Tag: CloudWatch Logs

A wide range of solutions ingest data, store it in Amazon S3 buckets, and share it with downstream users. Often, the ingested data is coming from third-party sources, opening the door to potentially malicious files. This post explores how Antivirus for Amazon S3 by Cloud Storage Security allows you to quickly and easily deploy a multi-engine anti-malware scanning solution to manage file protection and malware findings.

Many customers operate in a hybrid environment with on-premises infrastructure interconnected with a cloud provider’s infrastructure. This post details how HCL Technologies used the AWS Centralized Log Management Reference Architecture, and discusses how HCL removed the requirements for Amazon Kinesis Data Streams. We also explore how HCL used Amazon Kinesis Data Firehose to stream from an Amazon CloudWatch Logs destination in a centralized logging account.

Data security has always been an important consideration for organizations when complying with data protection regulations. Protegrity, an AWS ISV Partner and global leader in data security, has released a serverless User Defined Function (UDF) that adds external data tokenization capabilities to the Amazon Athena platform. Learn how customers can use the Protection Athena Protector UDF to tokenize or detokenize data at scale.

Learn how TalaTek uses Amazon CloudWatch as an alternative to traditional SIEM solutions with the same functionality. CloudWatch gives TalaTek system-wide visibility, allowing them to monitor their AWS-based SaaS solution, the TalaTek Intelligent Governance and Risk Integrated Solution (TiGRIS). CloudWatch helps TalaTek keep TiGRIS running efficiently while allowing us to meet FedRAMP logging requirements.

Building web applications in a serverless environment has brought many advantages, but with every big shift there are certain practices such as malware protection that need to be reinvented. Organizations need a solution that’s easy to query from web application via API with no infrastructure required. Learn how SophosLabs Intelix offers a suite of APIs which provide specific, actionable intelligence about files.

Automatically identifying and upgrading existing SSD volumes to take advantage of the new gp3 general purpose volumes for Amazon EBS can help organizations reduce storage costs. Learn how to upgrade your existing gp2 volumes, without interruption, to the next generation of general purpose SSD volumes using AWS Systems Manager. This a core component of nubeGo’s Cloud Managed Service (NCMS) which helps customers automate cost savings, security guardrails, and compliance requirements with minimal effort.

Many companies are using Amazon Redshift to analyze and transform their data. As data continues to grow and become even more important, they are looking for more ways to extract valuable insights. One use case we’re especially excited to support is that of data tokenization and masking. Amazon Redshift has collaborated with Protegrity, an AWS Advanced Technology Partner, to enable organizations with strict security requirements to protect their data while being able to obtain the powerful insights.

Customers are always looking for new ways to improve operational efficiency and the security posture of applications running in their virtual private clouds (VPCs). Amazon Route 53 recently launched a Resolver Query Logs capability which lets customers log the DNS queries originating in their Amazon VPC. Follow along with our step-by-step instructions for logging VPC DNS queries in Amazon Route 53, ingesting them into Splunk, and then analyzing them with Splunk.

Learn how to integrate a customer’s LoRaWAN devices connected to The Things Stack’s LoRaWAN Network Server with the AWS IoT Core service running in their account. This integration enables you to unleash the functionality available in the AWS IoT portfolio of services (and overall AWS ecosystem) for implementing solutions. The Things Stack is maintained by The Things Industries (TTI) and provides an integrated chain of products and services for customers to start working on IoT.

Inference is an important stage of machine learning pipelines that deliver insights to end users from trained neural network models. These models are deployed to perform predictive tasks like image classification, object detection, and semantic segmentation. However, constraints can make implementing inference at scale on edge devices such as IoT controllers and gateways challenging. Learn how to train and convert a neural network model for image classification to an edge-optimized binary for Intel FPGA hardware.