow their collaborators to see the data itself.

Additionally, AWS Clean Rooms provides a feature that gives customers even more control over their data: cryptographic computing. This feature allows AWS Clean Rooms to operate over data that customers encrypt themselves and that the service cannot actually read. Specifically, customers can use this feature to select which portions of their data should be encrypted and to encrypt that data themselves. Collaborators can continue to analyze that data as if it were in the clear, however, even though the data in question remains encrypted while it is being processed in AWS Clean Rooms collaborations. In this way, customers can use AWS Clean Rooms to securely collaborate on data they may not have been able to share due to internal policies or regulations.

Cryptographic computing

Using the cryptographic computing feature of AWS Clean Rooms involves these steps:

Users create AWS Clean Rooms collaborations and set collaboration-wide encryption settings. They then invite collaborators to support the analysis process.
Outside of AWS Clean Rooms, those collaborators agree on a shared secret: a common, secret, cryptographic key.
Collaborators individually encrypt their tables outside of the AWS Cloud (typically on their own premises) using the shared secret, the collaboration ID of the intended collaboration, and the Cryptographic Computing for Clean Rooms (C3R) encryption client (which AWS provides as an open-source package). Collaborators then provide the encrypted tables to AWS Clean Rooms, just as they would have provided plaintext tables.
Collaborators continue to use AWS Clean Rooms for their data analysis. They impose access-control rules on their tables, submit SQL queries over the tables in the collaboration, and retrieve results.
These results might contain encrypted columns, and so collaborators decrypt the results by using the shared secret and the C3R encryption client.

As a result, data that enters AWS Clean Rooms in encrypted format will remain encrypted from input tables to intermediate values to result sets. AWS Clean Rooms will be unable to decrypt or read the data even while performing the queries.

Note: For those interested in the academic aspects of this process, the cryptographic computing feature of AWS Clean Rooms is based on server-aided private set intersection (PSI). Server-aided PSI allows two or more participants to submit sets of values to a server and learn which elements are found in all sets, but without (1) allowing the participants to learn anything about the other (non-shared) elements, or (2) allowing the server to learn anything about the underlying data (aside from the degrees to which the sets overlap). PSI is just one example of the field of cryptographic computing, which provides a variety of new methods by which encrypted data can be processed for various purposes and without decryption. These techniques allow our customers to use the scale and power of AWS systems on data that AWS will not be able to read. See our Cryptographic Computing webpage for more about our work in this area.

Let’s dive deeper into each new step in the process for using cryptographic computing in AWS Clean Rooms.

Key agreement. Each collaboration needs its own shared secret: a secure cryptographic secret (of at least 256 bits). Customers sometimes have a regulatory need to maintain ownership of their encryption keys. Therefore, the cryptographic computing feature supports the case where customers generate, distribute, and store their collaboration’s secret themselves. In this way, customers’ encryption keys are never stored on an AWS system.

Encryption. AWS Clean Rooms allows table owners to control how tables are encrypted on a column-by-column basis. In particular, each column in an encrypted table will be one of three types: cleartext, sealed, or fingerprint. These types map directly to both how columns are used in queries and how they are protected with cryptography, described as follows:

Cleartext columns are not cryptographically processed at all. They are copied to encrypted tables verbatim, and can be used anywhere in a SQL query.
Sealed columns are encrypted. The encryption scheme used (AES-GCM) is randomized, meaning that encrypting the same value multiple times yields different ciphertexts each time. This helps prevent the statistical analysis of these columns, but also means that these columns cannot be used in JOIN clauses. They can be used in SELECT clauses, however, which allows them to appear in query results.
Fingerprint columns are hashed using the Hash-based Message Authentication Code (HMAC) algorithm. There is no way to decrypt these values, and therefore no reason for them to appear in the SELECT clause of a query. They can, however, be used in JOIN clauses: HMAC will map a given value to the same fingerprint every time, meaning that JOINs will be able to unify common values across different fingerprint columns.

Encryption settings. This last point—that fingerprint values will always map a given plaintext value to the same fingerprint—might give pause to some readers. If this is true, won’t the encrypted table be vulnerable to statistical analysis? That is absolutely correct: it will. For this reason, users might wish to set collaboration-wide encryption settings to control these forms of analysis.

To see how statistical analysis might be a concern, imagine a table where one fingerprint column is named US_State. In this case, a simple frequency analysis will reverse-engineer the plaintext values relatively quickly: the most common fingerprint is almost certain to be “California”, followed by “Texas”, “Florida”, and so on. Also, imagine that the same table has another fingerprint column called US_City, and that a given fingerprint appears in both columns. In that case, the fingerprint in question is almost certain to be “New York”. If a row has a fingerprint in the US_City column but a NULL in the US_State column, furthermore, it’s very likely that the fingerprint is for “District of Columbia”. And finally, imagine that the table has a cleartext column called Time_Zone. In this case, values of “HST” (Hawaii standard time) or “AKST” (Alaska standard time) reveal the value in the US_State column regardless of the cryptography.

Not all datasets will be vulnerable to these kinds of statistical analysis, but some will. Only customers can determine which types of analysis may reveal their data and which may not. Because of this, the cryptographic computing feature allows the customer to decide which protections will be needed. At the time of collaboration creation, that is, the creator of the AWS Clean Rooms collaboration can configure the following collaboration-wide encryption settings:

Whether or not fingerprint columns can contain duplicate plaintext values (addressing the “California” example)
Whether or not fingerprint columns with different names should fingerprint values in the same way (addressing the “New York” example)
Whether or not NULL values in the plaintext table should be left as NULL in the encrypted table (addressing the “District of Columbia” example)
Whether or not encrypted tables should be allowed to have cleartext columns at all (addressing the time zone example)

Security is maximized when all of these options are set to “no,” but each “no” will limit the queries that C3R will be able to support. For example, the choice of whether or not encrypted tables should be allowed to have cleartext columns will determine which WHERE clauses will be supported: If cleartext columns are not supported, then the Time_Zone column must be cryptographically processed — meaning that the clause WHERE Time_Zone=”EST” will not act as intended. There might be reasons to set these options to “yes” in order to enable a wider variety of queries, which we discuss in the Query behavior section later in this post.

Decryption. AWS Clean Rooms will write query results to an Amazon Simple Storage Service (Amazon S3) bucket. The recipient copies these results from the bucket to some on-premises storage and then runs the C3R encryption client. The client will find encrypted elements of the output and decrypt them. Note that the client can only decrypt elements from sealed columns. If the output contains elements from a fingerprint column, the client will warn you, but will also leave these elements untouched, as cryptographic fingerprints can’t be decrypted.

Having finished our overview, let’s return to the discussion regarding how encryption can affect the behavior of queries.

Query behavior

Implicit in the discussion so far is something worth calling out explicitly: AWS Clean Rooms runs queries over the data that is provided to it. If the data given to AWS Clean Rooms is encrypted, therefore, queries will be run on the ciphertexts and not the plaintexts. This will not affect the results returned, so long as the columns are used for their intended purposes:

Fingerprint columns are used in JOIN clauses
Sealed columns are used in SELECT clauses

(Cleartext columns can be used anywhere.) Queries might produce unexpected results, however, if the columns are used outside of their intended purposes:

Sometimes queries will fail when they would have succeeded on the plaintext. For example, ciphertexts and fingerprints will be string values, even if the original plaintext values were another type. Therefore, SUM() or AVG() calls on fingerprint or sealed columns will yield errors even if the corresponding plaintext columns were numeric.
Sometimes queries will omit results that would have been found by querying the plaintext. For example, attempting to JOIN on sealed columns will yield empty result sets: no two ciphertexts will be the same, even if they encrypt the same plaintext value. (Also, performing a JOIN on fingerprint columns with different names will exhibit the same behavior, if the collaboration-wide encryption settings specified that fingerprint columns of different names should fingerprint values differently.)
Sometimes results will include rows that would not be found by querying the plaintext. As mentioned, ciphertexts and fingerprints will be string values—base64 encodings of random-looking bytes, specifically. This means that a clause such as WHERE ‘US_State’ CONTAINS ‘CA’ will match some ciphertexts or fingerprints even when they would not match the plaintext.

To avoid these issues, fingerprint and sealed columns should only be used for their intended purposes (JOIN and SELECT clauses, respectively).

Conclusion

In this blog post, you have learned how AWS Clean Rooms can help you harness the power of AWS services to query and analyze your most-sensitive data. By using cryptographic computing, you can work with collaborators to perform joint analyses over pooled data without sharing your “raw” data with each other—or with AWS. If you believe that you can benefit from cryptographic computing (in AWS Clean Rooms or elsewhere), we’d like to hear from you. Please contact us with any questions or feedback. Also, we invite you to learn more about AWS Clean Rooms (including its use of cryptographic computing). Finally, the C3R client is open source, and can be downloaded from its GitHub page.

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

Want more AWS Security news? Follow us on Twitter.

Using OpenSearch Service as a vector database

Semantic search

Retrieval Augmented Generation with LLMs

Recommendation engine

Media search

Understanding the technology

Approximate and exact nearest-neighbor search

Approximate k-NN

Score script

Painless extensions

Vector search algorithms

Hierarchical Navigable Small Worlds

Inverted File

Vector similarity metrics

Advantage of OpenSearch as a vector database

Scale and limits

Number of vectors

Number of dimensions

Customer stories:

Amazon Music

Brand protection at Amazon

Conclusion

About the Authors

ThoughtSpot: Live analytics for your modern data stack

Integrate ThoughtSpot using Amazon Redshift partner integration

Partner feedback

Conclusion

About the Authors

Overview of Account Creation Fraud Prevention for AWS WAF

Walkthrough

Set up Account Creation Fraud Prevention

To set up ACFP

Add the AWS WAF JavaScript SDK to your application pages

To add the SDK to your application pages

Review metrics

To view AWS WAF metrics for the distribution

Best practices

Limit the requests that ACFP evaluates to help lower costs

Use SDK integration

Use both ACFP and ATP for comprehensive account fraud prevention

Test ACFP before you deploy it to production

Pricing and availability

Conclusion

Update highlights

How to use the new guide

More information

The Ransomware Task Force

Center for Internet Security

What do you do in your current role and how long have you been at AWS?

How did you get started in the data protection and secrets management space? What about it piqued your interest?

Why does secrets management matter to customers today?

What’s been the most dramatic change you’ve seen in the data protection and secrets management space?

With AWS re:Inforce 2023 around the corner, what will your session focus on? What do you hope attendees will take away from your session?

Where do you see the secrets management space heading in the future?

Is there something you wish customers would ask you about more often?

How about outside of work—any hobbies?

Target use cases

Facilitate your security investigations with elevated visibility

Simplify your compliance monitoring and reporting

Analyze multiple years of security data quickly

Unify security data management across hybrid environments

Updates since preview launch

Ecosystem integrations

Get help from AWS Professional Services

Summary

Noteworthy announcements

Artificial intelligence

The human element

Cloud security

Security takes a village

Breakout sessions, chalk talks, and lightning talks

Hands-on sessions (builders’ sessions and workshops)

Cryptographic computing

Query behavior

Conclusion

The collective thoughts of the interwebz