AWS HIPAA Compliance Best Practices Checklist

Nowadays, most medical providers across the globe tend to implement cloud-based architecture for their medical services. And it’s not surprising, especially considering today’s pandemic reality; medical software is a must. However, to build a highly secure solution to deliver medical services, you must abide by the US 1996 law, namely the HIPAA Security Rule. This legislation represents a set of required and adequate protections for managing electronic confidential patient information and avoiding its disclosure without prior patient’s knowledge and even consent.

So, if you want to develop a medical solution and make your healthcare services cloud-based, you will have to apply the latest technologies for maintaining data compliance. To build cloud-based apps according to the Privacy Rule, most healthcare providers apply Amazon Web Services (AWS) due to its increased agility, security, and innovation potential.

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Understanding read and readUrl Functions With MuleSoft DataWeave

read Function

read function is used to read the string or binary and returned parsed content. It is a very useful function when the reader isn’t able to determine the content type by default.

It takes three parameters:

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How to Use Mulesoft VM Connector

VM Connector is used for intra-app (within the app) and inter-app communication through either transient or persistent asynchronous queues.               

  • Transient queues: This type of queue is volatile means data would be lost in case of system crashes or restart. Transient queues are faster than persistent queues.
  • Persistent queues: This type of queues are more reliable, data would be persisted in case of a system crash or failure, or restart. These queues are slower than Transient Queues.

VM Connector is mainly used for the following: 

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The Value of Internal APIs

Internal APIs are designed primarily to streamline software development and simplify systems and operational processes. These currently represent the vast majority of use cases.

Internal APIs are often overlooked since they are aimed at in-house developers. These types of APIs generally work with proprietary data specific to a company and its departments. Although this data must be protected, it must also be accessible to those who work with it. Internal APIs allow for exactly this kind of secure access, creating more efficient development cycles for their products.

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My Picks for Shifting Left: ‘21

Here at ShiftLeft, we are gearing up for Shifting Left: ’21, a one-day application security conference for developers and security practitioners on Jan 28, 2021. I’ve been a huge fan of security conferences ever since I attended my first security conference, NorthSec in Montreal. This time, I am excited to be on the organizer’s side and present this conference to you.

Shifting Left: ‘21 is entirely online and free to register here. Now let’s get into it! Here are the sessions that I am most excited about and that you should attend if you like machine learning, developing secure applications, or hacking into applications.

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Logging With New Relic for Mulesoft APIs

What Is New Relic?

New Relic is web application performance service designed to figure in real-time together with your live web app. New Relic Infrastructure provides flexible, dynamic server monitoring. You can see the important performance data of your app in New Relic, like browser reaction time by geography and browser type, web transactions in real-time, etc.

Steps to Registering With New Relic

  1. Go to
  2. Click on Sign Up if you do not have an account already.

New Relic sign-up screenshot.

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Getting Started With AWS Monitoring

Amazon Web Services (AWS) is the most popular public cloud, with 175 services and counting. A key element of a successful cloud operation is gaining visibility into what is running where, what issues are occurring, and dealing with them, preferably automatically.

In this article, I’ll discuss the basics of AWS monitoring, including Amazon services that can assist with monitoring, key metrics to watch for the most popular Amazon services, and a special focus on monitoring EC2 environments, which are the basis for most Amazon deployments.

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Streaming Data From Files Into Multi-Broker Kafka Clusters

There are multiple ways to ingest data streams into the Apache Kafka topic and subsequently deliver to various types of consumers who are hooked to the topic. The stream of data that collects continuously from the topic by consumers, passes through multiple data pipelines and then stream processing engines like Apache Spark, Apache Flink, Amazon Kinesis, etc and eventually landed upon the real-time applications to deliver a final data-driven decision. From finances, manufacturing, insurance, telecom, healthcare, commerce, and more, real-time applications are becoming the best solution for organizations to take immediate action, gain insights from the updated data. In the present day, Apache Kafka shapes the central nervous system that brings data from all aspects of the business to the large information operational hubs where choices are made.

The text files contain unformatted ASCII text and are commonly used for the storage of information. Each line of the file represents a data record and can be updated continuously to store. Every insert of a new line or lines on the text file can be considered as new data insertion on the file. Henceforth, every addition of a new line or lines on the text file continuously either by humans or applications (no modification on the already inserted line)and subsequently moves or sends to a different location can be considered as data streaming from the file. Every addition of a new line or row in the text file can be analyzed continuously by exporting the new line/lines to the Kafka topic and importing them by consumers that hooks up with the topic.

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Vector Similarity Search Hides in Plain View

Imagine a room with a wall of screens displaying closed-circuit video feeds from dozens of cameras, like a security office in a film. In the movies, there is often a guard responsible for keeping an eye on the screens that inevitably falls asleep, allowing something bad to happen. Although intuition and other distinctly “people skills” are useful in security, most would agree that the human attention span isn’t well-suited for always-on, 24/7 video monitoring. Of course, footage can always be reviewed after something happens, but it’s easy to see the security value of detecting something out of the ordinary as it unfolds.

Several cameras capturing different scenes.
Cameras capture our every move, but who watches them?

Now imagine a video artificial intelligence (AI) application capable of processing thousands of camera feeds in real-time. The AI constantly compares new footage to historical footage, then classifies anomalous events by their threat level. Humans are still involved, both to manage the system as well as review and respond to potential threats, but AI takes over where we fall short. This isn’t a hypothetical situation: from smart police drones to intelligent doorbells sold by Amazon and Google, AI-powered surveillance solutions are becoming increasingly sophisticated, affordable, and ubiquitous.

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How to Create a Messaging Server in Low-Code

In this sample, we will implement the server-side of a client-server style messaging app. To do so, you could use your preferred programming language, but for speed, we’ll use Linx, a low-code developer tool for backend APIs, integrations, and automation.

For a quick review of Linx and how it works, see this video.


We will implement some web methods, which will be useful for creating a messaging client. However, the following items fall outside the scope of this sample:

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