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    1Feb, 2023
    Types Of Software Testing: Different Testing Types With Details

    Are You Ready to Explore the Different Types of Software Testing?

    We, as testers, are aware of the various types of Software Testing like Functional Testing, Non-Functional Testing, Automation Testing, Agile Testing, and their sub-types, etc.

    Each of us would have come across several types of testing on our testing journey. We might have heard some and we might have worked on some, but not everyone has knowledge about all the testing types.

    Each type of testing has its own features, advantages, and disadvantages as well. However, in this tutorial, we have covered mostly each and every type of software testing which we usually use in our day-to-day testing life.

    Different Types Of Software Testing

    Functional Testing

    There are four main types of functional testing.

    #1) Unit Testing

    Unit testing is a type of software testing which is done on an individual unit or component to test its corrections. Typically, Unit testing is done by the developer at the application development phase. Each unit in unit testing can be viewed as a method, function, procedure, or object. Developers often use test automation tools such as NUnit, Xunit, JUnit for the test execution.

    Unit testing is important because we can find more defects at the unit test level.

    For example, there is a simple calculator application. The developer can write the unit test to check if the user can enter two numbers and get the correct sum for addition functionality.

    a) White Box Testing

    White box testing is a test technique in which the internal structure or code of an application is visible and accessible to the tester. In this technique, it is easy to find loopholes in the design of an application or fault in business logic. Statement coverage and decision coverage/branch coverage are examples of white box test techniques.

    b) Gorilla Testing

    Gorilla testing is a test technique in which the tester and/or developer test the module of the application thoroughly in all aspects. Gorilla testing is done to check how robust your application is.

    For example, the tester is testing the pet insurance company’s website, which provides the service of buying an insurance policy, tag for the pet, Lifetime membership. The tester can focus on any one module, let’s say, the insurance policy module, and test it thoroughly with positive and negative test scenarios.

    #2) Integration Testing

    Integration testing is a type of software testing where two or more modules of an application are logically grouped together and tested as a whole. The focus of this type of testing is to find the defect on interface, communication, and data flow among modules. Top-down or Bottom-up approach is used while integrating modules into the whole system.

    This type of testing is done on integrating modules of a system or between systems. For example, a user is buying a flight ticket from any airline website. Users can see flight details and payment information while buying a ticket, but flight details and payment processing are two different systems. Integration testing should be done while integrating of airline website and payment processing system.

    a) Gray box testing

    As the name suggests, gray box testing is a combination of white-box testing and black-box testing. Testers have partial knowledge of the internal structure or code of an application.

    #3) System Testing

    System testing is types of testing where tester evaluates the whole system against the specified requirements.

    a) End to End Testing

    It involves testing a complete application environment in a situation that mimics real-world use, such as interacting with a database, using network communications, or interacting with other hardware, applications, or systems if appropriate.

    For example, a tester is testing a pet insurance website. End to End testing involves testing of buying an insurance policy, LPM, tag, adding another pet, updating credit card information on users’ accounts, updating user address information, receiving order confirmation emails and policy documents.

    b) Black Box Testing

    Blackbox testing is a software testing technique in which testing is performed without knowing the internal structure, design, or code of a system under test. Testers should focus only on the input and output of test objects.

    Detailed information about the advantages, disadvantages, and types of Black Box testing can be found here.

    c) Smoke Testing

    Smoke testing is performed to verify that basic and critical functionality of the system under test is working fine at a very high level.

    Whenever a new build is provided by the development team, then the Software Testing team validates the build and ensures that no major issue exists. The testing team will ensure that the build is stable, and a detailed level of testing will be carried out further.

    For example, tester is testing pet insurance website. Buying an insurance policy, adding another pet, providing quotes are all basic and critical functionality of the application. Smoke testing for this website verifies that all these functionalities are working fine before doing any in-depth testing.

    d) Sanity Testing

    Sanity testing is performed on a system to verify that newly added functionality or bug fixes are working fine. Sanity testing is done on stable build. It is a subset of the regression test.

    For example, a tester is testing a pet insurance website. There is a change in the discount for buying a policy for second pet. Then sanity testing is only performed on buying insurance policy module.

    e) Happy path Testing

    The objective of Happy Path Testing is to test an application successfully on a positive flow. It does not look for negative or error conditions. The focus is only on valid and positive inputs through which the application generates the expected output.

    f) Monkey Testing

    Monkey Testing is carried out by a tester, assuming that if the monkey uses the application, then how random input and values will be entered by the Monkey without any knowledge or understanding of the application.

    The objective of Monkey Testing is to check if an application or system gets crashed by providing random input values/data. Monkey Testing is performed randomly, no test cases are scripted, and it is not necessary to be aware
    of the full functionality of the system.

    #4) Acceptance Testing

    Acceptance testing is a type of testing where client/business/customer test the software with real time business scenarios.

    The client accepts the software only when all the features and functionalities work as expected. This is the last phase of testing, after which the software goes into production. This is also called User Acceptance Testing (UAT).

    a) Alpha Testing

    Alpha testing is a type of acceptance testing performed by the team in an organization to find as many defects as possible before releasing software to customers.

    For example, the pet insurance website is under UAT. UAT team will run real-time scenarios like buying an insurance policy, buying annual membership, changing the address, ownership transfer of the pet in a same way the user uses the real website. The team can use test credit card information to process payment-related scenarios.

    b) Beta Testing

    Beta Testing is a type of software testing which is carried out by the clients/customers. It is performed in the Real Environment before releasing the product to the market for the actual end-users.

    Beta Testing is carried out to ensure that there are no major failures in the software or product, and it satisfies the business requirements from an end-user perspective. Beta Testing is successful when the customer accepts the software.

    Usually, this testing is typically done by the end-users. This is the final testing done before releasing the application for commercial purposes. Usually, the Beta version of the software or product released is limited to a certain number of users in a specific area.

    So, the end-user uses the software and shares the feedback with the company. The company then takes necessary action before releasing the software worldwide.

    c) Operational acceptance testing (OAT)

    Operational acceptance testing of the system is performed by operations or system administration staff in the production environment. The purpose of operational acceptance testing is to make sure that the system administrators can keep the system working properly for the users in a real-time environment.

    The focus of the OAT is on the following points:

    • Testing of backup and restore.
    • Installing, uninstalling, upgrading software.
    • The recovery process in case of natural disaster.
    • User management.
    • Maintenance of the software.

    Non-Functional Testing

    There are four main types of functional testing.

    #1) Security Testing

    It is a type of testing performed by a special team. Any hacking method can penetrate the system.

    Security Testing is done to check how the software, application, or website is secure from internal and/or external threats. This testing includes how much software is secure from malicious programs, viruses and how secure & strong the authorization and authentication processes are.

    It also checks how software behaves for any hacker’s attack & malicious programs and how software is maintained for data security after such a hacker attack.

    a) Penetration Testing

    Penetration Testing or Pen testing is the type of security testing performed as an authorized cyberattack on the system to find out the weak points of the system in terms of security.

    Pen testing is performed by outside contractors, generally known as ethical hackers. That is why it is also known as ethical hacking. Contractors perform different operations like SQL injection, URL manipulation, Privilege Elevation, session expiry, and provide reports to the organization.

    Notes: Do not perform the Pen testing on your laptop/computer. Always take written permission to do pen tests.

    #2) Performance Testing

    Performance testing is testing of an application’s stability and response time by applying load.

    The word stability means the ability of the application to withstand in the presence of load. Response time is how quickly an application is available to users. Performance testing is done with the help of tools. Loader.IO, JMeter, LoadRunner, etc. are good tools available in the market.

    a) Load testing

    Load testing is testing of an application’s stability and response time by applying load, which is equal to or less than the designed number of users for an application.

    For example, your application handles 100 users at a time with a response time of 3 seconds, then load testing can be done by applying a load of the maximum of 100 or less than 100 users. The goal is to verify that the application is responding within 3 seconds for all the users.

    b) Stress Testing

    Stress testing is testing an application’s stability and response time by applying load, which is more than the designed number of users for an application.

    For example, your application handles 1000 users at a time with a response time of 4 seconds, then stress testing can be done by applying a load of more than 1000 users. Test the application with 1100,1200,1300 users and notice the response time. The goal is to verify the stability of an application under stress.

    c) Scalability Testing

    Scalability testing is testing an application’s stability and response time by applying load, which is more than the designed number of users for an application.

    For example, your application handles 1000 users at a time with a response time of 2 seconds, then scalability testing can be done by applying a load of more than 1000 users and gradually increasing the number of users to find out where exactly my application is crashing.

    Let’s say my application is giving response time as follows:

    •  1000 users -2 sec
    •  1400 users -2 sec
    •  4000 users -3 sec
    •  5000 users -45 sec
    •  5150 users- crash – This is the point that needs to identify in scalability testing

    d) Volume testing (flood testing)

    Volume testing is testing an application’s stability and response time by transferring a large volume of data to the database. Basically, it tests the capacity of the database to handle the data.

    e) Endurance Testing (Soak Testing)

    Endurance testing is testing an application’s stability and response time by applying load continuously for a longer period to verify that the application is working fine.

    For example, car companies soak testing to verify that users can drive cars continuously for hours without any problem.

    #3) Usability Testing

    Usability testing is testing an application from the user’s perspective to check the look and feel and user-friendliness.

    For example, there is a mobile app for stock trading, and a tester is performing usability testing. Testers can check the scenario like if the mobile app is easy to operate with one hand or not, scroll bar should be vertical, background color of the app should be black and price of and stock is displayed in red or green color.

    The main idea of usability testing of this kind of app is that as soon as the user opens the app, the user should get a glance at the market.

    a) Exploratory testing

    Exploratory Testing is informal testing performed by the testing team. The objective of this testing is to explore the application and look for defects that exist in the application. Testers use the knowledge of the business domain to test the application. Test charters are used to guide the exploratory testing.

    b) Cross browser testing

    Cross browser testing is testing an application on different browsers, operating systems, mobile devices to see look and feel and performance.

    Why do we need cross-browser testing? The answer is different users use different operating systems, different browsers, and different mobile devices. The goal of the company is to get a good user experience regardless of those devices.

    Browser stack provides all the versions of all the browsers and all mobile devices to test the application. For learning purposes, it is good to take the free trial given by browser stack for a few days.

    c) Accessibility Testing

    The aim of Accessibility Testing is to determine whether the software or application is accessible for disabled people or not.

    Here, disability means deafness, color blindness, mentally disabled, blind, old age, and other disabled groups. Various checks are performed, such as font size for visually disabled, color and contrast for color blindness, etc.

    #4) Compatibility testing

    This is a testing type in which it validates how software behaves and runs in a different environment, web servers, hardware, and network environment.

    Compatibility testing ensures that software can run on different configuration, different databases, different browsers, and their versions. The testing team performs compatibility testing.

    Other Types of Testing

    Ad-hoc Testing

    The name itself suggests that this testing is performed on an ad-hoc basis, i.e., with no reference to the test case and also without any plan or documentation in place for this type of testing.

    The objective of this testing is to find the defects and break the application by executing any flow of the application or any random functionality.

    Ad-hoc testing is an informal way of finding defects and can be performed by anyone in the project. It is difficult to identify defects without a test case, but sometimes it is possible that defects found during ad-hoc testing might not have been identified using the existing test cases.

    Back-end Testing

    Whenever an input or data is entered on the front-end application, it is stored in the database and the testing of such database is known as Database Testing or Backend Testing.

    There are different databases like SQL Server, MySQL, Oracle, etc. Database Testing involves testing of table structure, schema, stored procedure, data structure, and so on. In Back-end Testing, GUI is not involved, the testers are directly connected to the database with proper access and testers can easily verify data by running a few queries on the database.

    There can be issues identified like data loss, deadlock, data corruption, etc during this back-end testing and these issues are critical to fixing before the system goes live into the production environment.

    Browser Compatibility Testing

    This is a sub-type of Compatibility Testing (which is explained below) and is performed by the testing team.

    Browser Compatibility Testing is performed for web applications and ensures that the software can run with a combination of different browsers and operating systems. This type of testing also validates whether a web application runs on all versions of all browsers or not.

    Backward Compatibility Testing

    It is a type of testing that validates whether the newly developed software or updated software works well with the older version of the environment or not.

    Backward Compatibility Testing checks whether the new version of the software works properly with the file format created by an older version of the software. It also works well with data tables, data files, and data structures created by the older version of that software. If any of the software is updated, then it should work well on top of the previous version of that software.

    Black Box Testing

    Internal system design is not considered in this type of testing. Tests are based on the requirements and functionality.

    Detailed information about the advantages, disadvantages, and types of Black Box testing can be found here.

    Boundary Value Testing

    This type of testing checks the behavior of the application at the boundary level.

    Boundary Value Testing is performed to check if defects exist at boundary values. Boundary Value Testing is used for testing a different range of numbers. There is an upper and lower boundary for each range and testing is performed on these boundary values.

    If testing requires a test range of numbers from 1 to 500, then Boundary Value Testing is performed on values at 0, 1, 2, 499, 500, and 501.

    Branch Testing

    This is also known as Branch coverage or decision coverage testing. It is a type of white box testing performed at the unit test level. It is done to make sure that each possible path from the decision point is executed at least once for 100% of test coverage.

    Example:

    Read number A, B
    If (A>B) then
    Print(“A is greater”)
    Else
    Print(“B is greater”)

    Here, there are two branches, one for if and the other for else. For 100% coverage, we need 2 test cases with different values of A and B.

    Test case 1: A=10, B=5 It will cover the if branch.

    Test case 2: A=7, B=15 It will cover the else branch.

    Comparison Testing

    Comparison of a product’s strengths and weaknesses with its previous versions or other similar products is termed Comparison Testing.

    Equivalence Partitioning

    It is a testing technique and a type of Black Box Testing. During this Equivalence Partitioning, a set of groups are selected and a few values or numbers are picked up for testing. It is understood that all values from that group generate the same output.

    The aim of this testing is to remove redundant test cases within a specific group that generate the same output but not any defect.

    Suppose the application accepts values between -10 and +10, then using equivalence partitioning, the values picked for testing are zero, one positive value, and one negative value. So the Equivalence Partitioning for this testing is  -10 to -1, 0, and 1 to 10.

    Example Testing

    Example testing is real-time testing. It includes real-time scenarios and scenarios are based on the experience of the testers.

    This type of testing is also known as experience-based testing because it uses the tester’s knowledge of how the application has worked in the past, how to break the application, what kind of errors are common in this type of application.

    Graphical User Interface (GUI) Testing

    The objective of this GUI Testing is to validate the GUI as per the business requirement. The expected GUI of the application is mentioned in the Detailed Design Document and GUI mockup screens.

    GUI Testing includes the size of the buttons and input fields present on the screen, alignment of all text, tables, and content in the tables.

    It also validates the menu of the application. After selecting different menu and menu items, it validates that the page does not fluctuate, and the alignment remains the same after hovering the mouse on the menu or sub-menu.

    Incremental Integration Testing

    Incremental Integration Testing is a Bottom-up approach for testing, i.e continuous testing of an application when new functionality is added.

    Application functionality and modules should be independent enough to test separately. This is done by programmers or by testers.

    Install/Uninstall Testing

    Installation testing is performed to check that the software application is installed properly and working as per expectation. Installation testing is a phase of testing before users interact with the actual application for the first time. Installation testing is also called “Implementation Testing”.

    Uninstallation Testing is performed to confirm if all the components or elements of the software are removed from the system or not.

    Installation and Uninstallation Testing is done on full, partial, or upgraded install/uninstall processes on different operating systems under different hardware or software environments.

    Mutation Testing

    Mutation Testing is a type of white box testing in which the source code of one program is changed and verifies whether the existing test cases can identify these defects in the system.

    The change in the program source code is very minimal, so it does not impact the entire application, only the specific area having the impact and the related test cases should be able to identify those errors in the system.

    Negative Testing

    The mindset of the tester is to “Break the System/Application” and it is achieved through Negative Testing.

    Negative Testing technique is performed using incorrect data, invalid data, or input. It validates if the system throws an error of invalid input and behaves as expected.

    It should not take much time to load any page or system and should be sustained during peak load. Different performance and load tools are used to do this testing.

    Recovery Testing

    It is a type of testing that validates how well the application or system recovers from crashes or disasters.

    Recovery Testing determines if the system can continue its operation after a disaster. Assume that the application is receiving data through a network cable and suddenly that network cable has been unplugged.

    Sometime later, plug in the network cable; then the system should start receiving data from where it lost the connection due to the network cable being unplugged.

    Regression Testing

    Regression testing is testing of unchanged features of the application to make sure that any bug fixes, adding new features, deleting, or updating existing features, are not impacting the working application.

    To find out regression scope is an important part in  Regression Testing. To find out regression scope, Tester needs to find out the area of application where changes happened and the Impact of those changes on the entire application. It is difficult to cover the whole regression test suite in every release, so Automation Testing Tools are used in regression testing.

    Risk-Based Testing (RBT)

    For Risk-Based Testing, the functionalities or requirements are tested based on their priority. Risk-Based Testing includes testing of highly critical functionality, which has the highest impact on business and in which the probability of failure is very high.

    Priority decisions are based on business needs, so once priority is set for all functionalities, then high priority functionality or test cases are executed first, followed by medium and then low priority functionalities.

    Low priority functionality may be tested or not tested based on the available time. Risk-Based Testing is carried out if there is insufficient time available to test the entire software and the software needs to be implemented on time without any delay.

    This approach is followed only by the discussion and approval of the client and senior management of the organization.

    Static Testing

    Static Testing is a type of testing which is done without the execution of any code. Reviews, walkthroughs, and inspections are different methods of performing static testing. Activities like reviewing of requirement documents, customer requirement specification, high level, and low-level design, code syntax, naming standards, etc. come under static testing.

    Static Testing also applies to test cases, test plans, test scenarios. Static testing is done to prevent the defect rather than catching the defect at a later stage. That is why static testing is cost-effective.

    For example, Tester is testing a pet insurance website. The logic for premium calculation is described in requirement documentation. As a part of static testing, testers can review the developer code for premium calculation and compare it with the requirement document to prevent the defect related to premium calculation.

    Vulnerability Testing

    The testing, which involves identifying weaknesses in the software, hardware, and network, is known as Vulnerability Testing. In malicious programs, the hacker can take control of the system, if it is vulnerable to such kinds of attacks, viruses, and worms.

    We need to check if those systems undergo Vulnerability Testing before production. It may identify critical defects and flaws in security.

    Resource : https://www.softwaretestinghelp.com/types-of-software-testing/

    1Feb, 2023
    The Different Types of Cybersecurity

    Cyber security is a wide field covering several disciplines. It can be divided into seven main pillars:

    1. Network Security

    Most attacks occur over the network, and network security solutions are designed to identify and block these attacks. These solutions include data and access controls such as Data Loss Prevention (DLP), IAM (Identity Access Management), NAC (Network Access Control), and NGFW (Next-Generation Firewall) application controls to enforce safe web use policies.

    Advanced and multi-layered network threat prevention technologies include IPS (Intrusion Prevention System), NGAV (Next-Gen Antivirus), Sandboxing, and CDR (Content Disarm and Reconstruction). Also important are network analytics, threat hunting, and automated SOAR (Security Orchestration and Response) technologies.

    2. Cloud Security

    As organizations increasingly adopt cloud computing, securing the cloud becomes a major priority. A cloud security strategy includes cyber security solutions, controls, policies, and services that help to protect an organization’s entire cloud deployment (applications, data, infrastructure, etc.) against attack.

    While many cloud providers offer security solutions, these are often inadequate to the task of achieving enterprise-grade security in the cloud. Supplementary third-party solutions are necessary to protect against data breaches and targeted attacks in cloud environments.

    3. Endpoint Security

    The zero-trust security model prescribes creating micro-segments around data wherever it may be. One way to do that with a mobile workforce is using endpoint security. With endpoint security, companies can secure end-user devices such as desktops and laptops with data and network security controls, advanced threat prevention such as anti-phishing and anti-ransomware, and technologies that provide forensics such as endpoint detection and response (EDR) solutions.

    4. Mobile Security

    Often overlooked, mobile devices such as tablets and smartphones have access to corporate data, exposing businesses to threats from malicious apps, zero-day, phishing, and IM (Instant Messaging) attacks. Mobile security prevents these attacks and secures the operating systems and devices from rooting and jailbreaking. When included with an MDM (Mobile Device Management) solution, this enables enterprises to ensure only compliant mobile devices have access to corporate assets.

    5. IoT Security

    While using Internet of Things (IoT) devices certainly delivers productivity benefits, it also exposes organizations to new cyber threats. Threat actors seek out vulnerable devices inadvertently connected to the Internet for nefarious uses such as a pathway into a corporate network or for another bot in a global bot network.

    IoT security protects these devices with discovery and classification of the connected devices, auto-segmentation to control network activities, and using IPS as a virtual patch to prevent exploits against vulnerable IoT devices. In some cases, the firmware of the device can also be augmented with small agents to prevent exploits and runtime attacks.

    6. Application Security

    Web applications, like anything else directly connected to the Internet, are targets for threat actors. Since 2007, OWASP has tracked the top 10 threats to critical web application security flaws such as injection, broken authentication, misconfiguration, and cross-site scripting to name a few.

    With application security, the OWASP Top 10 attacks can be stopped. Application security also prevents bot attacks and stops any malicious interaction with applications and APIs. With continuous learning, apps will remain protected even as DevOps releases new content.

    7. Zero Trust

    The traditional security model is perimeter-focused, building walls around an organization’s valuable assets like a castle. However, this approach has several issues, such as the potential for insider threats and the rapid dissolution of the network perimeter.

    As corporate assets move off-premises as part of cloud adoption and remote work, a new approach to security is needed. Zero trust takes a more granular approach to security, protecting individual resources through a combination of micro-segmentation, monitoring, and enforcement of role-based access controls.

    Resource : https://www.checkpoint.com/cyber-hub/cyber-security/what-is-cybersecurity/

    31Jan, 2023
    Artificial Intelligence May Actually Help Humanize Financial Services

    The financial services sector has long been criticized as being insulated, elitist, and discriminatory. Will artificial intelligence finally open up and “democratize” this industry? It’s likely, and will happen a number of ways – by empowering customers, by opening up services to underserved communities, and by increasing the breadth of capabilities companies can offer.

    While still in the minority, a growing number of financial services executives are bringing in AI as a part of their customer experiences and operations. About half of 500 executives (48%) responding to a survey conducted by Economist Impact and SAS in March 2022 identified advanced data analytics as among the most important technologies to harness, and 34% specifically cited AI and machine learning as their paths to the future.

    Similarly, a study from Deloitte AI Institute confirms that 32% of financial services executives indicate their organizations use AI. “It’s undeniable that AI is the future of financial services,” the study’s authors state, adding that while “many FinTechs have embraced AI, the financial services industry is largely in the early stages of AI adoption.”

    AI and machine learning introduce great complexities, and many financial services companies are still assessing where and how to invest in these approaches. “There are a lot of moving parts with AI and machine learning,” says Michael Upton, chief digital officer at First Tech Federal Credit Union, which exclusively services Microsoft, Amazon, Intel, Hewlett-Packard, and employees of other technology firms. Once put in place, however, these technologies have a critical role to play in the emerging digital enterprise. “Covid really accelerated digital, and the industry did a good job in customers’ needs from a tactical and transactional perspective. But I think industrywide, we lacked some engagement, we lacked some warmth, we lacked some relevance, particularly through digital channel. We need to re-instate humanization into digital, and AI is a tool that’s going to help us get there. Combined with in-person contact, AI can help deliver more personalized, more relevant, services in tune with what customers need at a particular moment.”

    First Tech Federal sees greatly personalized interactions and services to customers as the main goal of its own expanding AI efforts. “Using AI and ML, we believe we will put ourselves in the best position to help each individual member meet their needs at any point in time,” says Upton. “We’re looking to be relevant when the member needs us to be relevant, no matter which touchpoint they so choose. We’re looking to leverage this into personalization and relevant engagement, whether it be a sales engagement, a servicing engagement, or a retention engagement.”

    While AI promises to open up the levels of services financial institutions can deliver, there are challenges that need to be overcome, including skewed expectations, skills issues, and implementation issues. “Talent scarcity is a key gating factor,’ says Bjorn Austraat, senior vice president and head of AI acceleration at Truist. “This is true for specialized data science resources but – importantly – also for all-important resources and leaders that can speak to both technical and business stakeholders,’ he explains. This includes those “that are fluent in data science and executive-speak. An over-reliance on purely technical skills can lead to disjointed science experiments without a clear business return and an excessive focus on business outcomes — especially early on in sometimes lengthy data science and model ops lifecycles — can squelch disruptive innovation.”

    Barriers to achieving success with AI are common across all sectors, says Charlene Coleman, senior managing partner and head of the modern finance sector of Launch Consulting Group. But financial services brings in its own sets of issues. “Deploying AI to democratize the financial system requires bold, human-centered leadership willing to invest in technology and talent. Next, institutions lacking an AI strategy will not move beyond the experimental phase. Most do not have a centralized data backbone that supports analysis and intelligent recommendations. Finally, they must adopt a new operating model that moves away from functional silos to enable speed and agility.”

    Artificial intelligence “can help redefine and restore personalized experiences that build trust for consumers and small business owners,” says Coleman. “Assuming informed consent, an example is AI-powered personalized conversational interfaces and biometric profiles that have shown promise in helping vulnerable consumers avoid debt traps fueled by late fees and inflexible payment schedules.”

    This means more than building models to support algorithms, no matter how well designed. “People often assume ‘we just need a great model to solve our problem,’” says Austraat. “However, the model is only five percent of the solution. The integration, instrumentation, validation, ongoing monitoring and ultimately dollarization are the other 95%.” The key is to “think of the model as the race car engine,” he adds. “You’ll need a lot of other things to win the race: gas, shocks, tires, a pit crew, and a driver.”

    The key to AI success in financial services is to sell or promote AI adoption to the business. “I use a simple phrase to accelerate this alignment: ‘Whose life is going to get better, by how much and how do we know that?’” says Austraat. “If you can truly answer that question, you have covered all the bases from framing, to deployment, to value proposition and value perception and realization, to political air cover. Explainability trumps model performance in financial services. In particularly sensitive areas such as credit underwriting, banks and other institutions must balance the desire to innovate and use cutting-edge AI with the reasonable regulatory expectations around explainability, robustness and fairness. The hottest solution doesn’t always win, especially if it’s too much of a black box.”

    This requires a much more holistic view of AI, beyond the lab or data science team. “You can’t just let the data scientists do their thing,” Austraat says. “A holistic teaming approach centered around cross-functional pods is critical to engage legal, risk, data engineering, implementation engineering, operations, support and business leaders early and often to create sustainable success.”

    In the end, technologies such as AI and ML “are simply tools,” says Upton. ‘You need to have a very clear business strategy, a very good go-to-market strategy, and a very good operational plan to leverage those tools to create the experiences and drive business value. People tend to get enamored with the tool or the tech, but they’re not really clear on their use case for the value of the investment. You can buy all the coolest tools in the world, but if you don’t account for the change management, the adoption, the helping the organization leading into the why and how to use these tools to drive out the things that matter, you’re just going to have a lot of expensive tools by themselves.”

    Resource : https://www.forbes.com/sites/joemckendrick/2023/01/28/artificial-intelligence-may-actually-help-humanize-financial-services/?sh=4d0cc4c1d9f5

    30Jan, 2023
    Top 5 technology trends to watch out in 2023

    With technology being a great leveler, the world has become flat and offers equal opportunities to all. Whether you are someone born in the Bay area or a remote village in Africa, your opportunity to create an impact and to make a difference in the world is the same. 

    In India, for instance, the government is adopting technology readily with initiatives like JAM (Jan Dhan-Adhaar-Mobile) trinity and offering a level playing field to all its citizens. Close to 47% of India’s GDP gets transacted on this platform and my recent interaction with a local tea seller showcases how this tech adoption is making a difference at the grassroots.  

    I curiously asked this young man from Kerala who operates his tea shop single-handedly, the reason to adopt UPI payments. He explained how it saves his time in giving change to customers after each transaction and that now he doesn’t need to go to the bank to deposit cash.

    On the other hand, the bank to which his UPI transactions are linked now knows that this young man is generating INR 30,000/- per working day. Therefore, their risk profiling AI software is now able to analyze this data and recommend him a loan for his business expansion with better certainty of loan repayment. This was not possible 3 years ago. This is the beauty of what is happening around us!

    In the past few years, India has undergone significant economic expansion, with the adoption of new technology such as AI, Robotics, IoT, Blockchain, Cybersecurity, and Big data. In this article, I would like to discuss few technology trends that are gaining significant mindshare and will continue to do so in 2023.

    Web 3.0: Web 1.0 was “Read-Only”, Web 2.0 is “Read-Write”, and you can imagine Web 3.0 as “Read-Write and Execute”.  This is the next level of internet where the internet will be more interactive and immersive and will be led by user generated content and interaction. For example, to imagine next level of AI in Web 3.0 – AI would be used for natural language processing, to recognize your emotions and facial expressions to render relevant content. This means, when you frown while watching an Instagram reel, it automatically moves to the next genre when you swipe up. This space would therefore be great for enterprises to present relevant content to their users. 

    The way we consume gaming and financial instruments, “DeFi” (Decentralized Finance) will also change big time. Several Web 3.0 apps are gaining popularity and are worth mentioning. For example, “Brave” browser that allows one to block all ads and evade trackers while rewarding good content through “BAT” (Basic Attention Token), it could change the way people look at digital ads. Also, the widespread use of “NFT” (Non-Fungible Token) will help the content creators’ music, video and photo content get better recognition and revenue for their work by ensuring traceable ownership and minimizing the middlemen in between.

    Cybersecurity: The travel and tourism industry is embracing emerging technologies to redefine products, services, and consumer experiences, but doing so makes their cyber ecosystems more exposed to security risks as these technologies handle massive volume of financial transactions and priceless customer data. Travel firms are, therefore, a prime target for cyber-crime. Ransomware and phishing attacks are two of the major concerns in this sector. Due to the current dependence on data, organizations are also at risk from human mistakes brought on by their own staff. To keep the data secure, organizations must invest in cybersecurity as well as a variety of hardware and software solutions. It is evident that a step change is needed, and a more comprehensive, business-driven, and risk-based approach to building cybersecurity capability is going to be the norm in the coming years.

    Metaverse: Metaverse travel has the potential to dramatically revolutionize how people engage with the travel industry, imagine digitally touring a hotel from the comfort of your home on the other side of the world. By creating three-dimensional replicas of real locations, metaverse travel will create new experiences and new possibilities for people to choose hotels and activities. In 2023, metaverse will emerge as a grand concept, that will be based on a single interconnected ecosystem, like the internet. Soon, autonomous metaverse platforms with different economies and the capacity to transport assets and things between worlds may become possible. There has been a lot of conversations around the governance, so things are going to get tricky, as there would be no single controller. There will be a shift in viewpoint, and it will inevitably give rise to new travel patterns and an increasing demand for virtual reality and metaverse travel services. As several companies continue to compete for this space, the future is surely going to look very different, especially with the possibility of creating multiple identities in the metaverse.

    Cloud:  In recent years, the travel sector has seen an increasing interest in cloud computing investments. However, there are still several issues that are delaying the rate of cloud adoption. But as client expectations rise and the travel sector’s technological environment becomes more dynamic, a lot of travel organizations have come to understand the benefits of cloud computing. In the travel tech industry, we deal with a lot of personal data. Through growing computational power and capacity of the cloud, data storage becomes simpler thereby enabling us to provide personalized travel experiences.

    Wearables and IoT: The Internet of Things (IoT) is one of the most significant and potent emerging technology topics that must be understood and explored by individuals and businesses. According to me, this is merely the tip of the iceberg, as we will see additional wearables with cutting-edge capabilities soon. For instance, non-intrusive wearables for HbA1C monitoring are very close to reality, which would benefit diabetic patients around the globe to monitor and act in real-time. IoT devices are also bringing transformation in many industries, including agriculture. Now there are devices that can tell a farmer the exact amount of water needed per square inch of the farming land. These devices make it possible to identify the crops infested by pests and then, separately treat only the affected yield, instead of the entire crop. Such devices are not only feature-rich but are also the primary driving force behind the Internet of Things.

    Many of the bigger cross-industry breakthroughs were anticipated in the upcoming year and beyond are likely to be built on the patterns indicated above. Furthermore, it is gaining ground and is probably going to be precursors to the technology that will define the overall course of human technology.

    Resource : https://timesofindia.indiatimes.com/blogs/voices/top-5-technology-trends-to-watch-out-in-2023/

    30Jan, 2023
    Security Technologies

    The following article, Security Technologies, provides you with an outline of the most common technologies used in security. We all aware of the pace with which the internet is growing. For everything that we had to travel or to stay in long queues for longer, the Internet had made them easy for us, and now almost all of the things are available at our fingertips. In simple words, we can say that it is not easy to imagine a single day without the internet. It is known to everyone that everything had two sides: pros and cons, and the same applies to the Internet as well. With the fast increase in Internet usage, the attacks happening in organizations have also been increased. For contemporary business or organizations, a new challenge has been evolved that protects their body from cyber attacks. Here we will be discussing the technologies that are available to protect the organizations from cyber attacks so that the flow of their operations remains smooth.

    Security Technologies

    In order to protect organizations from cyber attacks, several technologies are available to fight against them.

    Below are some of the common technologies that are used very often in organizations.

    1. Data Loss Prevention

    Data Loss Prevention may be defined as the technology concerned with validating if the data sent out from the organization is sensitive enough to hinder the business. Usually, the data are sent through emails and under this technology, the mails have been monitored to ensure that it is not carrying the confidential data out from the organization. By virtue of this technology, all the emails and their attachments are monitored closely to ensure that all the data sent outside the organization are appropriate and not something confidential.

    2. Intrusion Detection System

    An intrusion Detection System(IDS) can be defined as the technology which monitors all the traffic that enters the organization to ensure that those are not malicious. It can also be considered a tool responsible for checking the traffic and raising the alert if the traffic is found malicious or appears to be originated from the untrusted source. This technology is mainly concerned with giving a close view of the traffic to ensure that it is something that the organization should allow to get in.

    3. Intrusion Prevention System

    Intrusion Prevention System(IPS) may be defined as the technology or tool that takes action against the traffic that is labelled malicious by the IDS. Usually, the IPS drops the packet entering into the system once it is considered untrusted. It is the main protection point that makes sure that malicious traffic should not enter into the organization’s network. It is IPS that makes sure that all the traffic that enters the system should comply with the policies that are defined by the organizations so that it should not affect the working of the systems in any way.

    4. Security Incident and Event Management

    It is also known as SIEM. It is mainly concerned with invoking the alert once anything unusual is found on the organization’s network. Several tools can be integrated into SIEM to make sure that anything that is malicious must generate the alert so that the security team could take action against it and keep the internal environment protected. It also keeps track of the logs that are generated while ensuring the security of the network. It can also be considered as the central system that has other tools attached to it. All the tools work as peers that protect the network in their own way.

    5. Firewall

    The firewall works as the first layer of protection of any system or network. There are various types of Firewalls based on their role. In order to protect the internet, network firewalls are used, while in order to protect the web application, there are web application firewalls. This technology has been developed to ensure that the internal network is protected from unusual traffic, and nothing malicious could make it to the internal network. The technology ensures that the ports should be open only for the appropriate communication, and the untrusted data should not hit the system anyhow. The firewall could either allow the traffic to enter or could configure the port filtration to make sure that all the traffic passes through it must be useful for the service running on any particular port,

    6. Antivirus

    Antivirus is another technology used in cybersecurity. As its name states, it protects the system from the virus. The virus is nothing but the malicious code that makes the host or network to take unexpected actions. It is deployed in the network and can also be used as endpoint protection. All the devices connected to the network can have an antivirus installed in them to protect themselves from virus attacks. In order to detect whether the particular file is a virus, the antivirus used the signatures present in the repository of that antivirus. The latest antivirus has the capability to leverage the anomalies to detect the virus and take action against it.

    Conclusion

    The risk associated with the internet has been grown parallelly to its usage. The architecture of the web application or the network these days is supposed to be complex in order to satisfy the business needs, and with the high complexity, it also increases the chance of the system being breached. In order to ensure that the system is protected from any of the security mishappenings, the organization is expected to take all the measures seriously and use the technologies to defend their systems. On the other hand, the complex or high level of security breaches has also given birth to efficient cybersecurity technologies or tools that are ample power to ensure the system’s safety. Those tools could be leveraged to protect the system, keep the business flow smoothly and help the business focus on its operations rather than remaining concerned about its security.

    Resource : https://www.educba.com/security-technologies/

    27Jan, 2023
    What are the Common Pain Points of Python Programming?

    The common pain points of Python programming language can be overwhelming at times

    Both desktop and web-based apps may be created using the programming language Python. It is a general-purpose, high-level, open-source programming language that is mostly used for data analysis, artificial intelligence, and backend web development. Although it is one of the most approachable and simple languages to learn, those studying it for the first time may encounter certain difficulties.

    Beginners sometimes run across minor obstacles when using Python, which gives them the impression that the language is difficult. The typical problems with Python that new users typically run across are:

    Setting up

    It’s challenging to decide between pip and pipenv were given the abundance of package managers. Understanding the benefits of using virtualenv.

    Indentation

    You could find it completely absurd to have stringent indentation restrictions in a language’s grammar if you’re coming from C/C++ or Java. But with time, you’ll begin to value it in a long term.

    Pythonic Way

    If you’re used to writing code in C/C++, particularly loops, you can encounter a lot of resistance from Python programmers.

     Difficult to Master

    Don’t be deceived by Python’s easy syntax; you will always learn something new. Easy to learn, difficult to master.

    Python is one of the most solid and powerful programming languages, yet it is also quite “simple to learn.” The majority of developers and companies now use Python as their main language. We intend to assist developers like you in resolving all the significant Python issues that they encounter.

    Resource: https://www.analyticsinsight.net/what-are-the-common-pain-points-of-python-programming/

    27Jan, 2023
    Top 10 Cybersecurity Trends That Will Dominate in 2023

    Top 10 Cybersecurity Trends for 2023: Stay Ahead of the Curve and Protect Your Business

    To stay protected, businesses and individuals must stay current on the latest developments in the rapidly evolving field of cybersecurity. As a result of the widespread adoption of computerized systems by corporations, organizations, and even governments to manage their daily operations as a result of the Digital Revolution, cybersecurity has become a top priority to protect data from various online attacks and unauthorized access. As news of data breaches, ransomware, and hacks becomes the norm, it is implied that there will be a parallel shift in cybersecurity trends.

    Here are the top 10 cybersecurity trends that will dominate in 2023:

    1. Data Breaches: Organizations all over the world will continue to prioritize data. Protecting digital data is the main objective right today, whether it’s for an individual or a business. Any tiny defect or weakness in your computer’s software or browser could allow hackers access to your sensitive data.
    2. Rise of Automotive Hacking: Today’s vehicles are loaded with automated software that enables smooth connectivity for drivers in areas like airbags, cruise control, door locks, and advanced driver aid systems. These vehicles use Bluetooth and Wi-Fi to connect, which exposes them to several security flaws or hacker threats. With more automated vehicles on the road in 2023, it’s anticipated that attempts to take control of them or listen in on conversations will increase.
    3. Cloud security: The need to secure these assets grows as more businesses move their data and applications to the cloud. This entails making sure that data is safeguarded both during transit and while it is stored, as well as that cloud infrastructure is properly configured to restrict access.
    4. Artificial intelligence and machine learning: Through the use of these technologies, cybersecurity tools and systems like intrusion detection and response, threat hunting, and incident response are being made more effective.
    5. Internet of Things: Securing those devices becomes more crucial as more of them are connected to the internet. This covers everything from industrial control systems to home security cameras.
    6. Cyber insurance: Organizations are turning to cyber insurance as a way to guard themselves against monetary losses as the frequency and severity of cyber-attacks continue to rise.
    7. Email security: Email is still one of the most popular methods for cyberattacks, so businesses must put in place strong email security protocols to guard against phishing, malware, and other dangers.
    8. Identity and access management: Protecting against cyber-attacks requires making sure that only authorized users have access to sensitive data and systems. This entails putting in place multi-factor authentication and stringent access controls.
    9. Remote work: Organizations must make sure that their networks and data are properly secured as more employees work remotely. This includes implementing efficient endpoint security and granting secure access to corporate resources.
    10. Compliance and regulations: Organizations must make sure they adhere to several laws and standards, including the Health Insurance Portability and Accountability Act (HIPAA) and the General Data Protection Regulation (GDPR).

    Conclusion: These are a few of the most important cybersecurity trends that 2023 is predicted to bring about. Organizations will undoubtedly be more hesitant to increase their security measures as a result of these cybersecurity trends. This year, it’s anticipated that businesses will spend over $100 billion on asset protection alone. It would be a wise decision to begin their learning curve in cybersecurity today so that they can become authorities for tomorrow, as infrastructure security is now a significant component of almost every organization.

    Resource : https://www.analyticsinsight.net/top-10-cybersecurity-trends-that-will-dominate-in-2023/

    24Jan, 2023
    Beginner’s Guide to Neural Networks in Artificial Intelligence

    Beginner’s Guide to Neural Networks in the Field of Artificial Intelligence

    As we go through the topic of artificial intelligence we often come across several topics like neural networks. Neural networks are subsets of machine learning. In this article, we will go through the neural networks in artificial intelligence. This beginner’s guide to neural networks will help you clearly understand the different categories of AI.

    What Are Neural Networks?

    Neural networks are also known as simulated neural networks which are a subset of machine learning that forms the foundation of deep learning algorithms. Their structure and names are inspired by the design of the human brain. Neural networks are designed to mimic how biological neurons communicate with one another.

    Artificial neural networks are made up of node layers and each layer of them has an input layer, hidden layers, and an output layer. Each artificial neuron or node is linked with another and has its threshold and weight. If the output of an individual node exceeds the specified threshold value, then the is activated and begins to send data to the next layer network. or else, data is not passed to the next network layer.

    Neural networks use training data to improve and learn their accuracy over time. However, as these learning algorithms are tuned for accuracy, they become powerful tools in artificial intelligence and computer science allowing us to classify and cluster data at high speeds. When compared to manual identification by human experts, speech recognition or image recognition tasks can take minutes rather than hours. Google’s search algorithm is well-known for neural networks.

    Neurons, synapses, weights, biases, propagation functions, and a learning rule are all components of a typical neural network. Neurons will receive an input p j(t) from predecessor neurons with an activation a j(t), a threshold theta j, an activation function f, and an output function f out. Connections are made up of connections, weights, and biases that govern how neuron $i$ transfers output to neuron $j$.

    Propagation computes the input, outputs the output, and adds the function of the preceding neurons to the weight. The learning of a neural network essentially refers to adjusting the free parameters, such as weights and biases. The learning rule changes the weights and thresholds of the network’s variables. There are three basic sequences of events in the learning process. Which includes:

    A new environment simulates the neural network.

    As a result of this simulation, the free parameters of the neural network are altered.

    Because of the changes in its free parameters, the neural network then responds to the environment in a new way.

    Supervised vs. Unsupervised Learning:

    Supervised learning is how neural networks learn. Supervised machine learning is comprised of an input variable x and a desired output variable y. In this section, we introduce the concept of an environmental educator. As a result, we can say that the teacher has both an input and output set. The neural network is completely unaware of its surroundings. The input is shown to both the teacher and the neural network, and the neural network produces an output based on the input. This output is then compared to the desired output of the teacher, and an error signal is generated at the same time. The network’s free parameters are then gradually adjusted to minimise error. When the algorithm achieves an acceptable level of performance, the learning process comes to an end. Unsupervised machine learning uses input data X but produces no output variables. The goal is to model the underlying structure of the data to learn more about it. The terms classification and regression are used to describe supervised machine learning. Clustering and association are the keywords for unsupervised machine learning.

    Types of Neural Networks

    There are seven types of neural networks

    The first type of perceptron has three or more layers and employs a nonlinear activation function.

    The second type of neural network is the convolutional neural network, which employs a multilayer perceptron variation.

    The third type of neural network is the recursive neural network, which uses weights to make structured predictions.

    The fourth is a recurrent neural network, which connects neurons in a directed cycle. The long short-term memory neural network employs the recurrent neural network architecture and lacks an activation function.

    The final two modules are sequence-to-sequence modules that use two recurrent networks and shallow neural networks to generate a vector space from the text. These neural networks are extensions of the basic neural networks.

    Resource : https://www.analyticsinsight.net/beginners-guide-to-neural-networks-in-artificial-intelligence/

    24Jan, 2023
    Top 5 AI-powered Cybersecurity threats in 2023

    Watch out for the top 5 AI-Powered cybersecurity threats; the future of cyber attacks

    Cybersecurity threats are constantly evolving in today’s digital age. The increasing use of artificial intelligence (AI) in cyber-attacks has been one of the most significant developments in recent years. As AI technology advances, attackers use it to launch more sophisticated and effective attacks. These AI-powered cybersecurity threats are a growing concern for organizations and individuals alike, as they can evade traditional security measures and cause significant damage. In this article, we will explore the top five AI-powered cybersecurity threats.

    1. Advanced Persistent Threats (APTs): A sophisticated, sustained cyberattack known as an advanced persistent threat (APT) occurs when an intruder enters a network undetected and stays there for a long time in order to steal sensitive data. They frequently involve the use of artificial intelligence to avoid detection and target specific organizations or individuals.
    2. Deepfake attacks: These employ artificial intelligence-generated synthetic media, such as videos or images, to impersonate real people and carry out fraud or disinformation campaigns.
    3. AI-powered malware: Malware that uses AI has been taught to think for itself, adapt its course of action in response to the situation, and particularly target its victims’ systems. Malicious software that uses AI to avoid detection and adapt to changing environments, makes defense more difficult.
    4. Phishing: Using natural language processing and machine learning, attackers can create more convincing phishing emails and messages that are designed to trick individuals into revealing sensitive information.
    5. DDoS attacks: Distributed Denial of Service (DDoS) attacks that use AI to identify and exploit vulnerabilities in a network, allowing the attacker to amplify the scale and impact of the attack.

    These are some of the other AI-powered cybersecurity threats that are causing concern in the tech industry. Cybercriminals are increasingly employing AI to carry out sophisticated and targeted attacks on individuals and organizations. Individuals and businesses must stay informed about the latest AI-powered cyber threats and implement robust security measures to protect themselves.

    Resource : https://www.analyticsinsight.net/top-5-ai-powered-cybersecurity-threats-in-2023/

    19Jan, 2023
    What is Artificial Intelligence chatbot Chat GPT? Know why it has landed in ‘anti-Hindu’ controversy

    Artificial Intelligence is being deemed the future of the tech world, with the capabilities of several new AI projects even transcending the expectations of the human mind. Something similar has been done with a new AI technology called Chat GPT.

    Chat GPT is an AI technology that can help the user learn more about almost all the topics in the entire world. Not just this, but Chat GPT also learns from its users, gaining more and more knowledge each time you ask it a question about any topic.

    While Chat GPT is a new and innovative step towards the evolution of AI, it has recently landed in a major controversy. Many people have alleged that artificial intelligence is hurting the sentiments of Hindus through some of the content available on the religion.

    First, let’s learn what AI Chat GPT is and how it functions for a user.

    What is Chat GPT?

    Chat GPT is an AI-based chatbot, which has been developed by the company OpenAI. It has slowly become one of the most popular AI-based tools which are being actively used by students, working professionals, and teachers.

    Taking a step ahead of Wikipedia and basic Google searches, Chat GPT has emerged as an AI-powered chatbot that uses a field of machine learning known as natural language processing (NLP) to generate responses to users’ questions.

    If you ask Chat GPT a question, it will analyze thousands of web pages and content on the internet in seconds and give you a detailed answer within the set word limit. People have been using Chat GPT for homework, writing books, and even writing comedy routines.

    What is interesting is, every time you ask Chat GPT a question, it will give you a detailed answer and even learn more, which will equip it to give even better answers in the next attempt.

    What is Chat GPT’s controversy related to Hindu religion?

    Several users pointed out that Chat GPT is displaying content on the Hindu religion that can be deemed objectionable by many. An example of this is, when a user asks Chat GPT to make a joke on any Hindu deity, it displays the joke without any disclaimer or warning.

    But when the same is written for the Bible or Islamic religion, Chat GPT displays a warning that such content can hurt religious sentiments. Many are defending the AI by saying that it might not have knowledge about the Hindu religion yet, but many are also deeming it as ‘Anti-Hindu’.

    Resource : https://www.dnaindia.com/technology/report-what-is-artificial-intelligence-chatbot-chat-gpt-know-why-it-has-landed-in-anti-hindu-controversy-3017825