The COVID-19 pandemic has triggered an acute need for healthcare providers to avail the assistance of technology solution providers that offer solutions based on industry-wide regulations like FedRAMP.

Nowadays, IT and tech solutions are developing applications keeping compliance in mind. Healthcare providers and public agencies can implement such digital applications to address the challenges. However, the best digital healthcare application is the one that has gone through the best testing process. We help you know this by discussing the best testing approach for healthcare applications in the context of FEDRAMP.

This blog might be of your interest: Mobile Application Testing Solutions For New-age Mobile Applications

About FedRAMP

The Federal Risk and Authorization Management Program (FedRAMP) is a risk-based approach to adopt and leverage the cloud by the federal government. Established in 2011, it is intended to empower enterprises with new-age cloud capabilities. A cost-effective approach, it emphasizes security of federal information, authorization, and continuous monitoring of cloud’s performance and its products.

Why FedRAMP?

Some of the reasons explaining the importance of FedRAMP are as below:

• FedRAMP governs the security and risk assessment of cloud by providing a set of standards.

• Any application of cloud by a public agency must comply with FedRAMP standards.

• As a rigorous process, FedRAMP evaluation offers a standardized approach to security assessment, authorization, and continuous monitoring. Each of these requirements must be satisfied to prevent falling out of compliance.

• FedRAMP effectively helps reduce duplication and inconsistencies and facilitates cost efficiencies.

• FedRAMP establishes a public-private partnership to promote innovation and the advancement of more secure information technologies. With FedRAMP, you can forge public-private partnerships and drive innovation for more secured IT framework.
• FedRAMP enables the federal government to accelerate the adoption of cloud computing by creating transparent standards and processes for security authorizations and allowing agencies to leverage security authorizations on a government-wide scale.        FedRAMP helps create transparent standards and processes which agencies can leverage and scale with ease. This feature of FedRAMP accelerates cloud adoption by agencies.

Authorization through an agency and process in FedRAMP: 

The Agency Authorization process allows agencies to work with a CSP (Cloud Service provider as and when required. When a CSP decides to work with an agency to avail Authority to Operate (ATO), they get to work with the agency throughout the FedRAMP authorization lifecycle. Listed below are the  different processes that go into the authorization process.

• Readiness Assessment –To achieve the FedRAMP-ready tag, it is essential on the part of CSP to partner with a certified 3PAO (Third Party Assessment Organization). It is then only that completing assessment of readiness would be feasible. The RAR (Readiness Assessment Report) records the capability of CSP to meet security requirements.
• Pre-Authorization–  CSP establishes a formal partnership with an agency through requirements specified in the marketplace. In this process, CSP has to ensure that the leadership is committed to implementing FedRAMP process. Moreover, it has to ensure the security categorization. The last step is to initiate the kickoff meeting so that background functionality and technical security are ensured and there are not compliance gaps.
• Full Security Assessment –Before this process, it is expected that SSP is complete and approved by the agency. An independent audit is conducted by 3PAO with an aim to test the CSP’s system. Security Assessment Report (SAR) are prepared, based on which CSP develops POA&M (Plan of Action for achieving Milestones)
• Agency Authorization Process –. This process involves the agency conducting a review of security authorization package. CSP initiates remediation as per the results of the review. Finally, the agency customer drives risk analysis before issuing an ATO. The CSP and 3ATO then uploads all requisite security material to FedRAMP’s secured repository. This is followed by the review of the security assessment materials to be included in the marketplace.
• Continuous Monitoring – All the agency customers must be kept informed and updated with security deliverables. So, CSP ha to provide periodic deliverables for security. This includes vulnerability scans, annual security assessments, significant change requests, incident reports and updated POA&M. Through the services, each agency conducts periodic (monthly and annual) monitoring of deliverables.

Strategy for compliance testing

Indium will follow several strategies in its digital assurance solutions to perform various compliance tests to ensure the application security. Below are the types to testing requirements involved during testing for FEDRAMP requirements.

Web Application/ Mobile Application /Application Program Interface (API) Testing:

• Cloud Access – Roles associated to the cloud access and access limitations.
• Configuration – Web server configuration level validation.
• Authentication and Session Management – Validates how session is created between browsers/devices and maintains session state.
• Data Storage – Verify Encryption outside application platform.
• API – Perform internet searches to identify any publicly available information on the target web application and make sure all input elements are validated.

High-level Testing activities to meet the FedRAMP Req.:

Check this out : Automation Testing for Financial Services Application

Network & Penetration Testing:

Below is the process that will be ensured as a part of network and penetration testing to ensure the FEDRAMP requirement

• Security assessment schedule
• Describe Tools for security controls
• Injection Analysis & Broken Authentication / Authorization (API & Web)
• Vulnerability Assessment & Penetration Test on API (request & response) & Web.
• Vulnerability analysis on API, APK & iOS

Stages in Vulnerability assessment:

Tools to validate:

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Video livestreaming is streaming multimedia content in real time over the Internet. Using the technology, users can create, share & view live videos through the support of an internet enabled platform. There is also non-live media stream which technically streams but do not broadcast in real time. Live streaming has phenomenal ability to connect with audience with real time across the globe in cost efficient way which earlier was possible only through TV broadcasts.

Live streaming contributes to over 30% of the total video content that is being watched globally.  Post Covid-19, it is estimated that there is a 300% increase in live streaming and it has become the most important forms of communication opening doors for multiple opportunities like students attending live classes, online doctor consultation, artists performing virtual concerts, live commerce shopping, retail customers shopping online, video conferencing, etc.

Live streaming in social media

Even though the concept of live streaming prevailed earlier, it was the social media with its incredible power of connecting peoples across started using it in a much larger scale along with numerous other features like live chatting, closed caption with multi language support, play back speed controls, multi resolution, video replay, controlling audience, applying effects, etc. Statistics mentions that around 23% of social media users are using live streaming to connect with others

However, the platform capability to engage a live audience is challenging as even a minor glitch for a few seconds during the live stream could impact the interest of the audience. The most important aspect of live streaming is to provide high quality end user experience without any imperfections. Is there an efficient way to validate the quality of live streaming? Here are some notable testing points to ensure quality live streaming.

Live stream Test Process

The digital assurance of the live stream testing is a necessity and partnering with an efficient digital assurance services partner is equally important. The following are the process steps for live stream testing.

• ● User feedback

Planning and preparation play a vital role in validating live streaming. The test team should be aware of the items that could potentially cause issues. It is best to maintain a checklist that is created through analysing patterns. One way to create this is through carefully looking into user feedback of similar application and understand what the users are concerned about. A collection of these items will indicate the areas of additional focus.

• ● QA Tools

There are multiple cross platform screen casting and streaming tools available to test different dimensions of live stream. Using various broadcasting, video and audio controls, live streams can be setup and tested.

• ● Standards & Protocols

Each live streaming uses different protocols based on their business needs. However, the efficiency of the video stream depends on the multiple factors. All individual factors contribute to the overall quality and thus be carefully taken care during development and testing. There are various protocol like HTTP Live Streaming (HLS), Real-Time Messaging Protocol (RTMP), WebRTC, Secure Reliable Transport (SRT), etc. Each protocol has its own advantages/disadvantages and knowing it would really understand the scope and expectations. 

• ● Beyond the reach

When a match is broadcasted in television, it is very much live that there is no delay in the telecast even for a few seconds. This is also applicable to live streaming. Latency is the time difference between the video sent from host to the receiver. The ultra low latency ensures video is streaming without any delays and reaches wider audience.

• ● Widest possibility

The compatibility factor in live streaming is responsible for delivering the video content to a larger audience. The technology should make sure that live streaming is possible across multiple devices, platforms and browsers. It would be helpful to understand the audience location and have trending list of popular devices, platforms & browsers based on statistics. These primary platforms should be at least covered during the tests.

• ● Subjective Video quality

Bitrate, video resolution & image sharpness contributes larger part of the video quality. Bitrate is associated with delivering best video quality dynamically to users based on different network capabilities. The video resolution adaptiveness is to be validated with different internet speed to check whether the   system automatically adapts to the best quality video under various conditions. 

• ● User Experience

Whatever technology or features that is developed, it really matters only when audience is convenient in using it. UX/UI and UI/UX testing play a major role in engaging the users with the application. Usability tests should be performed to understand the application usage and issues.

• ● Security & Privacy

The scale of security and privacy related issues in live stream are much higher than other technologies. As the content is targeted to a wider audience across the globe, there should be much responsibility involved to avoid problems. There are content management systems and other security features which restrict text, image, video using AI programmed filters to keep the content safe. The security and privacy aspect of live stream should be extensively tested.

In addition to the above points discussed that are other areas to focus which depends on individual business. There is much scope for live streaming in the coming years as new players and domains have started to use this technology to are Overall 99.999% availability, stability & premium quality are three key customer success metrics that a tester should look for ensure the purpose of the technology is fulfilled.

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The way we work and live has been constantly changing in the last few years. Google CEO Sundar Pichai predicts that the advancement in artificial intelligence and machine learning will be even more revolutionary than the invention of fire.

According to Comptia, 86% of CEOs report that AI is considered mainstream technology in their offices as of 2021. Businesses across the globe are battling labour shortages, economic crises, and many other hurdles that affect business efficiency. Intelligent and comprehensive digital solutions include the use of artificial intelligence and machine learning as they are referred to as the ‘brains’ of smart machines that will help businesses deliver increased business productivity & constructive solutions. Many predictions in the field of artificial intelligence and machine learning are being made that we will see below:

Predictions about AI/ML in Business

• Accessibility and Democratization of Processes: Artificial intelligence and machine learning are no longer the responsibility of a single employee in the IT department. It is available to engineers, support representatives, sales engineers, and other professionals that can make use of it to solve everyday business problems. Machine learning will soon emerge to be the standard tool that is used to solve certain complex computational problems. It will help in personalizing customer experiences and provide an enhanced insight into customer behaviours.
• Enhanced Security for Data Access: AI & ML tools can track and analyze higher network traffics and recognize threat patterns to prevent cyber-attacks. This can be done in conjunction with monitoring the networks in question, detecting malware activities, and other related practices. Enterprises can adopt advanced AI solutions to both monitor data and construct special security mechanisms in their AI models. AI can help by recognizing patterns and suggesting business intentions using smart algorithms. AI-powered security will reach new heights in the days to come.
• Deep Learning to Aid Data Analysis: Deep learning happens after the creation of multiple layers of artificial neural networks to use for processing large amounts of unstructured data. This allows the machine to learn how to analyze and categorize inputs without being specifically instructed on how to handle the task. The use cases for deep learning range from industries such as predictive maintenance to product strategies in software development companies. Some autonomous locomotive and automobile enterprises are already implementing deep learning capabilities into their products. In the future, businesses across industries will increasingly leverage deep learning for data analysis.
• Natural Language Processing Enhancing Use Cases: Natural Language Processing involves both computational linguistics, and the general model of the human knowledge- paired with machine learning, statistical learning, and deep learning models all working closely with each other. NLP can help in making one aware of the subconscious patterns in the organization’s processes- this can help identify strategies to boost business efficiency. It is used both in the legal and commercial space, as dense legal contracts and documents and can be analyzed with speed.

Having got an insight into the probable trends for Artificial intelligence and machine learning, here we discuss a few use cases that are driving the use of AI/ML forward:

Use Cases for AI/ML in 2022

• Machine Learning in Finance: Machine learning techniques are paramount to enhancing the security of transactions by detecting patterns and possibilities of fraud in advance. Credit card fraud detection is an example of improving transactional and financial security through machine learning. These solutions work in real-time to constantly ensure security and generate alerts. Organizations across the globe use machine learning techniques to conduct sentiment analysis for stock market price predictions. In this instance, business trading is aided by the algorithm, where various data sources such as social media data help to perform sentiment analysis.
• Machine Learning in Marketing: Machine learning can aid with considering customer and business objectives while considering purchase patterns, pricing, comparison with other businesses, and mapping marketing points that can align with customer objectives. Content curation and development is an essential component in an era of digital marketing. There are tools that can help to customize the content as per the customer’s preferences and also tools that can help effectively organize content for customers for better engagement. Customization, understanding customers, and creating a memorable experience are all aided by machine learning as seen in the examples of chatbots that use AI technologies.
• Machine Learning in Healthcare: Administrative tasks can be delegated to natural language processing software, which can effectively reduce the physician’s and other healthcare staff’s overall workload. This can help the healthcare staff concentrate better on the patient’s health and spend less time going through legal and manual administrative work. NLP tools can help generate electronic health records and with managing critical administrative tasks in the healthcare industry. The tools would automatically find words and phrases to include in the electronic health record at the patient’s visit. They can create visual charts and graphs that can help the physician understand the patient’s health better.

Also Read: 10 Promising Enterprise AI Trends 2022

AI/ML Paving the Road Ahead for Growth

In 2022, along with the help of artificial intelligence and machine learning technologies, businesses will increasingly try to automate repetitive tasks and processes that involve sifting through large volumes of data and information. It is also possible that businesses will bring down their dependence on the human workforce to improve the overall accuracy, speed, and reliability of the information that is being processed.

AI/ML is usually called disruptive technologies as they are powerful enough to elevate industry practices by assisting organizations in achieving business objectives, making important decisions, and developing innovative services and products. Data specialists, analysts, CIOs, and CTOs alike should consider using these opportunities to efficiently scale their business capabilities to have an edge in the business.

The post AI & ML: Forecasts and Trends for 2022 and beyond appeared first on Indium.

However, this transition necessitates the creation of a detailed migration plan. What method will a corporation use to transfer data? How will the information be organised? How will a business be able to track data?

Setting goals and expectations for what the firm wants to achieve is the first step in migrating data to the cloud. For instance, how much data does the organisation intend to transfer, and at what point will the amount of data to be moved be reduced? Who will take over the project’s duties and responsibilities? What is the budget for the company, and what cloud platform will be used? The planning phase also includes code development and effective cloud migration strategies. The smoother the relocation process, the more well-planned it is.

Today’s organizations rely heavily on data for decision-making. These new-age data users, who aren’t only business intelligence experts, might make up the majority of an organization’s workforce, which is only growing. Considering this, the automated metadata management platform must be user-friendly in order to modernise data. As a result, the user interface must be so basic that users, regardless of their professional expertise, can readily access data.

Having discussed about the importance of data migration to the cloud for BI, let’s look at some of the data modernization trends.

Data Modernization Trends to look out for

Trend #1 – Uniform Implementation

When migrating to the cloud, the entire organisation must approach data in the same way. Business intelligence teams must teach data concepts, such as how to categorise, insert, and access data, so that the entire organisation can communicate in a common language. The analytics created from the data will be inconsistent if two departments use different categories for the same data. It’s also critical for data teams to teach the rest of the firm how to use and analyse data so that it may be put to the best possible use.

Effective data managers, on the other hand, strive to move the correct data to the right place as they transition their data. Rather than merely scaling up their existing data storage solution, data managers are increasingly attempting to turn the data into more valuable configurations. This shift does more than just storage. Data managers strive to improve analysis, interaction with other systems, and other aspects of data management. Instead of replicating local storage for 20 subsidiary offices, a company might construct global repositories based on data classification, such as worldwide marketing, operations records, logistic records etc

Trend #2 – Multi-functional adoption / Wider adoption

Markets that have previously recognised the value of data migration, such as banking, financial services, and insurance (BFSI), are expected to continue to employ it heavily. Similarly, marketing departments across sectors are expected to maintain their market leadership in data migration adoption and use to strengthen their data analysis capabilities.

However, as the benefits of data migration become more widely recognised, new businesses and professions are seeing the most rapid growth. Human resources is expected to be a fast-growing industry, according to Markets and Markets, as HR professionals seek data migration to integrate multiple data sources for improved data analysis.

Similarly, retail and consumer products are expected to expand the most, as companies strive to better understand customers throughout the product and customer life cycle. As the benefits of data migration and data analysis become more widely acknowledged, we can expect this shifting demand between industries and job functions to continue. While we may not be able to forecast which segments will expand the fastest next year, we can surely expect change to continue.

Trend #3 – Automated storage and storage scaling

Large organisations quickly exhaust the capacity of local hard drive arrays due to the general increase in data volume. Data managers must juggle frequent requests for more capacity while also staying ahead of hardware obsolescence and failure. Migrating data to the cloud will not totally solve these problems, but it will alleviate the urgency and complexity.

The hours of installation, configuration, testing, and troubleshooting necessary to install physical hard drives are readily replaced by mouse clicks on the cloud interface. Scaling storage is simple and perhaps automated, which appeals to data managers and drives the trend of moving more data to the cloud.

The largest corporations continue to exceed their current capacity. Markets & Markets predicts that demand for data migration in total dollars will continue to be dominated by these huge enterprises. However, as the cloud’s flexibility becomes more generally acknowledged in 2022, small and medium-sized businesses are likely to drive the fastest-growing section of the data migration market.

Trend #4 – Capitalizing on Integrational features

Companies that are using cloud for their applications are paying close attention to the integration offerings of cloud vendors. For example, Azure’s integration capabilities are becoming increasingly popular among Azure platform customers, and some organisations have begun to build lightweight integration frameworks on top of AWS’ offerings.

Rapid change will always have an impact on technology; it’s the essence of the business. Still, there’s no disputing that we’re in the midst of a generational transformation, with businesses pivoting faster than ever to stay ahead of the pack. Although digital integration is not new, its impact on a company’s long-term performance and importance has never been greater.

Trend #5 – Data restructuring and refined data transfer

Previous data migrations may have merely shifted data from one repository to another, but with AI, data migration cycles are more optimized. As such, data analysis becomes more efficient and effective. Data modernization, which entails migrating data from legacy databases to modern databases, is driven by this need. Unstructured data is categorised and structured, and old databases are restructured and refined, thanks to data modernization. According to a recent Deloitte poll, 84 percent of respondents have begun data transformation initiatives, with the financial services industry leading the way.

Companies who were among the first to migrate data to the cloud are now actively involved in data migration for data modernization. This contributes to the established industries’ status as market leaders in data migration. Marketing departments, for example, may have been the first to migrate data to the cloud for data analysis, but they are now also leading the way in data transfer from one database to another. Thus they are seeking the benefits of scalability, cost savings, flexibility, performance, and extended capability.

This might interest you: 5 Tips For Successful Data Modernization

Wind-up

We recognise that adopting modern technologies may help firms remain competitive and nimble as they seek out flexible, scalable data infrastructure solutions. Those who have yet to modernise, on the other hand, will face an uphill battle in extracting insights from their data. According to a recent IDG poll, more than 90% of data professionals claimed it is difficult to make data available in a format that can be used for analytics.

Companies should continue to improve their data architecture and keep an eye on industry trends and cutting-edge technology to help them compete with data insights. On-premises and legacy tools, on the other hand, were never designed to handle enormous amounts of data in a flexible and scalable manner. As new trends drive significant changes in data management strategy, businesses must prioritise modernization.

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