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In March, Snowflake announced exciting releases, including advances in AI and ML with new features in Snowflake Cortex, new governance and privacy features in Snowflake Horizon, and broader developer support with the Snowflake CLI. Read on to learn more about everything we announced last month. Snowflake Cortex LLM Functions – in public preview Snowflake Cortex is an intelligent, fully managed service that delivers state-of-the-art large language models (LLMs) as serverless SQL/Python functions; there are no integrations to set up, data to move or GPUs to provision. In Snowflake Cortex, there are task-specific functions that teams can use to quickly and cost-effectively execute complex tasks, such as translation, sentiment analysis and summarization. Additionally, to build custom apps, teams can use the complete function to run custom prompts using LLMs from Mistral AI, Meta and Google. Learn more. Streamlit Streamlit 1.26 – in public preview We’re excited to announce support for Streamlit version 1.26 within Snowflake. This update, in preview, expands your options for building data apps directly in Snowflake’s secure environment. Now you can leverage the latest features and functionalities available in Streamlit 1.26.0 — including st.chat_input and st.chat_message, two powerful primitives for creating conversational interfaces within your data apps. This addition allows users to interact with your data applications using natural language, making them more accessible and user-friendly. You can also utilize the new features of Streamlit 1.26.0 to create even more interactive and informative data visualizations and dashboards. To learn more and get started, head over to the Snowflake documentation. Snowflake Horizon Sensitive Data Custom Classification – in public preview In addition to using standard classifiers in Snowflake, customers can now also write their own classifiers using SQL with custom logic to define what data is sensitive to their organization. This is an important enhancement to data classification and provides the necessary extensibility that customers need to detect and classify more of their data. Learn more. Data Quality Monitoring – in public preview Data Quality Monitoring is a built-in solution with out-of-the-box metrics, like null counts, time since the object was last updated and count of rows inserted into an object. Customers can even create custom metrics to monitor the quality of data. They can then effectively monitor and report on data quality by defining the frequency it is automatically measured and configure alerts to receive email notifications when quality thresholds are violated. Learn more. Snowflake Data Clean Rooms – generally available in select regions Snowflake Data Clean Rooms allow customers to unlock insights and value through secure data collaboration. Launched as a Snowflake Native App on Snowflake Marketplace, Snowflake Data Clean Rooms are now generally available to customers in AWS East, AWS West and Azure West. Snowflake Data Clean Rooms make it easy to build and use data clean rooms for both technical and non-technical users, with no additional access fees set by Snowflake. Find out more in this blog. DevOps on Snowflake Snowflake CLI – public preview The new Snowflake CLI is an open source tool that empowers developers with a flexible and extensible interface for managing the end-to-end lifecycle of applications across various workloads (Snowpark, Snowpark Container Services, Snowflake Native Applications and Streamlit in Snowflake). It offers features such as user-defined functions, stored procedures, Streamlit integration and direct SQL execution. Learn more. Snowflake Marketplace Snowflake customers can tap into Snowflake Marketplace for access to more than 2,500 live and ready-to-query third-party data, apps and AI products all in one place (as of April 10, 2024). Here are all the providers who launched on Marketplace in March: AI/ML Products Brillersys – Time Series Data Generator Atscale, Inc. – Semantic Modeling Data paretos GmbH – Demand Forecasting App Connectors/SaaS Data HALitics – eCommerce Platform Connector Developer Tools DataOps.live – CI/CD, Automation and DataOps Data Governance, Quality and Cost Optimization Select Labs US Inc. – Snowflake Performance & Cost Optimization Foreground Data Solutions Inc – PII Data Detector CareEvolution – Data Format Transformation Merse, Inc – Snowflake Performance & Cost Optimization Qbrainx – Snowflake Performance & Cost Optimization Yuki – Snowflake Performance Optimization DATAN3RD LLC – Data Quality App Third-Party Data Providers Upper Hand – Sports Facilities & Athletes Data Sporting Group – Sportsbook Data Quiet Data – UK Company Data Manifold Data Mining – Demographics Data in Canada SESAMm – ESG Controversy Data KASPR Datahaus – Internet Quality & Anomaly Data Blitzscaling – Blockchain Data Starlitics – ETF and Mutual Fund Data SFR Analytics – Geographic Data SignalRank – Startup Data GfK SE – Purchasing Power Data —- ​​Forward-Looking Statement This post contains express and implied forward-looking statements, including statements regarding (i) Snowflake’s business strategy, (ii) Snowflake’s products, services, and technology offerings, including those that are under development or not generally available, (iii) market growth, trends, and competitive considerations, and (iv) the integration, interoperability, and availability of Snowflake’s products with and on third-party platforms. These forward-looking statements are subject to a number of risks, uncertainties, and assumptions, including those described under the heading “Risk Factors” and elsewhere in the Quarterly Reports on Form 10-Q and Annual Reports of Form 10-K that Snowflake files with the Securities and Exchange Commission. In light of these risks, uncertainties, and assumptions, actual results could differ materially and adversely from those anticipated or implied in the forward-looking statements. As a result, you should not rely on any forward-looking statements as predictions of future events. © 2024 Snowflake Inc. All rights reserved. Snowflake, the Snowflake logo, and all other Snowflake product, feature, and service names mentioned herein are registered trademarks or trademarks of Snowflake Inc. in the United States and other countries. All other brand names or logos mentioned or used herein are for identification purposes only and may be the trademarks of their respective holder(s). Snowflake may not be associated with, or be sponsored or endorsed by, any such holder(s). The post New Snowflake Features Released in March 2024 appeared first on Snowflake. View the full article

Financial institutions and healthcare providers deal with a vast amount of sensitive data like PII and PHI data, from Social Security numbers (SSN) to credit card information and medical records. Often, this data resides in various file formats like Excel, CSV, and others, stored on-premises or in cloud object storage. As companies migrate these files… The post A Case Study for Protecting Files with Sensitive Data in the Cloud appeared first on Baffle. The post A Case Study for Protecting Files with Sensitive Data in the Cloud appeared first on Security Boulevard. View the full article

This quick guide breaks down the steps of achieving CMMC so your business can protect sensitive government data. The post How to Get CMMC Certified appeared first on Scytale. The post How to Get CMMC Certified appeared first on Security Boulevard. View the full article

Amazon Macie has introduced new managed data identifiers to expand its capabilities for discovering and identifying Stripe API keys, Google Cloud API keys, Driver’s license numbers (India) and national identification numbers (India) in Amazon Simple Storage Service (Amazon S3). Understanding the presence and location of such data in your S3 storage helps you to better plan data security, governance, and privacy of your organization. With over 100+ managed data identifiers, Macie helps protect your sensitive data at scale. View the full article

Today we are announcing Amazon CloudWatch Logs data protection, a new set of capabilities for Amazon CloudWatch Logs that leverage pattern matching and machine learning (ML) to detect and protect sensitive log data in transit. While developers try to prevent logging sensitive information such as Social Security numbers, credit card details, email addresses, and passwords, sometimes it gets logged. Until today, customers relied on manual investigation or third-party solutions to detect and mitigate sensitive information from being logged. If sensitive data is not redacted during ingestion, it will be visible in plain text in the logs and in any downstream system that consumed those logs. Enforcing prevention across the organization is challenging, which is why quick detection and prevention of access to sensitive data in the logs is important from a security and compliance perspective. Starting today, you can enable Amazon CloudWatch Logs data protection to detect and mask sensitive log data as it is ingested into CloudWatch Logs or as it is in transit. Customers from all industries that want to take advantage of native data protection capabilities can benefit from this feature. But in particular, it is useful for industries under strict regulations that need to make sure that no personal information gets exposed. Also, customers building payment or authentication services where personal and sensitive information may be captured can use this new feature to detect and mask sensitive information as it’s logged. Getting Started You can enable a data protection policy for new or existing log groups from the AWS Management Console, AWS Command Line Interface (CLI), or AWS CloudFormation. From the console, select any log group and create a data protection policy in the Data protection tab. When you create the policy, you can specify the data you want to protect. Choose from over 100 managed data identifiers, which are a repository of common sensitive data patterns spanning financial, health, and personal information. This feature provides you with complete flexibility in choosing from a wide variety of data identifiers that are specific to your use cases or geographical region. You can also enable audit reports and send them to another log group, an Amazon Simple Storage Service (Amazon S3) bucket, or Amazon Kinesis Firehose. These reports contain a detailed log of data protection findings. If you want to monitor and get notified when sensitive data is detected, you can create an alarm around the metric LogEventsWithFindings. This metric shows how many findings there are in a particular log group. This allows you to quickly understand which application is logging sensitive data. When sensitive information is logged, CloudWatch Logs data protection will automatically mask it per your configured policy. This is designed so that none of the downstream services that consume these logs can see the unmasked data. From the AWS Management Console, AWS CLI, or any third party, the sensitive information in the logs will appear masked. Only users with elevated privileges in their IAM policy (add logs:Unmask action in the user policy) can view unmasked data in CloudWatch Logs Insights, logs stream search, or via FilterLogEvents and GetLogEvents APIs. You can use the following query in CloudWatch Logs Insights to unmask data for a particular log group: fields @timestamp, @message, unmask(@message) | sort @timestamp desc | limit 20 Available Now Data protection is available in US East (Ohio), US East (N. Virginia), US West (N. California), US West (Oregon), Africa (Cape Town), Asia Pacific (Hong Kong), Asia Pacific (Jakarta), Asia Pacific (Mumbai), Asia Pacific (Osaka), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada (Central), Europe (Frankfurt), Europe (Ireland), Europe (London), Europe (Milan), Europe (Paris), Europe (Stockholm), Middle East (Bahrain), and South America (São Paulo) AWS Regions. Amazon CloudWatch Logs data protection pricing is based on the amount of data that is scanned for masking. You can check the CloudWatch Logs pricing page to learn more about the pricing of this feature in your Region. Learn more about data protection on the CloudWatch Logs User Guide. — Marcia View the full article

We are excited to announce data protection in Amazon CloudWatch Logs, a new set of capabilities that leverage pattern matching and machine learning capabilities to detect and protect sensitive log data-in-transit. Amazon CloudWatch Logs enables you to centralize the logs from all of your systems, applications, and AWS services, in a single, highly scalable service. With log data protection in Amazon CloudWatch Logs, you can now detect and protect sensitive log data-in-transit logged by your systems, and applications. View the full article