Platforms for low-code/no-code development allow developers and non-developers alike to construct mobile or web applications by simply dragging and dropping application components. They are often referred to in the same breath as the development methodologies they support. Designing apps with low-code and no-code modular techniques is easier since they do not require developers to write code individually. Those who are not software professionals, such as business analysts, office managers, and small-company owners, can also use them to create and test new applications. Traditional programming languages, machine code, or the development work behind the platform's configurable components are unnecessary for these folks to construct apps. A graphical user interface (GUI) that allows them to connect components and third-party application program interfaces is available in both cases (APIs). Modules can be rearranged and retested until the program functions as desired. To solve business problems fast, there is a shortage of competent software engineers, which has led to an increase in low-code and no-code platforms. For many workers in the digital workplace, low-code and no-code applications are becoming increasingly popular. Why is Low-Code/No-Code Important?There has been a dramatic increase in the number of tools and technology accessible to businesses during the past decade. IT departments are no longer maintaining homogeneous stacks but rather a variety of sophisticated, multi-tiered systems. And as those settings have changed, so has the IT infrastructure that supports them. There were a few decades back when an IT team relied solely on highly specialized individuals, such as a DBA who was fluent in AS/400, a director who was fluent in COBOL, and so on. Writing a script to automate the ETL process of transferring data to Cognos would be possible. Custom scripts can no longer keep up with today's demands for speed and agility, and specialization is incompatible with the more widespread cross-platform operations. The number of developers familiar with all the tools used in a given end-to-end procedure is incredibly small. Using low-code/no-code development platforms, IT can swiftly assemble new processes and build apps without studying, writing, and testing new scripts. Because the code is abstracted away, IT workers do not need to master every tool or technology involved in a set of procedures. Using the correct low-code technology, any IT team member may create dependable, cross-platform business processes and expedite application delivery. Benefits of Low-Code/No-Code to BusinessA wide selection of technologies may be used to construct anything from IT processes to commercial software using low-code/no-code development. Although there are many differences among low-code development platforms, there are several qualities that they all share in common.
Depending on the platform, monitoring, and resource management can also be included in a low-code development platform. There are several advantages to using low-code to construct applications or a more comprehensive solution that integrates all of your company's processes: Enhancement of Organizational Flexibility As a result, most low-code platforms are expandable, allowing IT to turn APIs and web services into reusable building blocks. New tools and technologies can be integrated and deployed faster, which helps organizations keep up with market developments and customer needs. Reduced CapEx/OpEx Purchased software has traditionally been quicker than designing an in-house solution. That dynamic is altered by low-code development. A low-code platform can be used instead of a new tool to quickly assemble the necessary processes for complex jobs. The IT department can construct these processes in a fraction of the time and with a fraction of the resources typically required to research, write, and test new scripts. Shadow IT is becoming less and less necessary Business teams often leave IT departments out of the loop regarding software implementation and management. Every time this happens, it boils down to the same thing: it took too long. It may dramatically minimize the requirement for shadow IT by employing a low-code development tool to deploy new processes and applications rapidly. As a result, citizen developers and coders can build their applications using user-friendly interfaces that allow business users to perform and monitor activities. Achieve More in Less Time with Automation Using low-code platforms and the correct DevOps tools helps speed up the development of automated tasks and processes. IT and business events can trigger workflows, templates can be used to create new processes quickly, and variables and flow control make it easy to handle data across platforms. As a result of pre-built connections and API access, every digital business process can be automated. Rapid Iterations The demands of the business world are constantly shifting. Low-code development platforms can assist in addressing these requirements by providing tools that allow developers to iterate and alter current processes swiftly. Many things may be done to ensure modifications are readily reversed, such as demanding documentation and providing revision history. Testing facilities can also be used to guarantee that processes will run smoothly when they are put into use in a manufacturing environment. Enhanced Efficiency IT can accomplish more in less time by streamlining the development process and expanding the usage of automation. With low-code apps, some technologies make it easier to manage processes. Monitoring and alerting capabilities can be part of this, informing staff when a process is in danger of malfunctioning. As an alternative, operations can be rerouted, or remediation workflows can be executed to maintain SLAs and decrease the time spent by IT in fighting fires. Under the budget and timetable With traditional application development, IT teams typically find it challenging to stay on budget and on time. The teams are generally understaffed and under-skilled to keep up with ever-changing standards and languages. Most of the underlying complexities may be abstracted away from developers by using low-code app development, making it easier to learn and faster to master. Most of the standard components are readily available, saving time and guaranteeing that the apps are deployed in record time. https://bit.ly/45qL83K https://images.unsplash.com/photo-1526649661456-89c7ed4d00b8?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=M3wxMTc3M3wwfDF8c2VhcmNofDR8fG5vJTIwY29kZXxlbnwwfHx8fDE2ODQyNzc3MDF8MA&ixlib=rb-4.0.3&q=80&w=2000 https://deepakguptaplus.wordpress.com/2023/05/23/breaking-down-barriers-how-low-code-and-no-code-are-democratizing-access-to-technology/
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As businesses continue to evolve in the digital era, it is becoming increasingly important to prioritize customer needs and preferences in product management. As such, product management has grown to become customer-centric in its approach. LoginRadius, a leading customer identity and access management (CIAM) platform uses customer-centric product management at the core of its product development process to drive success. This approach helps align the research and development of LoginRadius CIAM with evolving customer needs to offer a technically superior digital identity platform that meets market demands. Here, I write about how the Product Management team delivers a world-class CIAM experience by validating customer needs, implementing new capabilities while reducing product complexity, and ensuring a shorter time to go-to market. Customer-Centric Product FrameworkValidating customer needs in the ever evolving world of technology is crucial for any product management team, and the team at LoginRadius is no exception. Our Product team established a new product framework based on industry best practices to build a structured approach to understand, validate, and align customer needs with product development efforts. The key steps of our product framework are as follows:
Following this approach helps avoid assumptions and wasting time and resources on features not aligned with customer needs. Overall, the framework has played a significant role in ensuring that we continue to deliver a world-class CIAM platform for our customers without over-engineering it. Reducing ComplexityBased on insights captured from customer and interdepartmental collaboration, our Product Management team discovered that the number of features available in the platform made the product overly complex and lead to the following problems:
Once the overcomplexity issue was identified, we embarked on a product transformation journey to ensure our identity platform becomes leaner by deprecating unnecessary features and optimizing product architecture. At the same time, we made sure these changes didn’t impact existing customers but also elevated the performance and scaled the capabilities of the simplified platform to efficiently handle even the most demanding use cases and workloads. The following is a chart that compares the before and after request and response per second of our APIs from a benchmark stress testing: The Product Management team put in a lot of effort to implement this transformation. The team conducted research and analysis, built use cases, and collaborated with different teams across the whole company to help ensure the transition was well-informed, well-planned, and well-executed. As a result, the team was able to:
Improving the User ExperienceOn-screen help is the first place a customer looks for support while configuring and using any website. Our Product Management team identified areas of the LoginRadius Admin Console that were customer pain points by collecting feedback and analyzing data. Once the key problems were identified, they and worked extensively to improve the support available to the customer in those areas by adding new help options and enhancing the existing ones. Here are a few highlights of the improvements:
As a result, our customers can more intuitively understand how to navigate our admin interface and better leverage various functionalities and features. Amplifying Customer RelationOur team's customer-centric product management and initiatives have immensely improved our platform and contributed to delivering our product strategy. With this achievement, we have observed no customer churn due to our CIAM platform capabilities, as it remains robust and technically superior. KuppingerCole Analysts, a trusted advisor for identity and security solutions, has named us leaders in all major categories within their September 2022 Leadership Compass report: Originally published on LinkedIn https://bit.ly/43c4SpL https://guptadeepak.com/content/images/2023/05/product-management-loginradius.png https://deepakguptaplus.wordpress.com/2023/05/19/delivering-world-class-ciam-the-product-management-journey-at-loginradius/ In today's fast-paced digital age, where users juggle multiple online accounts, platforms, and services, the need for streamlined access has never been more crucial. Enter the world of Single Sign-On (SSO), a game-changing solution designed to simplify authentication and enhance user experience across various digital touchpoints. As organizations and individuals rely heavily on various applications and platforms, SSO has emerged as the key to unlocking seamless and secure access, playing a pivotal role in the rapidly evolving digital landscape. SSO is a security feature that allows users to access multiple applications and websites using a single set of credentials. This can be a great way to improve security and user experience. The Importance of SSO in Today's Digital LandscapeAs the digital landscape continues to expand and evolve, the importance of Single Sign-On (SSO) solutions has become more apparent than ever. SSO is critical in simplifying and securing access to the myriad of daily applications, platforms, and services that modern users and organizations rely on. Here are some key reasons why SSO is essential in today's digital landscape:
The importance of SSO in today's digital landscape cannot be overstated. Single Sign-On has emerged as an essential tool in today's digital ecosystem, offering numerous benefits to both users and organizations. By implementing SSO, organizations can enhance user experience, improve security, increase IT efficiency, and simplify compliance – all of which contribute to a more robust and streamlined digital environment. Types of Single Sign-On SolutionsVarious types of single sign-on solutions are available, each with its own strengths and weaknesses. There are four most common types of single sign-on solutions: password-based SSO, federated identity and SSO, social login, and enterprise identity providers.
Organizations should choose the type of single sign-on solution that best fits their needs based on factors such as the number of applications, the level of security required, and the complexity of their IT infrastructure. Key Considerations When Implementing Single Sign-OnSingle sign-on (SSO) is a robust authentication method that simplifies the user experience and improves security. However, implementing SSO requires careful consideration of various factors, including choosing the right SSO solution, ensuring security and privacy, and providing user onboarding and training. Let's explore these critical considerations in detail. A. Choosing the Right SSO SolutionWhen implementing SSO, the first step is to choose the right solution that meets the organization's needs. There are various types of SSO solutions, including password-based SSO, federated identity and SSO, social login, and enterprise identity providers. To choose the right SSO solution, organizations should consider the following:
B. Ensuring Security and PrivacyImplementing SSO introduces new security and privacy concerns that organizations must address. The following are critical considerations for ensuring security and privacy:
C. User Onboarding and TrainingThe success of SSO implementation depends on user adoption. The following are critical considerations for user onboarding and training:
Implementing SSO requires careful consideration of various factors, including choosing the right solution, ensuring security and privacy, and providing user onboarding and training. Organizations can successfully implement SSO, simplify the user experience, and improve security by addressing these fundamental considerations. How to Implement SSOThere are various ways to implement SSO, including using a third-party SSO provider or using an open-source SSO solution. Using a Third-Party SSO ProviderUsing a third-party SSO provider is a simple and effective way to implement SSO. A third-party SSO provider offers a cloud-based authentication service that allows users to log in to multiple applications and services using a single set of credentials. The following are the steps to implement SSO using a third-party SSO provider:
Using an Open-Source SSO SolutionUsing an open-source SSO solution is another way to implement SSO. An open-source SSO solution offers a flexible and customizable authentication service that can be tailored to the organization's needs. The following are the steps to implement SSO using an open-source SSO solution:
Implementing SSO using a third-party SSO provider or an open-source SSO solution offers various benefits, including improving the user experience, productivity, and security. By following the steps outlined, you can successfully implement SSO and reap the benefits of this powerful authentication method. Real-World Examples of Single Sign-On ImplementationSingle sign-on (SSO) is a robust authentication method that simplifies the user experience and improves security. Organizations of all sizes and industries have implemented SSO to streamline their authentication process. Some real-world examples of SSO implementation include a large enterprise and a small business. Case Study 1: A Large EnterpriseA large enterprise with over 20,000 employees faced challenges in managing user access to the organization's applications and services. Users had to remember multiple usernames and passwords, which caused frustration and reduced productivity. The IT team also faced challenges in managing user accounts, permissions, and security across multiple applications and services. To address these challenges, the organization decided to implement SSO. The Challenges The organization faced the following challenges before implementing SSO:
SSO Solution Implemented The organization implemented a federated identity and SSO solution using Security Assertion Markup Language (SAML) and Active Directory Federation Services (ADFS). The solution enabled users to log in to multiple applications and services using their corporate credentials, eliminating the need to remember multiple usernames and passwords. The IT team could centrally manage user accounts, permissions, and security, reducing the risk of data breaches and insider threats. The Results Achieved The organization achieved the following results after implementing SSO:
Case Study 2: A Small BusinessA small business with 50 employees faced challenges in managing user access to the organization's cloud-based applications and services. Users had to remember multiple usernames and passwords, leading to frustration and reduced productivity. The IT team also faced challenges in managing user accounts and permissions across multiple applications and services. To address these challenges, the organization decided to implement SSO. The Challenges The organization faced the following challenges before implementing SSO:
The SSO Solution Implemented The organization implemented a password-based SSO solution using a cloud-based identity provider. The solution enabled users to log in to multiple applications and services using a single set of credentials. The IT team could manage user accounts and permissions centrally, reducing the risk of data breaches and insider threats. The Results Achieved The organization achieved the following results after implementing SSO:
SSO implementation has various benefits for organizations of all sizes and industries. Organizations can improve the user experience, productivity, and security by streamlining the authentication process. The two case studies presented demonstrate how SSO can address large enterprises and small businesses challenges, leading to significant benefits and improvements. ConclusionSSO is a valuable security and convenience feature that can be used to improve businesses' security and user experience. There are a few different ways to implement SSO, and businesses should choose the best method. SSO is a robust authentication method that can offer many benefits to organizations. However, it is essential to carefully consider the challenges of SSO before implementing it. https://bit.ly/3MbqGuG https://images.unsplash.com/photo-1620325867502-221cfb5faa5f?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=M3wxMTc3M3wwfDF8c2VhcmNofDJ8fG1hc3RlcmNsYXNzfGVufDB8fHx8MTY4NDE4ODcwNnww&ixlib=rb-4.0.3&q=80&w=2000 https://deepakguptaplus.wordpress.com/2023/05/16/the-gateway-to-a-streamlined-digital-world-your-single-sign-on-masterclass/ As a CTO with over one and a half decades of expertise in the ever-changing field of cybersecurity, I have been observing the immense impact that artificial intelligence (AI) has had on the wide technological landscape. Also, I have witnessed how AI-based solutions have emerged as a crucial aspect of enhancing processes in various fields and disciplines over the years. And the cybersecurity field is no exception. The ability of AI-based machine learning (ML) models to identify patterns and make data-driven decisions and inferences present a highly innovative approach to quickly identifying malware, directing incident response and even predicting potential breaches before they occur. Given the significant potential of AI in the field of cybersecurity, this article explores how AI fits into the broader cybersecurity landscape and how it can be effectively leveraged to enhance the security of businesses and their users, along with some of its limitations. Exploring the intersection of Artificial Intelligence and cybersecurityIn the modern era of digitization, data is exponentially generated, and a larger amount of metadata is either saved or received online, whether directly or indirectly. Moreover, for the data to attain its intended location or be utilized for specific purposes, it is frequently crucial to transmit it across a network or store it in a specific database or server. Here is where cybersecurity practices are implemented to ascertain the ultimate safeguarding of data transmission, storage and access — which is a crucial aspect of the battle against cyberattacks. As the technological landscape advances, cybercriminals tend to execute a diverse array of illicit activities, leading to substantial disruption in the online community. However, businesses can harness the power of AI and cybersecurity to mitigate risks and enhance security by detecting fraudulent activities and cyberattacks. Having said that, AI serves as a crucial factor in machine-based decision-making. For example, a sophisticated AI system could detect dubious actions on the network and impede access until the requisite authorization is provided. These AI techniques are predicated on machine learning algorithms, empowering programmers to train algorithms using data collected over an extended duration. The AI algorithm is designed in such a way that it can recognize and differentiate between legitimate access and fraudulent access. Accordingly, it improves a business's security by making attacks and irregularities more predictable. Furthermore, AI technologies have a computational and analytical speed that surpasses human efforts and can determine abnormalities far more quickly than present techniques. As a result, AI and ML techniques can together help businesses defend against cyberattacks that could cost them millions of dollars. How to leverage AI in the cybersecurity landscapeAs previously discussed, AI has many advantages and applications in various fields, including cybersecurity. Given the rapidly evolving nature of cyberattacks and the development of sophisticated attacking mediums, AI can benefit businesses by staying up-to-date in terms of security. AI can improve threat detection through automation and provide a more effective response compared to traditional security systems and manual techniques. This helps businesses optimize their cybersecurity measures and stay ahead of potential threats. Here are some key benefits of leveraging AI in the cyber security landscape. Threat detection: Businesses can tremendously benefit from AI-based cybersecurity practices in identifying cyber threats and disruptive activities by cyber criminals. In fact, the proliferation of new malware is happening at an alarming rate, making it extremely challenging for traditional software systems to keep up with the evolving threat landscape. AI algorithms, however, discover patterns, recognize malware and find any unauthorized activities done before they impact a system. This makes AI a valuable tool for protecting against cybercrime and maintaining the security of business operations. In fact, AI and ML-based cybersecurity solutions can significantly shorten the time required for threat identification and incident response, and they can immediately notify the business of unusual behavior. Bot defense: Another realm where AI is employed to combat digital threats is the defense against bots. In today's virtual landscape, a considerable volume of web traffic is generated by bots, some of which pose potential security hazards. Bots, also identified as automatic scripts or software, are used by cybercriminals to initiate attacks on websites, networks and systems. Furthermore, bots can be utilized for a variety of malicious activities, such as Distributed Denial of Service (DDoS) attacks, takeovers of accounts and the scraping of sensitive information. AI-based solutions can be used to detect and block bot traffic by analyzing the patterns and behaviors of the incoming traffic. Machine learning algorithms can be trained to identify and flag suspicious activity, such as high volumes of artificial traffic coming from bot networks or abnormal requests. With AI, businesses can effectively discover the answers to questions like "what seems like a normal user journey?" and "what would be a potentially harmful atypical experience?" by looking at data-based behavioral patterns. Phishing detection: AI can greatly benefit the cybersecurity landscape by detecting sophisticated phishing attempts. AI-based machine learning models can analyze and classify incoming emails and messages to identify whether they are legitimate or fraudulent. By leveraging natural language processing techniques, AI can scan for keywords, phrases and other indicators that are commonly associated with phishing attacks. This lowers the possibility of a successful phishing attack by enabling security teams to swiftly identify and address possible risks. Moreover, AI algorithms can detect and flag suspicious URLs and domains. Phishing attackers often use deceptive URLs to trick users into revealing sensitive information. AI-based cybersecurity systems can analyze URLs and domain names to identify whether they are genuine or fake. These systems can then block access to malicious websites or display warning messages to users before they interact with the site. Limitations of AI in cybersecurityAI systems, despite their ever-increasing sophistication, remain beholden to limited knowledge. These systems can only function with the aid of their trained data sets, thus making them potentially impotent in the face of novel or intricate threats that lie beyond their recognized realm. Furthermore, such limitations render them susceptible to both false negatives and false positives, thus facilitating both instances of unidentified threats and unnecessary alarms. Another crucial risk confronting AI systems is the presence of inherent biases and resultant discrimination. Such biases can emerge as a consequence of unbalanced data sets or faulty algorithms, thus engendering either unfair or inaccurate assessments, potentially leading to serious consequences. Finally, there exists the formidable threat of an over-reliance on AI systems, which can lead to risky complacency and, eventually, a false sense of safety. This could then lead to a regrettable lack of focus on other crucial aspects of cybersecurity, such as user education, the enforcement of policies and regular system updates and patches. The application of AI in detecting and combating cybercrime is undoubtedly a game-changer, bringing new and improved levels of efficacy to the cybersecurity domain. Also, it goes without saying that incorporating human intelligence along with AI can overcome any possible limitations posed by AI systems. There's presently an extensive acceptance that AI plays an important part in data security, and this acceptance is anticipated to expand over the upcoming years as businesses realize its advantages. In fact, the commercial appraisal of AI in data security is expected to attain $66.22 billion by 2029, growing at a CAGR of 24.2% between 2020 and 2027. In order to stay ahead of cyber threats, businesses ought to invest in developing and implementing novel AI-based cybersecurity solutions. The continued breakthrough of AI expertise will doubtlessly influence the prospect of data security, and businesses that leverage AI effectively will be best postured to safeguard themselves against cyber threats and provide exceptional user satisfaction, thereby sustaining a competitive advantage. Originally published at Entrepreneur https://bit.ly/3p7TrRg https://images.unsplash.com/photo-1484043937869-a468066a4fbd?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=MnwxMTc3M3wwfDF8c2VhcmNofDE2Nnx8QUklMjBzZWN1cml0eSUyMGN5YmVyfGVufDB8fHx8MTY4MDgyMDUxNQ&ixlib=rb-4.0.3&q=80&w=2000 https://deepakguptaplus.wordpress.com/2023/05/05/how-ai-is-shaping-the-cybersecurity-landscape-exploring-the-advantages-and-limitations/ The e-commerce industry is flourishing, and being an e-commerce business owner, securing your platform and customer data should be your biggest priority in 2023. But the question is: Why is 2023 the time to reinvent your e-commerce security? Well, most businesses relying on legacy systems and conventional tools/technologies aren’t aware that their customers or employees can still fall prey to cybercriminals continuously finding new loopholes. On the other hand, hackers continuously create new traps through SQL injections or brute force attacks making innocent buyers victims of it. And if you wish to maintain your customers’ credibility and thrive in business, you must put your best foot forward in preventing and containing common e-commerce security threats. Let’s look at some of the cybersecurity trends of 2023 and beyond and learn how to protect your e-commerce business from them: DoS and DDoS Attacks: A denial of service (DoS) attack refers to a cyberattack where hackers try to shut down your online store by flooding irrelevant/junk traffic and making it unavailable to genuine users. And a distributed DoS (DDoS) attack is an advanced form of a DoS attack where attackers use multiple devices/botnets to infect your platform with malware. This leads to a shutdown of your e-commerce store.
While these attacks may seem identifiable and familiar, the frequency of these attacks in the e-commerce landscape is quite problematic. Let’s look at some of the best measures your business can take to reinforce its e-commerce security. How to Reinvent Your E-Commerce Cybersecurity Posture1. Perform regular SQL checks.Regular SQL checks are among the most underrated aspects of securing your e-commerce website. Ensure you turn on daily SQL checks for robust security from SQL injection attacks. Various tools and software are available online that automatically monitor and check your e-commerce platform. 2. Never collect or save unnecessary customer information.Remember, attackers, steal what you have, not what you don’t! Hence, you shouldn't save any unnecessary customer data that can be compromised and impact your customers. Using encrypted checkout while processing online payments ensures your website has no loopholes in tracing sensitive customer information. 3. Properly configure perimeter defenses.You may have a robust firewall, but it's useless if misconfigured. Relying on a third-party hosting provider has benefits but poses severe cybersecurity threats if the e-commerce platform isn’t configured correctly. Therefore, you must ensure you get certain security services and are configured correctly with your hosting. These include DDoS protection, data loss detection/prevention, antivirus/anti-malware protection, etc. With the e-commerce market experiencing a surge in demand over the past couple of years, specific security threats that require adequate attention have lingered. Therefore, you must understand the importance of incorporating and implementing the proper security measures to ensure your business remains safe and your customers remain protected and loyal. Originally published at TotalRetail https://bit.ly/40L7oBH https://images.unsplash.com/photo-1599658880436-c61792e70672?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=MnwxMTc3M3wwfDF8c2VhcmNofDV8fGVjb21tZXJjZXxlbnwwfHx8fDE2ODI2NDQzMDI&ixlib=rb-4.0.3&q=80&w=2000 https://deepakguptaplus.wordpress.com/2023/04/29/e-commerce-cybersecurity-trends-to-watch-in-2023/ Have you ever pondered the transformative impact of artificial intelligence on the realm of identity and access management (IAM)? As someone who has spent over 15 years helping companies navigate the ever-changing landscape of identity management, cybersecurity, and technological innovation, I am excited to impart my insights on this fascinating topic. Whether you're an industry expert or a curious novice, there's something for everyone in this discussion. With each passing day, AI is changing the way we work and interact with each other, having a profound impact on every aspect of our lives. The domain of IAM is no exception to this AI revolution. IAM systems that are backed by AI offer several benefits in three major aspects: authentication, identity management, and secure access. Just imagine a world where passwords are no longer needed, behavioral patterns become the new standard for identity authentication and AI and ML algorithms can effectively detect and impede security breaches even before they occur. This is no longer a far-off aspiration; rather, it's an emerging reality that is progressing rapidly. Having said that, this article will explore the benefits of incorporating AI in IAM operations, as well as limitations and best practices. Benefits Of Incorporating AI In IAMImproved SecurityBy leveraging cutting-edge AI algorithms, IAM systems can detect anomalies in user behavior and pinpoint potential threats before they cause any damage. An example of this would be the ability of an AI-based IAM system to analyze user login behavior, such as time, place, and even user actions, and mark any dubious activity. AI is also capable of providing secure authentication. By utilizing behavioral analysis, AI can establish a comprehensive profile of normal activity patterns. If any deviation from this norm occurs, AI can flag it and prompt further authentication steps like multifactor or risk-based authentication. Effectively Adhering To ComplianceWhen it comes to adhering to compliance, businesses need to ensure that they are strictly following security and privacy laws because failing to comply can result in legal and financial consequences. In the context of IAM practices, compliance requires limiting access to information to those who need it and denying it to everyone else, which can be challenging for businesses. To surmount this challenge, businesses can integrate AI and machine learning algorithms to considerably enhance their compliance with security and privacy regulations by continuously monitoring traffic, learning user behaviors, and applying precise access restrictions. A Personalized Yet Secure ExperienceThe rise of AI in the realm of IAM has revolutionized the way businesses approach user experience and engagement. With AI-powered solutions, businesses can deliver a more personalized, secure, and seamless experience to their users. Adaptive authentication is one instance of an AI-powered IAM system that encourages a personalized yet secure experience. It uses machine learning algorithms to continuously analyze and evaluate user behavior and context to determine the level of risk associated with a specific user's activity. The system can then modify the authentication requirements and prompt for additional authentication factors based on the risk assessment to deliver a more individualized experience while still ensuring security. Limitations Associated With AI In IAMAI models are as good as the data they are trained on. If the data is biased or skewed, then the AI model will reflect that bias. This can result in unfair or inappropriate decisions, which can lead to legal and ethical issues. Also, as cybersecurity threats become more sophisticated, AI algorithms require diverse and up-to-date data to detect and respond to emerging threats effectively. However, it is challenging to procure such a vast amount of quality data in practice, especially when considering small businesses. Best Practices For Getting Started• Identify the use cases. Start by identifying the instances where AI integration can add value to your identity and access management practices. • Collect and prepare the data. AI models need large datasets to function effectively. Collect and prepare data related to user activity, access logs, and other relevant information. Ensure that the data is accurate, complete, and up-to-date. • Implement a phased approach. When implementing AI-powered IAM, it is best to take a phased approach instead of going all in at once. Starting with a pilot project that targets a specific use case allows for the identification of any risks or limitations before scaling up. • Develop a data governance framework. Since AI models require access to sensitive data, it is crucial to develop a comprehensive data governance framework that encompasses data handling policies, access controls, and auditing procedures to ensure the optimal operation of AI models while keeping data secure. • Monitor the IAM systems. Regular monitoring of AI-based IAM systems is imperative to ensure their functionality, enabling the detection of any anomalies or threats. Also, regular risk assessments are vital in identifying new risks and guaranteeing that the AI-powered IAM is aligned with business objectives and cybersecurity strategy. ConclusionThe integration of AI in IAM can be a game-changer for businesses looking to provide a secure and seamless experience to their users. AI technology has already begun to revolutionize the way that businesses manage identity and access, making it more secure and efficient. I believe the businesses that implement this technological integration will be in a good position to thrive in the swiftly changing modern business landscape. Hence, as a business owner, this is the ideal time to take advantage of the opportunities presented by AI and position your business for success in the digital era. Originally published at Forbes https://bit.ly/40P6FzH https://guptadeepak.com/content/images/2023/03/forbes--The-Impact-Of-AI-On-Identity-And-Access-Management.jpeg https://deepakguptaplus.wordpress.com/2023/04/24/the-impact-of-ai-on-identity-and-access-management/ We want our customers to succeed at what they do and aim to contribute to their success in any way possible with LoginRadius CIAM. It is a core value of our Customer Success team’s philosophy and a fundamental principle at LoginRadius.
Our Customer Success team works around the clock throughout the year, regardless of the time and day, to help customers with their requirements and immediate needs. Here, I write about how we built a solid foundation for our Customer Success team and helped our customers succeed with LoginRadius. Platform IntegrationAssisting customers to enable smooth platform usage is one of our priorities. During the implementation phase, our team’s dedicated customer success managers, implementation engineers, and project delivery head closely work with our customers’ tech team to ensure seamless integration, deployment, and continuous usage of LoginRadius CIAM. Customer success managers plan and manage the onboarding project and make sure deadlines and go-live dates are met. Implementation engineers help customers with requirement analysis, complex use cases, data migration, coding, debugging, and suggestions on API workflows to ensure success. This has resulted in a 100% success rate for all of our customer implementations in production. Our assistance is not only limited to implementation; whenever prospective customers want to evaluate our platform, we proactively initiate and assist with proof of concept, including addressing customers’ concerns and working effectively with multiple stakeholders. Excellent Customer RelationsAn important measure of our performance is how well we drive value for our customers in the long run. To ensure that our customers get the most out of our platform, we have focused extensively on providing quick and responsive support and actively engaging with their important stakeholders. Firstly, we conduct monthly and quarterly business reviews to report various aspects regarding the platform usage and to get input from the customers on their needs and concerns. Some of the topics commonly covered include:
Secondly, we seek feedback at important touchpoints, such as review meetings and while resolving support tickets. It helps us prioritize specific feature implementations in our roadmap and address infrastructure and scalability concerns via our 2022 re-engineering efforts. Thirdly, we actively maintain our platform documentation so that is it easy to reference and stays updated as we release new products and features. Finally, whenever customers expect significant or high-traffic events, we provide additional live support to ensure quick communication and infrastructure stability for a seamless end-user experience. As a result, our customers have given us strong, positive feedback that they’re happy to share with the media and public. For example, here’s a press release with ITV. We have maintained excellent relationships with all of our customers and continue to engage with them proactively while helping them attain better ROI from LoginRadius CIAM. Our amazing implementation and technical support team receives good feedback from our customers.
Upholding SLAsMaintaining SLAs (service level agreements) is one of our priorities, and we’re happy to say that in 2022, we met our SLAs 100% of the time. That is, we have addressed all of our customer issues, maintained uptime, and resolved support tickets within the agreed upon time with our customers. We are determined to continue doing the same for all coming years. Specifically, four core aspects contribute extensively to maintaining SLAs at all levels: customer support, CIAM platform, DevOps, and IT infrastructure management. Our product, development, and QA teams work innovatively and actively engage with our customer success and support teams to understand customer needs and concerns. These efforts and continuous improvements deliver a superior product that is stable, secure, and highly scalable. We couple the product capabilities with exceptional DevOps and infrastructure management, so our customers will have a seamless experience with LoginRadius CIAM. Based on the customers’ use cases and data privacy regulations, our infrastructure team determines the optimal multi-cloud and multi-region deployment for best availability, leverages Infrastructure as Code (IAC) for efficient deployment and post-release management, and utilizes their in-house technical expertise to deliver the best in class SLAs. Reporting Security Events and AnomaliesLoginRadius CIAM has built-in security features which ensure real end users can smoothly log in while bad actors and malicious activities are blocked. To do so, our InfoSec team utilizes industry-leading compliance frameworks and rigorously follows best practices to ensure our organization and platform are robustly secured against threats, attacks, and vulnerabilities. In addition, whenever large-scale suspicious or abnormal activities such as unusual network traffic or bot attacks occur, we immediately block malicious actors and alert customers so that they are aware and work with them collaboratively to identify root cause and determine preventative measures for the future. We also share monthly anomalies and extensive security reports with customers so that they have peace of mind around the safety of their users’ data. Customer FeedbackWe gather customer input at every possible touchpoint, all of which translates into better understanding their immediate priorities and the broader needs emerging in the digital identity space. Our customer success and product management teams collaborate on these customer feedback, which help us enhance platform features, develop new products, add security components, improve scaling and performance, and improve the user experience on the platform's interface. As a result, in last couple of years, we have observed zero customer churn due to our CIAM platform capabilities. Final ThoughtsOverall, we continuously strive to help our customers succeed in using and getting the most value out of LoginRadius CIAM. While I’m proud of the team’s achievements and excellence in serving our customers, I’m confident that we’ll keep enhancing our standards and relentlessly push the boundaries of how we can better serve our customers. Originally published at LinkedIn https://bit.ly/43X4s83 https://guptadeepak.com/content/images/2023/04/pushing-the-boundries-of-customer-success-loginradius.png https://deepakguptaplus.wordpress.com/2023/04/22/pushing-the-boundaries-of-customer-success/ Let's start with AI, what it is and how does it work Artificial Intelligence (AI) is a technology that deals with creating and developing machines that can perform tasks that typically require human intelligence. These tasks include learning, reasoning, problem-solving, understanding natural language, perception, and decision-making. AI combines large amounts of data with intelligent algorithms that enable machines to learn from patterns or features within the data. There are several approaches and techniques to achieve AI, but some of the most common methods include the following: 1. Machine Learning: This involves training computer algorithms to recognize patterns in data and make predictions or decisions based on that data. Supervised learning, unsupervised learning, and reinforcement learning are some types of machine learning techniques. 2. Neural Networks: These are mathematical models inspired by the structure and functioning of the human brain. They consist of interconnected nodes or neurons that work together to process and learn from input data. Deep learning, a subset of neural networks, involves training large and complex neural networks to perform advanced tasks like image and speech recognition. 3. Natural Language Processing (NLP): This subfield of AI deals with the interaction between computers and human languages. NLP enables machines to understand, interpret, and generate human language in a way that is both meaningful and useful. 4. Expert Systems: These AI programs mimic a human expert's decision-making abilities in a specific domain. They are designed to solve complex problems by reasoning through knowledge, represented mainly as if-then rules rather than through conventional procedural code. 5. Robotics: This field deals with the design, construction, and operation of robots, which are machines that can perform tasks autonomously or semi-autonomously. AI techniques are often used to enable robots to navigate, sense their environment, and make decisions based on the data they collect. AI systems continuously refine their performance through training and feedback, allowing them to adapt and improve over time. This process enables AI to understand better and process complex information, ultimately leading to more accurate and efficient solutions for various tasks and challenges. AI RegulationAccording to Wikipedia, the regulation of artificial intelligence is the development of public sector policies and laws for promoting and regulating artificial intelligence (AI). Regulation of AI refers to the rules, policies, and guidelines established by governments, organizations, or other regulatory bodies to ensure the responsible, ethical, and safe development and deployment of artificial intelligence technologies. AI regulation aims to address privacy, security, fairness, transparency, and accountability concerns while promoting innovation and benefiting society. We looked into the Pros and Cons of AI Regulation, and now let's check out what is the current state of AI regulation. The Current State of AI RegulationThe current state of AI regulation is still in its early stages, with various countries and organizations working on developing policies and guidelines to address the challenges posed by AI technologies. There is no globally unified regulatory framework for AI, and approaches to AI regulation vary across different regions and jurisdictions. Some key developments in AI regulation include: 1. European Union (EU): In April 2021, the European Commission proposed new regulations on AI, known as the Artificial Intelligence Act. This proposal aims to create a legal framework for AI in the EU that focuses on ensuring human-centric, safe, and transparent AI while promoting innovation. The proposed regulations cover aspects such as data protection, transparency, accountability, and the prohibition of certain AI practices that are considered harmful. The EU has also established the High-Level Expert Group on AI, which has published AI ethics guidelines emphasizing the need for AI to be trustworthy, transparent, and aligned with human values. 2. United States: AI regulation in the US is primarily sector-specific and varies across different federal and state agencies. There is no comprehensive federal AI regulation, but recently released a Blueprint AI Bill of Rights in Congress. The National Security Commission on Artificial Intelligence (NSCAI) report includes recommendations on AI research and development, workforce, national security, and ethical considerations. The White House Office of Science and Technology Policy (OSTP) has been working on developing AI policies and guidelines, including the American AI Initiative. In 2021, NIST released an initial draft of an AI Risk Management Framework (AI RMF), which had already been revised twice. 3. India: The Indian government is taking a cautious approach to AI regulation and has not yet implemented any specific regulations for artificial intelligence (AI). In 2018, the National Strategy on Artificial Intelligence (NSAI) was released. The NSAI identifies several ethical and legal issues that need to be addressed in the development and use of AI. It also recommends the development of a national AI framework that would include guidelines for the ethical development and use of AI. The government is working to develop a regulatory framework that will promote the responsible development and use of AI in India. 4. China: China has actively promoted AI development and released various national strategies and plans, such as the New Generation AI Development Plan 2017. While comprehensive AI regulation is still under development, China has issued guidelines on AI ethics and governance, emphasizing the need for AI to be controllable, transparent, and secure. Some local governments in China have also introduced AI and data protection regulations. 5. United Kingdom: The UK government is taking a proactive approach to AI regulation. In 2021, the government published a white paper on AI regulation, which sets out several principles for the responsible development and use of AI. The government works with industry, academia, and other stakeholders to develop the framework. The UK government revised the policy in March 2023. It took a pro-innovation approach to AI regulation based on the belief that AI has the potential to bring significant benefits to society. 6. International organizations: Various international organizations are working on AI governance and regulation. The Organization for Economic Co-operation and Development (OECD) has published AI principles that emphasize the need for AI to be transparent, safe, and respect human rights. The United Nations (UN) has also initiated discussions on AI governance and ethics through forums such as the International Telecommunication Union (ITU) and the UN Educational, Scientific, and Cultural Organization (UNESCO). Moreover, the World Economic Forum (WEF) has established the Global AI Council to help shape global AI policies and promote responsible AI development. 7. Industry and research initiatives: Several technology companies and research institutions have released their own AI ethics principles and guidelines, which address issues like fairness, transparency, accountability, and privacy. For example, Google, Microsoft, and IBM have each published their own AI ethics guidelines. Furthermore, initiatives like OpenAI and the Partnership on AI bring together industry leaders, researchers, and organizations to collaborate on AI ethics and safety research. The current state of AI regulation is a patchwork of regional, national, and industry-specific initiatives. Policymakers and stakeholders worldwide are actively working on developing regulatory frameworks to address the complex challenges posed by AI. As AI continues to advance and impact various aspects of society, regulators must balance the need for innovation with protecting individual rights and promoting ethical AI development. Critical Aspects of AI Regulation1. Data protection and privacy: Ensuring that AI systems handle personal and sensitive data in compliance with data protection laws, such as the General Data Protection Regulation (GDPR) in the European Union. This involves safeguarding user privacy, enabling data portability, and providing users with control over their data. 2. Bias and fairness: Ensuring that AI algorithms do not perpetuate existing biases or unfairly discriminate against specific individuals or groups based on factors such as race, gender, or socioeconomic status. Regulations may require AI developers to test for and mitigate bias in their systems. 3. Transparency and explainability: Requiring AI systems to be transparent in their decision-making processes, enabling users to understand the rationale behind the AI's decisions. This may involve creating guidelines for AI developers to design interpretable and explainable models. 4. Accountability and liability: Establishing clear lines of responsibility and accountability for the actions and decisions made by AI systems. This may involve determining whether the AI developer, user, or another party should be held liable for any harm or damage caused by the AI. 5. Security and safety: Ensuring that AI systems are designed and deployed securely, minimizing the risks of data breaches, unauthorized access, or malicious use. This may involve setting standards for AI system security and requiring developers to adopt best practices for secure development. 6. Ethical considerations: Promoting the development of AI technologies that align with human values and ethical principles, such as respect for autonomy, beneficence, and justice. This may involve creating ethical guidelines for AI developers and encouraging the integration of ethics into AI research and development. 7. Oversight and monitoring: Establishing mechanisms for the ongoing monitoring and evaluation of AI systems, ensuring that they continue to meet regulatory requirements and respond to new risks or challenges that may emerge over time. The regulation of AI is an evolving field, as governments and organizations worldwide are still developing and refining their approaches to address the unique challenges posed by AI technologies. As AI continues to advance, it is crucial for stakeholders, including policymakers, industry leaders, researchers, and civil society organizations, to collaborate in creating a balanced regulatory framework that promotes innovation, protects individual rights, and ensures the responsible and ethical use of AI. Potential Challenges and Issues with AI RegulationSeveral challenges associated with AI regulation can make it difficult for policymakers and stakeholders to develop a comprehensive and practical regulatory framework. Some of these challenges include: 1. Rapid technological advancements: AI technologies are evolving quickly, making it challenging for regulators to keep up with the latest developments and ensure that policies remain relevant and practical. Policymakers must balance providing adequate oversight and not stifling innovation by over-regulating the industry. 2. Global nature of AI: AI technologies are developed and deployed globally, which can create inconsistencies in regulatory approaches across different countries and jurisdictions. This can lead to fragmented regulations, making it difficult for companies to navigate and comply with various regional and national requirements. 3. Balancing innovation and protection: Regulators must balance fostering innovation and protecting individuals' rights, safety, and privacy. Overly restrictive regulations may hinder the development and adoption of beneficial AI technologies, while insufficient regulations may expose individuals to potential harms and risks associated with AI. 4. Defining ethical principles: Ethical considerations are a crucial aspect of AI regulation, but it can be challenging to translate universally accepted ethical principles into enforceable policies. Different cultures and societies may have varying perspectives on what constitutes ethical AI, leading to potential disagreements and conflicts in developing regulatory frameworks. 5. Technical complexity: AI technologies, particularly machine learning and deep learning models, can be complex and difficult to understand, which poses challenges for regulators in evaluating their safety, fairness, and transparency. Developing regulations that effectively address these technical aspects without inadvertently limiting the potential of AI technologies is a significant challenge. 6. Bias and fairness: Ensuring that AI systems are unbiased and fair is critical. However, detecting and mitigating biases in AI algorithms can be challenging, primarily when they are based on extensive and diverse datasets. Regulators must develop methods to assess AI systems for potential biases and establish guidelines for developers to create fair and unbiased AI models. 7. Explainability and transparency: Many AI models, and deep learning systems, are often considered "black boxes," making it difficult to understand their decision-making processes. Developing regulations that promote explainability and transparency in AI systems may be challenging due to the inherent complexity of these technologies. 8. Accountability and liability: Determining who should be held responsible for the actions and decisions made by AI systems can be challenging, as the responsibility may lie with various parties, including developers, users, or even the AI systems themselves. Establishing clear lines of accountability and liability while considering the complexities of AI technologies is a significant challenge for regulators. 9. Cross-sector applicability: AI technologies are used in various sectors, including healthcare, finance, and transportation, each with its unique requirements and risks. Developing a one-size-fits-all regulatory framework may not be feasible, making it necessary to tailor regulations to specific industries and applications while maintaining a consistent overall approach. 10. Enforcement and monitoring: Enforcing AI regulations and ensuring compliance is another challenge, as it requires the development of practical monitoring mechanisms and tools to assess the performance and behavior of AI systems. Regulators must also determine the appropriate penalties and enforcement actions for non-compliant entities, which may be difficult due to AI technologies' global and rapidly evolving nature. These challenges highlight the need for a collaborative approach to AI regulation involving governments, industry leaders, researchers, and civil society organizations. By working together, these stakeholders can develop regulatory frameworks that address AI's potential risks and ethical concerns while promoting innovation and benefiting society. Next Steps on AI RegulationThe following steps in AI regulation involve a combination of ongoing efforts, collaboration, and new initiatives to address the challenges posed by AI technologies. Some key steps include: 1. Harmonizing global regulations: International cooperation and dialogue among countries and organizations will be crucial in developing a harmonized approach to AI regulation. Sharing best practices, lessons learned, and collaborating on common principles can help create a more consistent global regulatory environment that supports innovation while protecting individual rights. 2. Adapting to technological advancements: Regulators must stay informed about the latest AI developments and be prepared to update and adapt regulations as needed. This may involve creating flexible, future-proof regulatory frameworks that can accommodate rapid technological advancements while maintaining core principles. 3. Public-private partnerships: Collaboration between governments, industry leaders, researchers, and civil society organizations is essential in shaping AI regulations. These partnerships can help to ensure that diverse perspectives are considered and that regulations strike the right balance between promoting innovation and addressing potential risks and ethical concerns. 4. Sector-specific regulations: Policymakers should consider developing tailored regulations for specific industries and applications, as AI technologies have different implications and risks depending on the sector. This approach can help to address unique challenges and ensure that regulations are relevant and effective for specific use cases. 5. Promoting AI ethics and responsible AI development: Encouraging the integration of ethical principles and guidelines into AI research and development practices is an essential step in fostering responsible AI innovation. This can be achieved through education, training, and the development of tools and resources to help AI practitioners design and deploy AI systems that align with human values. 6. Strengthening enforcement and monitoring mechanisms: It is crucial to develop practical tools and mechanisms to monitor AI systems' compliance with regulations and assess their performance. This may involve the establishment of new regulatory bodies or the enhancement of existing ones, as well as the development of standardized metrics and evaluation methodologies for AI systems. 7. Public awareness and engagement: Raising public awareness about AI technologies, their potential benefits, and associated risks is essential in ensuring a well-informed public discourse on AI regulation. Engaging with the public and soliciting their input on AI policies and regulations can help ensure that diverse perspectives are considered, and AI technologies are developed and deployed to benefit society. 8. Ongoing evaluation and refinement: AI regulation is an evolving field, and it is essential for regulators to continuously evaluate and refine their approaches based on new developments and lessons learned. This may involve regular reviews of existing regulations, incorporating stakeholder feedback, and updating policies to address emerging risks and challenges. By focusing on these next steps, policymakers and stakeholders can work together to develop a comprehensive, practical, and adaptable regulatory framework for AI that balances the need for innovation with the protection of individual rights, ethical considerations, and societal benefits. https://bit.ly/3L5rJx3 https://images.unsplash.com/photo-1477414348463-c0eb7f1359b6?crop=entropy&cs=tinysrgb&fit=max&fm=jpg&ixid=MnwxMTc3M3wwfDF8c2VhcmNofDEwfHxwb2xpY3l8ZW58MHx8fHwxNjgxNjg1ODY1&ixlib=rb-4.0.3&q=80&w=2000 https://deepakguptaplus.wordpress.com/2023/04/17/navigating-the-future-of-ai-understanding-ai-regulation/ When I founded LoginRadius, we were one of the earliest players in the CIAM (customer identity and access management) domain, which was slowly evolving. Now, CIAM has mostly decoupled from IAM as a standalone enterprise solution. Furthermore, as digital experiences increasingly play a significant role in product differentiation and competitive advantage, CIAM has become even more specialized to help consumer-facing applications serve seamless and engaging user experiences. While we want to help as many enterprises as possible with our CIAM platform to serve their end users better, we also believe in opening up virtual dialogues about information security, CIAM, and digital experiences for retail or consumer-facing businesses so that business leaders can understand the complexity and make informed decisions. This is why we chose to create content by leveraging our critical thinking, technical expertise, and experience-based first-hand domain knowledge. LoginRadius Engineering BlogOver the past few years, we wanted developers to engage more with the LoginRadius platform. In line with this, we started the LoginRadius Engineering Blog, which progressively drew in a larger technical readership. The following provides a more detailed insight into our approach. Firstly, we established a new initiative through our engineering blog where LoginRadius developers could write articles and share their knowledge on various tech stacks and the LoginRadius CIAM platform. This initiative resulted in the publishing of top-notch, profoundly insightful, and actionable articles. As a result:
We also teamed up with other developers online who have practical experience under their belt to create and publish engineering tutorials sharing their expertise. Secondly, through our numerous articles, we have helped developers quickly and easily integrate CIAM into their website or app, which then allows them to focus more on building out and enhancing their own products and platforms. Finally, we also created engineering stories that help enterprises and engineering leaders get a glimpse into what we do at LoginRadius to push the boundaries of CIAM. For example, our lead architect Vijay Singh has written an article on Why We Re-engineered LoginRadius APIs with Go? These efforts have improved our visibility on search engines, helped enhance brand awareness, and driven engagement with positive results for us. In metrics, the engineering blog’s traffic has grown more than 2x, positively impacting our domain authority and thought leadership. Graph showing users and sessions growth from 2020 to 2022: Leadership Articles and eBooksFollowing the successful implementation of developer engagement, brand awareness, and CIAM community involvement, the next objective in the strategy was to be more active in technology leadership to reach the wider community. I aimed to educate individuals on the digital identity domain and the significance of managing identity, authentication, and authorization to achieve a seamless user experience and enhance cybersecurity. As part of this initiative, I strategized and wrote many articles around digital identity and cybersecurity with modern insights and innovative thinking for renowned magazines and websites such as Forbes, Entrepreneur, VentureBeat, and HackerNoon. The vast audience of these industry leaders helped us spread domain knowledge to many individuals. Next, we identified impactful topics and wrote short yet insightful ebooks to share in-depth information and actionable items about data privacy and the transition to passwordless authentication. The motive was to help enterprises with their digital operation and access management from the experience and learning gained in managing 100M+ user identities at LoginRadius. As anticipated, these efforts have positively impacted the tech industry and opened doors for us to connect with the CISOs and CIOs of Global 2000 companies. Our collaboration with these industry leaders has allowed us to offer valuable guidance and support to enhance their CIAM strategies and broaden their understanding of identity-related issues. We also assisted these organizations in navigating the constantly evolving landscape of cybersecurity and data protection. ConclusionOverall, as an early innovator in the CIAM domain, I believe in creating useful, knowledge sharing content to help businesses of various sizes understand the growing significance of CIAM in securely managing consumer-facing applications and ecosystems. Going forward, we’ll continue to create and share more useful and insightful information. Originally published at LinkedIn https://bit.ly/3zX2GWG https://guptadeepak.com/content/images/2023/04/1679653054610.png https://deepakguptaplus.wordpress.com/2023/04/15/why-we-focused-extensively-on-strategic-technical-content/ Third-party vendors are a common part of the business world, and they can provide a number of benefits to businesses. However, third-party vendors also pose a risk to businesses, as they can be a gateway for cyber-attacks. In recent years, businesses have increasingly become dependent on third-party vendors to maintain their systems/infrastructure as well as provide various services to clients. However, most companies have not invested in third-party vendor management programs, thereby increasing cyber-attacks. Several organizations and agencies such as online retailer Target Corporation, Marriott Corporation hotel chain, and the United States Office of Personnel Management have faced cyber criminals' attacks due to lack of adequate security when interacting with third-party vendors. As a result, third-party vendors can be described as one of the gateways to being exploited by cyber-criminals. How third-party vendors can be a gateway for cyber-attacks: The rise in third-party data breaches has resulted in exposing organizations’ private data as well as other massive adverse impacts. In most cases, third-party-related threats exist when the client provider's or third-party system or connection between the two parties is unsecure. Companies are likely to work with several third-party vendors during their daily operations from human resources, web development, design, and other service providers. Granting these service providers remote access to the organization's network results in creating a new threat vector. More so, connection to physical connectors such as integrating security systems with cameras, building management systems printers, and HVAC, among others, often increases an organization’s proneness to cyber-attacks. The increased use of the Internet of Things (IoT) operated through third-party vendors has increased cyber-criminals' abilities to gain access to firms' data and systems. Third-party vendors can be a gateway for cyber-attacks in a number of ways. For example, third-party vendors may have access to sensitive data, such as customer information or financial data. If a third-party vendor is hacked, this data could be compromised. Additionally, third-party vendors may not always have the same level of security as the businesses they work with. This means that if a third-party vendor is hacked, the business they work with may also be hacked. Risks of using third-party vendors: There are a number of risks associated with using third-party vendors. These risks include:
How to mitigate the risks of using third-party vendors: There are a number of things businesses can do to mitigate the risks of using third-party vendors. These include:
The use of third-party vendors to facilitate organizations' operations and service delivery has led to the emergence of new cyber-related threats. The best approach to mitigate these threats is for companies to invest in imposing cyber-security standards on their third parties vendors. https://bit.ly/3mjhvjc https://guptadeepak.com/content/images/2023/04/How-Do-Third-Party-Vendors-Work-As-A-Gateway-To-Cyber-Attacks-By-Criminals---guptadeepak.com.jpeg https://deepakguptaplus.wordpress.com/2023/04/11/the-risks-of-third-party-vendors-a-gateway-to-cyber-attacks/ |
AuthorChristopher Reardon, PhD, is a computer scientist and experimental roboticist. He joined the faculty at the University of Denver in late 2020 as an Assistant Professor in the Department of Computer Science at the Ritchie School of Engineering and Computer Science. Prior joining DU, Dr. Reardon spent over four and a half years as a research scientist at the U.S. Army Research Laboratory, where his research focused on enabling better human-robot teaming for autonomous mobility and maneuver contexts. Dr. Reardon has published over 25 peer-reviewed journal and conference articles to date. He is an IEEE Senior Member. He is actively involved in the greater robotics research community, regularly serving in organizational leadership roles at conferences and workshops. ArchivesNo Archives Categories |