Transform your Azure Ecosystem with AIOps to Increase Operational Efficiency

The cloud is now a primary place for SMEs and other large enterprises, and Microsoft’s Azure is considered one of the preferred IaaS and PaaS services for most business organizations.

As Artificial Intelligence and Machine Learning are changing the digital way of life, AIOps is set to uplift cloud services and make operations easy for the IT industry. It provides users with a broader range of benefits, including better customer experience, service quality assurance, and productivity boost.

Why Does Your Organization Need AIOps With Microsoft Azure Ecosystem

As cloud usage is in high demand, businesses are facing problems in managing their cloud infrastructure. AIOps for Azure provides better efficiency with the help of AI-driven software, ensuring smoother operations.

By executing AI operations and ML on Microsoft Azure, organizations can be benefited in many ways. Some of these are:

Efficient and Cost-Effective Infrastructure

Microsoft Azure helps lower the overall cost of a business when enabled with AIOPs and MLOps. AI and ML help make Azure cloud a better choice for Machine Learning Operations and Artificial Intelligence Operations.

Edge Computing

Edge processing aims to bring data resources closer to the users, thus improving the overall performance of the cloud infrastructure. It also helps reduce cost and increase processing capacity simultaneously.

Pre-Trained Machine Learning Models

The Microsoft Azure Platform offers pre-trained models. These can be used for a custom model for tailor-made processing of the company’s workloads. Many ML programs can be used as models through MicrosoftML for Python and MicrosoftML for R for various functions.

Manage Your Azure Infrastructure Easily With AIOps

Microsoft Azure is a reliable cloud service that manages data efficiently. As the cloud is always increasing and becomes complex as each day passes, it needs more developers and engineers to make it stable. It can become quite easy to remain at par with the constantly evolving cloud if there were a solution to make data-based decisions automatically.

Not only will this save a lot of time for the resources of your organization, but also make the process more efficient. AIOps and machine learning help streamline the process and assist engineers in taking actions based on the insights from the existing data.

AIOps is based on self-monitoring and requires no human intervention. Automation of services ensures improved service quality, reliability, availability, and performance.

Azure cloud professionals are no longer required to investigate the repeated process and manually operate the infrastructure. Instead, they use AI and ML engineering. AI operations can work independently, and human resources can utilize their time to focus on solving bigger problems and building new functions.

Design Your Own Growth Path by Systemizing Your Operations With AIOps

The AIOps framework can contribute in several ways. The major elements are explained below.

  • Extensive and Diversified IT Data: AIOps is predicted to bring together data from IT operations management and IT service management. Bringing data from different sources helps accelerate root cause identification of a problem and enables automation simultaneously.
  • Big Data Platform: The center of an AIOps platform is big data. As data is collected from different sources, it is required to be compiled together to support next-level analytics. AIOps aggregates big data and makes it accessible to be used in real-time.
  • Machine Learning: Analysing big data is not possible by humans alone. ML automates and analyzes new and diversified data with a speed that is unachievable without the AIOps framework.
  • Observation: It is the emerging of the traditional ITO domain and other non-ITOM data to enable new models and correlations. The combination of AIOps with real-time processing makes root cause identification easier.
  • Engagement: The traditional domain offers bi-directional communication to support data analysis and, thus, auto-creates documentation for audit while maintaining compliance. AIOps help in cognitive classification with routing and intelligence along with user touchpoints.
  • Act: This is the final stop for the AIOps strategy. It provides the codification of human knowledge into automation. It helps automate analysis, workflow, and documentation for further actions.

What’s Does the Future Have in Store for IT Operations?

Artificial Intelligence for IT operations is bringing a continuous change in the cloud business. In no time, adopting the AIOps way will become a necessity.

  • Accelerate Digital Transformation: Sooner than later, businesses will be able to offer data-driven experiences with the help of AIOps. It won’t be a hassle to migrate systems after systems, as most of the monotonous work will be handled by automated systems. This way, businesses can easily transform digitally to remain relevant
  • Solutions to Various Challenges: Often, when humans spend time performing basic calculations, a lot of time and energy is wasted. Moreover, there is always a chance of human error. Empowering developers with actionable insights, AIOps will make solving problems hassle-free, replacing many traditional monitoring tools
  • Finding Issues Automatically: A faster and more efficient way to improve customer satisfaction involves ensuring that there are no problems with your service or product. However, this can be challenging. With AIOps solutions, identifying issues and mitigating them will be a cakewalk. It will play an essential role in troubleshooting workloads and understanding and predicting customer needs in the current competitive environment, eliminating the need for having a dedicated team of resources to solve simple issues.

How Does AIOps Transform a Business?

1. Digitization of Routine Practices

The AIOps architecture helps digitize routine practices, like user requests, while processing and fulfilling them automatically. It can even evaluate whether an alert requires action and if all the supporting data is under normal parameters.

2. Recognizing Serious Issues Faster and More Accurately

There are chances of human error while looking out for threats. This may lead to an unusual download being ignored. AIOps tools tackle can solve this problem easily. It can run an antimalware function through the system, automatically and when required.

3. AIOps Streamline the Interactions Between Data Center Groups and Various Teams

AIOps shares all the relevant data with each IT group and provides the operations team with what they require. Manually meeting and sending data is no more required, as AIOps monitors data for each team to streamline the interactions between all groups.

Conclusion

With the help of Microsoft Azure, the value of companies associated with this ecosystem is scaling in an upward direction. To conclude, it can be rightly said that AIOps is the infusion of AI into cloud technology. When properly implemented, AIOps can help reduce time and attention on the IT staff of an organization.

AIOps open-source tools allow Azure cloud professionals to observe multiple systems and resources. With better ML capabilities, it can enable software to find the root cause of a problem and accelerate troubleshooting by providing the right remedies for all unusual issues of an IT organization running on Microsoft Azure.

Business Continuity During the Pandemic with VDI

The global COVID-19 pandemic has forced organizations around the world to adopt business processes & working styles they never imagined would be practical, sustainable, or productive in the long term. With governments suspending all business activities in the wake of the pandemic, and with nationwide lockdowns & travel restrictions, core business processes have been challenged. Thrown into the ocean of uncertainty, businesses have had no choice but to adapt in order to keep their heads above the water and swim to safety!

One of the main challenges for organizations during the pandemic has been to ensure a secure workplace for their employees. To steer through the challenges posed by the pandemic, businesses have had to embrace the remote work culture. Work From Home (WFH) which was once a privilege quickly became the only possible way to work! To that end, VDI (Virtual Desktop Infrastructure) hasproved to be the game-changer, enabling employees to connect & work securely from the comfort of their homes.

What is VDI?

Virtual Desktop Infrastructure (VDI) allows employees to securely access desktop applications and servers from outside their physical office premises. They are provided with computing capacity via virtual machines, enabling them to work from any remote location. Virtual machines allow a business to deploy applications and operating systems via a dedicated host server.

An organization can deploy business data, dedicated applications, and many more components of IT infrastructure to employees through a VDI. Business resources and applications are deployed to the employees in a virtual data center. Virtual desktop resources are delivered as a service by leveraging the internet. Unlike a physical desktop, you don’t need an endpoint device to connect to these business resources. The virtual desktop resources are managed in a data center and are delivered over a reliable network. Employees can use the applications deployed via VDI as if they are running locally – from any device.

Desktop Virtualization and VDI

Desktop virtualization creates a virtual version of the end user’s desktop environment. A virtual OS is created from the end user’s physical desktop via virtualization. VDI is a deployment model of desktop virtualization that is used by businesses for remote work. There are other deployment models for desktop virtualization like remote desktop services and DaaS (Desktop-as-a-Service).

In VDI a desktop image travels through the internet to the employee’s device. VDI is always deployed with the help of a server in a data center. Once deployed, users can interact with desktop applications and operating systems remotely.

Why is VDI a great solution?

Many firms around the globe have already started using VDI for providing a secure workplace to their employees. The major pros of VDI that ensure seamless business continuity are:

  • Centralized Monitoring:

With centralized monitoring and management VDI enables tighter control and saves a lot of time & effort for routine IT tasks. IT administrators can modify or patch virtual desktops at any time and can make changes to all the virtual desktops in a network at the same time.

  • Data Recovery:

With VDI, essential business data will be backed up in the data center. This data backup process is typically automated. Employees can run a virtual desktop recovery program if they face abrupt shutdowns or connection losses. If an end user’s device gets stolen, the connectivity of that device to the VDI can be terminated. Such features ensure that data is always safeguarded.

  • Enhanced Accessibility:

VDI empowers users to securely access their desktop from anywhere, at any time, and from any device, to gain access to their business files & applications. They do not have to use high-performing devices as computing capacity is also deployed under VDI – any PC, tablet, smartphone, or thin client can be used. This helps create a flexible work environment for employees.

  • Personalized Work Environment:

Virtual desktop environments are highly customizable to suit user preferences and business needs. For any ad-hoc requirements, virtual desktops can be quickly customized as required – much faster than a physical desktop.

Is VDI Cost-effective?

A business does not have to invest in any special hardware to deploy VDI. No specialized training is required to be able to use VDI. Virtual desktops are easy to manage & use and work exactly like a physical desktop. Deployment of large-scale virtual desktops involves much lower IT costs than physical infrastructure. For example, deploying a virtual desktop Mac would be less expensive than buying an Apple desktop. Resources can be deployed as required, and when needed, resulting in reduction in wastage and IT operating costs.  

In a Nutshell

The global VDI market size is expected to be USD 30 billion by 2026. VDI lends itself reliably to organizations as a business continuity strategy. It also empowers employees with tremendous flexibility in their work environment. Choose a reliable VDI for your organization and deliver a seamless working experience today! You can find information on zDesk, GAVS’ award-winning VDI + DaaS solution here.

Large Language Models: A Leap in the World of Language AI

In Google’s latest annual developer conference, Google I/O, CEO Sundar Pichai announced their latest breakthrough called “Language Model for Dialogue Applications” or LaMDA. LaMDA is a language AI technology that can chat about any topic. That’s something that even a normal chatbot can do, then what makes LaMDA special?

Modern conversational agents or chatbots follow a narrow pre-defined conversational path, while LaMDA can engage in a free-flowing open-ended conversation just like humans. Google plans to integrate this new technology with their search engine as well as other software like voice assistant, workplace, gmail, etc. so that people can retrieve any kind of information, in any format (text, visual or audio), from Google’s suite of products. LaMDA is an example of what is known as a Large Language Model (LLM).

Introduction and Capabilities

What is a language model (LM)? A language model is a statistical and probabilistic tool which determines the probability of a given sequence of words occurring in a sentence. Simply put, it is a tool which is trained to predict the next word in a sentence. It works like how a text message autocomplete works. Where weather models predict the 7-day forecast, language models try to find patterns in the human language, one of computer science’s most difficult puzzles as languages are ever-changing and adaptable.

A language model is called a large language model when it is trained on enormous amount of data. Some of the other examples of LLMs are Google’s BERT and OpenAI’s GPT-2 and GPT-3. GPT-3 is the largest language model known at the time with 175 billion parameters trained on 570 gigabytes of text. These models have capabilities ranging from writing a simple essay to generating complex computer codes – all with limited to no supervision.

Limitations and Impact on Society

As exciting as this technology may sound, it has some alarming shortcomings.

1. Biasness: Studies have shown that these models are embedded with racist, sexist, and discriminatory ideas. These models can also encourage people for genocide, self-harm, and child sexual abuse. Google is already using an LLM for its search engine which is rooted in biasness. Since Google is not only used as a primary knowledge base for general people but also provides an information infrastructure for various universities and institutions, such a biased result set can have very harmful consequences.

2. Environmental impact: LLMs also have an outsize impact on the environment as these emit shockingly high carbon dioxide – equivalent to nearly five times the lifetime emissions of an average car including manufacturing of the car.

3. Misinformation: Experts have also warned about the mass production of misinformation through these models as because of the model’s fluency, people can confuse into thinking that humans have produced the output. Some models have also excelled at writing convincing fake news articles.

4. Mishandling negative data: The world speaks different languages that are not prioritized by the Silicon Valley. These languages are unaccounted for in the mainstream language technologies and hence, these communities are affected the most. When a platform uses an LLM which is not capable of handling these languages to automate its content moderation, the model struggles to control the misinformation. During extraordinary situations, like a riot, the amount of unfavorable data coming in is huge, and this ends up creating a hostile digital environment. The problem does not end here. When the fake news, hate speech and all such negative text is not filtered, it is used as a training data for next generation of LLMs. These toxic linguistic patterns then parrot back on the internet.

Further Research for Better Models

Despite all these challenges, very little research is being done to understand how this technology can affect us or how better LLMs can be designed. In fact, the few big companies that have the required resources to train and maintain LLMs refuse or show no interest in investigating them. But it’s not just Google that is planning to use this technology. Facebook has developed its own LLMs for translation and content moderation while Microsoft has exclusively licensed GPT-3. Many startups have also started creating products and services based on these models.

While the big tech giants are trying to create private and mostly inaccessible models that cannot be used for research, a New York-based startup, called Hugging Face, is leading a research workshop to build an open-source LLM that will serve as a shared resource for the scientific community and can be used to learn more about the capabilities and limitations of these models. This one-year-long research (from May 2021 to May 2022) called the ‘Summer of Language Models 21’ (in short ‘BigScience’) has more than 500 researchers from around the world working together on a volunteer basis.

The collaborative is divided into multiple working groups, each investigating different aspects of model development. One of the groups will work on calculating the model’s environmental impact, while another will focus on responsible ways of sourcing the training data, free from toxic language. One working group is dedicated to the model’s multilingual character including minority language coverage. To start with, the team has selected eight language families which include English, Chinese, Arabic, Indic (including Hindi and Urdu), and Bantu (including Swahili).

Hopefully, the BigScience Project will help produce better tools and practices for building and deploying LLMs responsibly. The enthusiasm around these large language models cannot be curbed but it can surely be nudged in a direction that has lesser shortcomings. Soon enough, all our digital communications—be it emails, search results, or social media posts —will be filtered using LLMs. These large language models are the next frontier for artificial intelligence.

About the Author –

Priyanka Pandey

Priyanka is a software engineer at GAVS with a passion for content writing. She is a feminist and is vocal about equality and inclusivity. She believes in the cycle of learning, unlearning and relearning. She likes to spend her free time baking, writing and reading articles especially about new technologies and social issues.

Exceptional Customer Experience at the Heart of Great Products

The Customer Experience Strategy

Apple Inc stands out as one of the most innovative and customer-focused companies in the world. Their brand positioning and value from its products has catapulted them to one of the most valuable brands in the market today. The visionary responsible for Apple’s monumental growth is none other than its founder Steve Jobs. The fundamental principle he followed in all his strategies was to keep his customers at the center and simplify their lives with Apple products.

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All of Apple’s products had a customer-first approach and they invested heavily on understanding customers and their pain points. The products aimed for the best customer experience across different domains and ensured that every user craved to use an Apple product. This strategy transformed Apple into a religion from a technology company. When we look at the way we currently solve customer problems, we tend to start with the technological feasibility and then work towards solving the problem at hand. If the solutions are not feasible from a technological standpoint, certain customer needs are compromised.

Taking customer needs as a primary lever over technology is a very challenging move. Here organizations must be ready to adapt and experiment with little to no historical data to solve customer problems that are in front of them. This would require them to challenge the traditional ways in which they look at technology and the approach towards customer-centricity.

Customer Experience Foundations

The foundation of all customer experiences focuses on the cumulation of the value provided to the customer during their interaction with the brand. At all stages of the customer journey, the customer experience encompasses all the ways a customer interacts with the brand.

When we look at it from a product standpoint, the total product experience is the primary value offered to the customers. Here we have to take into account how the customer experiences the product, how the product delivers a lasting impression, and helps build a connection with the brand.

For businesses to succeed, a positive customer experience is crucial. A loyal customer can boost your revenue to eventually promote and advocate for you. This brings in more business from their network.

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Variables Influencing Customer Experience

Nowadays, no company can afford to provide a substandard customer experience regardless of the industry, their experience in the market, or their reputation. The way organizations deal with customers influence retention rates, brand value, and finally the financial performance. Given these facts, there are a couple of variables that are responsible for the overall customer experience.

1. Customer-Centric Culture

Businesses that treat their customers as king, the most prized asset for the organization, have reported higher returns than compared to their counterparts who do not emphasize the stance.

A customer-centric culture revolves around solving customer problems and adding real value at the end of the day. The organization’s leaders must take the effort to ensure that the teams focus on providing consistent customer experience through their marketing, sales cycle, and during the customer service phase.

A customer-centric work culture brings in the values of being there for the customer, solving their critical needs, and supporting them through the resolution process.

2. Product Value

A product that doesn’t solve customer needs and problems does not add value at all. Products that target all the pain points of the customer’s needs and expectations require lesser post-sales support than a product that does not.

Sustained success lies in a well-built product. Regardless of the brand’s industry and specialization, the product is what defines the brand. Even though marketing, sales, and customer service are required for a business to thrive, the brand will fail if the product is not effective.

3. Customer Touch Points

A connected customer is vital to all organizations. Reliability of brand strongly revolves around how easily the customer can approach representatives to either know about the product or solve some issues post the sale. Hence, providing customers with effective touchpoints from multiple channels is important to keep customers engaged and be readily available to solve any issues they are facing. These channels and touchpoints can include email, phone, text, instant messaging, social media, website, or even a third-party review site. All focus on getting connected to the customer and being there to address their needs.

4. Technology

Technologies have enabled brands to connect with customers deeper than ever before. Companies now use technology to prevent and avoid losses and create solutions to their shortcoming in their customer experience strategies. Personal analytics instruments help organizations get real-time feedback and analyze the customer pulse.

Technology enables brands to modernize and structure their products effectively and maximize their efficiency. It helps reduce or eliminate labor-intensive customer requests and speeds up the completion time of the processes. This empowers brands to have additional functionalities and cut costs at the same time.

5. People

A unified team of individuals makes a successful customer experience possible. Suppliers, marketers, salesmen, customer service agents, and many others play an essential role in delivering the best-in-class customer experience. For this to happen, each of these individuals must be well-versed with the organizational strategy and have the morale to implement an impactful customer experience that adds value.

Value-Driven Customer Experiences

When customers show their interest towards a brand’s product, they invest their time and money in the entire process. From the product, customers aim to gain business benefits that drive value for the brand. Out of all the variables that influence the customer experience, the product pricing and the contract value has an indirect influence on the type of relationship the customers will have with the brand.

A study conducted by Christopher Meyer and Andre Schwager on “Understanding Customer Experience” published in the Harvard Business Review, talks about classifying customers based on the billed revenues and their satisfaction scores.

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Here we can see that 4 different types of customers are formed out of the matrix. Dangling, Growth, At-Risk and Model Customers. An ideal business would always strive to maximize the number of model customers that churn out high volume of billed revenue and are satisfied with the service that they receive. 

The challenge here is to create a state where customers are comfortable with investing in a product that can guarantee business benefits out of its utilization. To achieve this state, brands must work towards understanding the value matrix that the product offers and tie its output with a measurable value realization for their customers. If the business benefits cannot be measured, customers tend to not invest heavily in the product and remain in the growth segment of the matrix.

At the end of the day, it’s the customer experience that’s at the heart of the product, that makes them realize the product’s true value and impact on their own business.

About the Author –

Ashish Joseph

Ashish Joseph is a Lead Consultant at GAVS working for a healthcare client in the Product Management space. His areas of expertise lie in branding and outbound product management.

He runs two independent series called BizPective & The Inside World, focusing on breaking down contemporary business trends and Growth strategies for independent artists on his website www.ashishjoseph.biz

Outside work, he is very passionate about basketball, music, and food.

AIOps for Service Reliability Engineering (SRE)

Data is the single most accountable yet siloed component within any IT infrastructure. According to a Gartner report, an average enterprise IT infrastructure generates up to 3 times more IT operational data with each passing year. Large businesses find themselves challenged by frequent unplanned downtime of their services, high IT issue resolution times, and consequently poor user experience caused by inefficient management of this data overload, reactive IT operations, and other reasons such as:

  • Traditional legacy systems that do not scale
  • Siloed environments preventing unified visibility into IT landscape
  • Unattended warning signs due to alert fatigue
  • Lack of advanced tools to intelligently identify root causes of cross-tier events
  • Multiple hand-offs that require manual intervention affecting problem remediation workflow

Managing data and automation with AIOps

The surge of AI in IT operations or AIOps is helping bridge the gap between the need for meaningful insights and human intervention, to ensure service reliability and business growth. AIOps is fast becoming a critical need since effective management of the humongous data volumes has surpassed human capabilities. AIOps is powered by AI/ML algorithms that enable automatic discovery of infra & applications, 360o observability into the entire IT environment, noise reduction, anomaly detection, predictive and prescriptive analytics, and automatic incident triage and remediation!

AIOps provides clear insights into application & infrastructure performance and user experience, and alerts IT on potential outages or performance degradation. AIOps delivers a single, intelligent, and automated layer of intelligence across all IT operations, enabling proactive & autonomous IT operations, improved operational efficiencies through reduction of manual effort/fatigue/errors, and improved user experience as predictive & prescriptive analytics drive consistent service levels.

The Need for AIOps for SRE

SRE mandates that the IT team always stays ahead of IT outages and proactively resolves incidents before they impact the user. However, even the most mature teams face challenges due to the rapidly increasing data volumes and expanding IT boundaries, created by modern technologies such as the cloud, and IoT. SRE faces challenges such as lack of visibility and technology fragmentation while executing these tasks in real-time.

SRE teams have started to leverage AI capabilities to detect & analyze patterns in the data, eliminate noise & gain meaningful insights from current & historical data. As AIOps enters the SRE realm, it has enabled accelerated and automated incident management and resolution. With AI at the core, SRE teams can now redirect their time towards strategic initiatives and focus on delivering high value to users.

Transform SRE with AIOps

SREs are moving towards AIOps to achieve these main goals:

  • Improved visibility across the organization’s remote & distributed systems
  • Reduced response time through automation
  • Prevention of incidents through proactive operations

AIOps Platform ZIFTM from GAVS allows enterprises focused on digital transformation to become proactive with IT incidents, by delivering AI-led predictions and auto-remediation. ZIF is a unified platform with centralized NOC powered by AI-led capabilities for automatic environment discovery, going beyond monitoring to observability, predictive & prescriptive analytics, automation & self-remediation enabling outcomes such as:

  • Elimination of digital dirt
  • IT team empowered with end-to-end visibility
  • Breaking away the silos in IT infrastructure systems and operations
  • Intuitive visualization of application health and user experience from the digital delivery chain
  • Increasing precision in intelligent root cause analyses helping drastic cut in resolution time (MTTR)
  • ML algorithms for continuous learning from the environment driving huge improvements with time
  • Zero-touch automation across the spectrum of services, including delivery of cloud-native applications, traditional mainframes, and process workflows

The future of AIOps

Gartner predicts a rapidly growing market size from USD 1.5 billion in 2020. Gartner also claims that the future of IT operations cannot operate without AIOps due to these four main drivers:

  • Redundancy of traditional approaches to handling IT complexities
  • The proliferation of IoT devices, mobile applications & devices, APIs
  • Lack of infrastructure to support IT events that require immediate action
  • Growth of third-party services and cloud infrastructure

AIOps has a strong role in five major areas — anomaly detection, event correlation and advanced data analysis, performance analysis, automation, and IT service management. However, to get the most out of AIOps, it is crucial to choose the right AIOps platform, as selecting the right partner is critical to the success of such an important org initiative. Gartner recommends prioritizing vendors based on their ability to address challenges, data ingestion & analysis, storage & access, and process automation capabilities. We believe ZIF is that AIOps solution for you! For more on ZIF, please visit www.zif.ai.

Customizing OOTB IT Network Security Software Products

Sundaramoorthy S

As global IT is rapidly being digitalized, the network security requirements of major businesses are offered as Out of The Box (OOTB) IT security products by IT OEMs (Information Technology Original Equipment Manufacturers).

The products offered by OEMs adhere to global standards like ISO/IEC 2700, NIST, GDPR, CCPA, and PDPB, which leads to businesses buying licenses for the end products with the intention of saving time and money. However, while integrating, deploying, and maintaining the product solution, the intention of owning the product is violated.  

This article focuses on the customizations of the OOTB products that should be avoided, and steps for tuning the customization of the requirements in the licensed products.

Customization is desirable when it lies within the OOTB product’s radar. Moving beyond the limits leads to multiple operational challenges.

Customizations that are narrower in scope end up being under-utilized. There are certain customizations that can very well be done without. It is ideal to conduct an analysis to validate whether the time and money invested for such customizations will give proportionate benefits/returns.

Product OEMs should be consulted on matters of future releases and implementations before taking such decisions. Choosing the right implementation partner is equally important. Failing to do so may result in issues in production systems, in terms of Audit, Governance, Security, and Operations. Realizing the flaw in later stages costs businesses heavily. Extensive testing must be conducted to ensure the end-to-end capabilities of the OOTB product are not violated.

Listed below are few observations based on my discussions with executives who have faced such issues in ongoing and completed implementations.

Customizations to Avoid

  • OOTB products are customized by overwriting thousands of lines of code. It makes the product tightly coupled to the network and makes the future upgrades and migration of the product complex.
  • Disregarding the recommendations of product architects & SMEs and making customizations to the existing capability of the products to meet the isolated requirements of a business leads to further hidden issues in the products. Finally, what the business demands is to customize, which violates the intent of the OOTB product.
  • Random customizations make the products compatible with the existing enterprise architecture which makes the network vulnerable.
    Below are some challenges:
    • OOTB designed products are unable to consume the business data as it is in some cases
    • Some business users are not willing to migrate to new systems, or unable to educate the users to utilize the new systems.
  • OOTB APIs are not utilized in places where it is required.

Cons of Customizing

  • OEMs provide support for OOTB features only and not for customized ones.
  • The impact of customizations on the product’s performance, optimization, and security is not always clear.
  • Audit and Governance are not manageable if the customizations are not end-to-end.
  • The above issues may lead to a lower return on investment for the customizations

Steps to Avoid Major Customization

For New implementations

  • The Road Map and strategy should be derived by doing a detailed analysis of the current and future state while selecting the product solution.
  • PoCs for requirements of the future state should be done with multiple products which offer similar services in the market to select the right one.
  • Future requirements vs product compliance matrix should be validated.
  • Gap analysis between the current state and future state should be executed through discussions with product owners and key stakeholders in the business.
  • Implementation partners could be engaged in such activities which could refine the analysis and offer their expertise on working with multiple similar products in the market so that the outcome (product selected) is best in terms of cost and techno-functional requirements.

For existing implementations where the product solution is already deployed

  • OOTB product features should be utilized efficiently by vendors, partners, and service providers.
  • To utilize the OOTB product, massaging the existing dataset or minimal restructuring post risk analysis is acceptable. This exercise should be done before onboarding the product solution.
  • For any new requirement which is not OOTB, rather than customizing the product solution independently as an end-user (business entity), a collaborative approach with implementation partners and OEMs’ professional services (minimal) should be taken. This can help address the complexity of requirements without any major roadblocks in the implementation in terms of security and performance of the product solution already deployed in the network. In this approach, support from the product team is available too, which is a great plus.

Role of OEMs

OEMs should take the necessary efforts to understand the needs of the customers and deliver relevant products. This will help in ensuring a positive client experience.

Below are few things the OEMs should consider:

  1. OEMs should have periodic discussions with clients, service providers, and partners, and collect inputs to upgrade their product and remain competitive.
  2. Client-specific local customizations which could be utilized by global clients should be encouraged and implemented.
  3. OEMs should implement the latest technologies and trends in OOTB products sooner than later.
  4. OEMs could use the same technical terminologies across the products which offer similar services, as of now individual products use their own which is not a client and user-friendly.

Since security is the top priority for all, above discussed improvisations, tips and pointers should be followed by all the IT OEMs in the market who produce IT network security products.

Customizations in IT security products are not avoidable. But it should be minimal and configurable based on the business-specific requirements and not major enhancements.

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About the Author –

Sundar has more than 13 years of experience in IT, IT security, IDAM, PAM and MDM project and products. He is interested in developing innovative mobile applications which saves time and money. He is also a travel enthusiast.

Introduction to Shift Left Testing

Abdul Riyaz

Never stop until the very end.

The above statement encapsulates the essence of Shift Left Testing.

Quality Assurance should keep up the momentum of testing during the end-to-end flow. This will ensure Quicker Delivery, Quality Product, and Increased Revenue with higher Profitability. This will help transform the software development process. Let me elucidate how it helps.

Traditional Testing vs Shift Left Testing

For several decades, Software Development followed the Waterfall Model. In this method, each phase depends on the deliverables of the previous phase. But over time, the Agile method provided a much better delivery pattern and reduced the delivery timelines for projects. In this Software Development model, testing is a continuous process that starts at the beginning of a project and reduces the timelines. If we follow the traditional way of testing after development, it eventually results in a longer timeline than we imagined.

Hence, it is important to start the testing process parallel to the development cycle by using techniques such as ‘Business-Driven Development’ to make it more effective and reduce the timeline of delivery. To ensure Shift Left Testing is intact, AUT (Application Under Test) should be tested in an automated way. There are many proven Automation Testing software available in the current world of Information Technology which help better address this purpose.

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AIOps Artificial Intelligence for IT Operations

End-to-End Testing Applied over Shifting Left!

Software Testing can be predominantly classified in 3 categories – Unit, Integration and End-to-End Testing. Not all testing correspondingly shifts left from Unit test to System test. But this approach is revolutionized by Shift Left Testing. Unit Testing is straightforward to test basic units of code, End-to-End Testing is based on the customer / user for the final product. But if we bring the End-to-End testing to the left, that will result in better visibility of the code and its impact on the entire product during the development cycle itself.

The best way we could leverage ML (Machine Learning) and achieve a Shift-Left towards design and development with testing is indicated by continuous testing, visual testing, API coverage, scalable tests and extendable coverage, predictive analytics, and code-less automation.

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First Time Right & Quality on Time Shift Left Testing not only reduces the timeline of deliveries, but it also ensures the last minute defects are ruled out and we get to identify the software flaws and conditions during the development cycle and fix them, which eventually results in “First Time Right”. The chance of leaking a defect is very less and the time spent by development and testing teams towards fixing and retesting the software product is also reduced, thereby increasing the productivity for “Quality on Time” aspects.

I would like to refer to a research finding by the Ponemon Institute. It found that if vulnerabilities are detected in the early development process, they may cost around $80 on average. But the same vulnerabilities may cost around $7,600 to fix if detected after they have moved into production.

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The Shift left approach emphasizes the need for developers to concentrate on quality from the early stages of a software build, rather than waiting for errors and bugs to be found late in the SDLC.

Machine Learning vs AI vs Shift Left Testing There are opportunities to leverage ML methods to optimize continuous integration of an application under test (AUT) which begins almost instantaneously. Making machine learning work is a comparatively smaller feat but feeding the right data and right algorithm into it is a tough task. In our evolving AI world, gathering data from testing is straightforward. Eventually making practical use of all this data within a reasonable time is what remains intangible. A specific instance is the ability to recognize patterns formed within test automation cycles. Why is this important? Well, patterns are present in the way design specifications change and, in the methods, programmers use to implement those specifications. Patterns follow in the results of load testing, performance testing, and functional testing.

ML algorithms are great at pattern recognition. But to make pattern recognition possible, human developers must determine which features in the data might be used to express valuable patterns. Collecting and wrangling the data into a solid form and knowing which of the many ML algorithms to inject data into, is very critical to success.

Many organizations are striving towards inducting shift left in their development process; testing and automation are no longer just QA activities. This certainly indicates that the terms of dedicated developers or testers are fading away. Change is eventually challenging but there are few aspects that every team can work towards to prepare to make this shift very effective. It might include training developers to become responsible for testing, code review quality checks, making testers aware of code, start using the same tools, and always beginning with testability in mind.

Shifting left gives a greater ability to automate testing. Test automation provides some critical benefits;

  • Fewer human errors
  • Improvised test coverage (running multiple tests at same time)
  • Involvement and innovative focus of QA engineers apart from day to day activities
  • Lesser or no production defects.
  • Seamless product development and testing model

Introducing and practicing Shift Left Testing will improve the Efficiency, Effectiveness and the Coverage of testing scope in the software product which helps in delivery and productivity.

References

About the Author –

Riyaz heads the QA Function for all the IP Projects in GAVS. He has vast experience in managing teams across different domains such as Telecom, Banking, Insurance, Retail, Enterprise, Healthcare etc.

Outside of his professional role, Riyaz enjoys playing cricket and is interested in traveling and exploring things. He is passionate about fitness and bodybuilding and is fascinated by technology.

Fireside Chat with Dr. Vinita Chauhan-Ramprasath

Dr. Vinita Chauhan

1. Tell us something about your childhood. What values had been instilled in you that helped you excel later in your life?

I think we all have our modest beginnings; I have certainly had mine. Growing up, we were comfortable but never outrageously wealthy. My parents were extremely hard workers and that is something they both instilled in me and my sister. We had everything we needed, but there weren’t a lot of luxuries and we didn’t miss them. Another thing our parents were very unequivocally insistent about was a good education. My father lost his father at a young age and then proceeded to educate himself and ended up getting his doctorate with a scholarship. My mother came from a family that put education above all else. Hard work and the value of education are two things that were instilled in us early in our lives.

2. What have been some of the biggest challenges in your life and how that has shaped you?

When I moved to the US, I lived on my own for the first time and so many things were new and different. Every immigrant has gone through that phase but for me that was especially hard because I was so sheltered before that. Getting a hang of the education system that was so different was also a task. After working in academic research for a while, there was a point when I realized didn’t want to be in academia. I had enrolled in an MBA program that I really enjoyed. When I went back from my maternity leave, I wasn’t willing to give up on my research position yet. There was one semester when my son was still an infant, I was taking 5 classes, working 30 hours a week in my lab, and teaching 2 online courses. It was a result of pure planning, and a lot of support from my husband; my days planned to the minute. It was a very trying time but was extremely rewarding.

3. How did you discover your passion for STEM?

I always enjoyed Biology. I found it fascinating and I was also fortunate enough to have some great Biology teachers. One of my teachers ended up mentoring me and helped me explore various opportunities. That was a big turning point for me. She tried to nurture my interests and talked to me about my options going forward. Studies have shown that school-going girls, lose interest in STEM at an early age, more so than boys, if not nurtured and supported appropriately. Girls take it harder when they make mistakes, and we need to show them to learn from it and continue moving forward.

4. What were the biggest leadership shifts in the past year?

We have all been trying to do our best juggling work and our family’s health. And we’re all in this together. There have been times my sons walked into the room while I was in meetings and no one batted an eyelid. Leaders understand that we are all managing things at home too and allow us the flexibility to do so. People step up to the challenge they are presented if we give them an opportunity to do so and the pandemic has clearly tested all of us.

5. Could you tell us something about how to manage remote teams?

I personally like to have video calls with my team members and know what is happening in their lives even outside of work. Our physical and mental health and well-being makes everything else possible, being mindful of that is important. It is also important to empower our teams to feel confident enough to come up with the best solutions. It is very fulfilling for me to see my team members come up with better ways of doing things and prove me wrong. A manager’s number one priority is to ensure that everyone is working to the best of their ability.

6. How important do you think is Diversity and Inclusion for corporates?

We are resistant to change but change is the only constant. Look at what the last year has taught us. Diversity, inclusion, and equity are considered buzzwords in corporate world, but they are important in every facet of life. There is a story about 4 people looking at a box as a problem but from different angles. So, it is a different problem for each of them, that results in different solution. Being inclusive fosters creativity and innovation.

Valuing our employees empowers them to be better performers. I have been fortunate to have leaders, both male and female, who have shown faith in me. I am particularly proud of working with Premier. Our leaders ensure that everyone is given a seat at the table and is heard and that makes everyone, in turn, want to do a better job.  

7. How would you describe an ideal technology partner?

The number one thing would be for them to understand our business. They must have the capability and resources to fulfill our business needs. Another important thing is clear communication. However, one thing that the pandemic reinforced was that the highest priority should be the ability to transform. Even if we don’t have an immediate need, we must have the capability to learn and adapt.

8. As someone from the healthcare industry, what message would you like to give to our readers especially about vaccination?

India is at a stage right now where US was sometime ago. We’ve had over a year to prepare for this and yet we aren’t adequately organized. On top of it, there is a debate about the vaccines raging on. The technology that these vaccines are based on has been widely researched. I would request people not to be skeptical of them. It will not make you immune from the infection, but it will ensure that you don’t die from COVID. Complications from COVID can have severe, adverse, long-term effects.

Please wear your masks, social distance if you step out of your homes and make the right decision for yourself and your families and get the vaccine when you are eligible.

About Dr. Chauhan –

Vinita Chauhan-Ramprasath was born and raised in India and spent most of her childhood in Mumbai. She graduated with her B.Sc. in Chemistry and Biochemistry from Mumbai and then received her M.Sc. in Biochemistry. Vinita moved to the United States in August 2000 and received her Doctorate in Diagnostic Medicine and Pathobiology. She got married in 2006 and moved to Charlotte where she worked as a research faculty at University of North Carolina at Charlotte before getting her MBA and joining Premier Inc. Currently Vinita works as a Director of ITS Operations where she manages the GAVS-Premier partnership as well as a part of the integration management office within Premier. Vinita lives in Charlotte, NC with her husband Ram and her two sons Neel and Nikhil and their dog Dakota.

API Security

Logaiswar S

“An unsecured API is literally an ‘all you can eat buffet’ for hackers.”

What is API security?

API security is the protection of network-exposed APIs that an organization, both owns and uses. APIs are becoming the preferred method to develop new-age applications. They are one of most common ways to interact between microservices and containers like systems and apps. API are developed using REST or SOAP methods. However, the true strength of API security depends on how there are implemented.

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REST API Security Vs SOAP API Security

REST APIs use HTTP and Support Transport Layer Security Encryption (TLS). It is a standard that makes the connection private and checks whether the data transferred between the two systems (client and server) is encrypted. REST API is faster than SOAP because of the statelessness of nature. REST API doesn’t need to store or repackage data.

SOAP APIs use built protocols known as Web services. These protocols are defined using a rule set that is guided by confidentiality and authentication. SOAP API has not been around for as long as REST API. SOAP API is more secure than REST API as it uses Web security for transmission long with SSL.

Why is API security important?

Organizations use API to connect services and transferred data. The major data breaches through API are broken, exposed, or hacked APIs. The way API security is used depends on what kind of data is transferred.

Security testing of APIs is currently a challenge for 35% of organizations, that need better capabilities than what current DAST and SAST technologies offer to automatically discover APIs and conduct testing. Organizations are moving from monolithic web applications to modern applications such as those that make heavy use of client-side JavaScript or ones that utilize microservices architecture.

How API Security works?

API security depends on authentication and authorization. Authentication is the first step; it is used to verify that the client application has the required permission to use API. Authorization is the subsequent step that determines what data and action an authentication application can access while interacting with API.

APIs should be developed with protective features to reduce the system’s vulnerability to malicious attacks during API calls.

The developer is responsible for ensuring the developed API successfully validates all the input collected from the user during API calls. The prepared statements with blind variables are one of the most effective ways to prevent API from SQL injection. XSS can be easily handled by cleaning the user input from the API call. Cleaning the inputs helps to ensure that potential XSS vulnerabilities are minimized.   

Best Practice for Secure API

Some basic security practice and well-established security control if the APIs are shared publicly are as follows:

  • Prioritize security: Potential loss for the organization happens using unsecured APIs, so make security a priority and build the API securely as they are being developed.
  • Encrypt traffic using TLS: Some organizations may choose not to encrypt API payload data that is considered to be non-sensitive, but for organizations whose API exchange sensitive data, TLS encryption should be essential.
  • Validate input: Never pass input from an API through to the endpoint without validating it first.
  • Use a WAP: Ensure that it can understand API payloads.
  • Use token: Establish trusted identities and then control access to services and resources by using tokens.
  • Use an API gateway: API gateways act as the major point of enforcement for API traffic. A good gateway will allow you to authenticate traffic as well as control and analyze how your APIs are used.

Modern API Data breach

USPS Cooperate Database Exposure

The weakness allowed an attacker to query the USPS website and scrape a database of over 60 million cooperate users, email addresses, phone numbers, account numbers, etc.

Exploitation

The issue was authentication-related which allowed unauthorized access to an API service called ‘informed visibility’, which was designed to deliver real-time tracking data for large-scale shipping operations.

This tracking system was tied into web API in a way that users could change the search parameters and view and even in some cases modify the information of other users. Since there wasn’t a robust anti-scraping system in place, this mass exposure was compounded by the automated and unfettered access available.

Lessons Learned

Providers giving extreme power to a specific service or function without securing every permutation of its interaction flow can lead to such exploits. To mitigate API-related risks, coding should be done with the assumption that the APIs might be abused by both internal and external forces.

References:

  1. https://www.redhat.com/en/topics/security/api-security
  2. https://searchapparchitecture.techtarget.com/definition/API-security
  3. https://nordicapis.com/5-major-modern-api-data-breaches-and-what-we-can-learn-from-them/

About the Author –

Logaiswar is a security enthusiast with core interest in Application & cloud security. He is part of the SOC DevSecOps vertical at GAVS supporting critical customer engagements.

#EmpathyChallenge – 3 Simple Ways to Practice Empathy Consciously

Padma Ravichandran

A pertinent question for the post COVID workforce is, can empathy be learnt? Should it be practiced only by the leaders, or by everyone – can it be seamlessly woven into the fabric of the organization? We are seeing that dynamics at play for remote teams is little unpredictable, making each day uniquely challenging. Empathy is manifested through mindful behaviours, where one’s action is recognized as genuine, personal, and specific to the situation. A few people can be empathetic all the time, a few, practice it consciously, and a few are unaware of it.

Empathy is a natural human response that can be practiced by everyone at work for nurturing an environment of trust. We often confuse empathy for sympathy – while sympathy is feeling sorry for one’s situation, empathy is understanding one’s feelings and needs, and putting the effort to offer authentic support. It requires a shift in perspective, and building trust, respect, and compassion at a deeper level. As Satya Nadella, CEO, Microsoft says, “Empathy is a muscle that needs to be exercised.”

Here are three ways to consciously practice empathy at work –

  • Going beyond yourself

It takes a lot to forget how we feel that day, or what is priority for us. However, to be empathetic, one needs to be less judgemental. When one is consciously practicing empathy, one needs to be patient with yourself, your thoughts, and not compare yourself with the person you are empathizing with. If we get absorbed by our own needs, it gets difficult to be generous and compassionate. We need to remember empathy leads to influence and respect, and for that we should not get blind sighted by our perceptions.

  • Being a mindful and intentional listener

While practicing empathy, one has refrain from criticism, and be mindful of not talking about one’s problems. We may get sympathetic and give unsolicited advice. Sometimes it only takes to be an intentional listener, by avoiding distractions, and having a very positive body language, and demeanour. This will enable us to ask right questions and collaborate towards a solution.

  • Investing in the person

Very often, we support our colleagues and co-workers by responding to their email requests. However, by building positive workplace relationships, and knowing the person beyond his/her email id, makes it much easier to foster empathy. Compassion needs to be not just in words, but in action too, and that can happen only by knowing the person. Taking interest in a co-worker or a team member, beyond a professional capability, does not come out of thin air. It takes conscious continuous efforts to get to know the person, showing care and concern, which will help us to relate to the myriad challenges they go through – be it chronic illness, child care that correlates to his/her ability to engaged at work. It will enable us to personalize the experience, and see the person’s point of view, holistically.

When we take that genuine interest in how we make others feel and experience, we start mindfully practicing empathy. Empathy fosters respect. Empathy helps resolves conflicts better, empathy builds stronger teams, empathy inspires one another to work towards collective goals, and empathy breaks authority. Does it take that extra bit of time to consciously practice it? Yes, but it is all worth it.

References

About the Author –

Padma is intrigued by Organization Culture and Behavior at workplace that impact employee experience. She is also passionate about driving meaningful initiatives for enabling women to Lean In, along with her fellow Sheroes. She enjoys reading books, journaling, yoga and learning more about life through the eyes of her 8-year-old son.