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How to use AI for an Identity Solution
The following blog discusses in detail how you can use AI for an identity solution. Let’s explore.
How can AI help with digital ID management?
There are various ways in which AI can help with digital ID management, and these are as follows:
- Determining the true identity of an individual becomes easier with AI.
- A facial recognition-based identity management and verification system is hard to circumvent.
- ML algorithms can detect forgery attempts concerning identity documents.
- AI-powered identity verification systems are faster than manual verification processes.
- Identity management solutions utilizing AI and ML can process a large number of ID verification requests quickly.
You can read more about AI for identity management in “How artificial intelligence can usher a new wave of identity verification services?”.
Examples of using AI for digital identity verification
The following are a few examples of AI based solutions to digital identity access management:
- Banks and financial services institutions are actively exploring AI and ML-powered identity verification solutions to detect frauds and prevent money laundering. Read more about this in “Intelligent security: how AI is revolutionizing identity verification, fraud detection and risk assessment”.
- Accenture, the technology giant is working on an AI and ML-powered digital identity and privileged access management (IAM) solution. The work-in-progress solution will improve the accuracy of updating user privileges and user access using AI and ML. You can read more about this solution in “Accenture security introduces identity management capability powered by artificial intelligence to transform the way user access privileges are managed, monitored and controlled”.
- Not only businesses but governments and lawmakers are seeing the value of AI-solutions to digital identity. E.g., legislators in Austria have brought in regulations that support AI-powered digital identity verification, as you can read in “Regulation in Austria embraces Artificial Intelligence for online identification”.
Continuing research and development into AI will likely bring a paradigm shift in identity and access management (IAM). In a not-so-distant future, AI will revolutionize identity security and data verification. AI could securely identify users by using sights and sounds, and it won‘t rely on predefined credentials. Read more about this in “Could AI improve identity management and security?”.
Building an AI based solution to digital identity
The following are the steps needed to build an AI based solution to digital identity:
1. Agree on a project scope
To start with, you need to induct a project manager (PM), an IT architect, and a team of business analysts to define the project scope and the business requirements. This team needs to work with the business stakeholders to define the business requirements.
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E.g., you might launch web apps for digital identity verification with a facial recognition feature. Additionally, you could plan to make the app efficient using AI.
2. Zero in on a project methodology
Experts have observed that the Agile development methodology fits well with artificial intelligence development projects, and you can read more about it in “5 ways to improve AI/ML deployments”. I recommend that you use Agile for lifecycle management of this identity and access management project.
3. Formulate a development approach
I recommend that you use the following development approach:
- Use a managed cloud services platform so that you don‘t need to manage IT infrastructure.
- Expedite your project using application programming interfaces (APIs) to implement the core features.
- Improve your test coverage using a test automation aid.
You can read “What is the best development approach to guarantee the success of your app?” to understand why this approach matters.
4. Estimate the project
Your PM should prepare a detailed project estimate as part of the project planning process since this would help the project sponsor to give a “go ahead” for the project. The PM should estimate the cost for the development manpower, IT infrastructure, development tools, etc.
Our guide “How much does it cost to develop an AI solution for your company?” can help you with this exercise.
5. Form a project team
You now need to form the rest of the project team with the following roles:
- UI designers;
- AI software developers;
- Web developers;
- Testers;
- DevOps engineers.
AI and ML skills are at a premium, therefore, you could find it hard to hire an AI programmer. Given that AI programming tends to be complex, you should induct a field expert development team. Read “Freelance app development team vs. field expert software development teams” to learn more about this.
6. Sign-up for AWS Elastic Beanstalk
You would like your team to focus on development instead of IT infrastructure management, therefore, I recommend that you use AWS Elastic Beanstalk. It‘s the Platform-as-a-Service (PaaS) offering from AWS, and it offers several advantages, e.g.:
- Elastic Beanstalk manages the cloud infrastructure, networking, storage, operating system, middleware, and runtime environment. This frees you up from the management of on premises resources so that you can concentrate on the web app development.
- You will find it easy to integrate databases and third- party APIs and SaaS apps with your web app.
- It‘s easy to use the DevOps tools that Elastic Beanstalk offers, moreover, you can use its auto-scaling capabilities to scale your web app easily to meet your business needs.
7. Find appropriate API solutions
The features of your proposed AI based digital identity solution will determine which APIs you should use, and I will explain this with a few examples.
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Example #1: Amazon Rekognition for facial recognition
If your proposed app includes facial recognition, then Amazon Rekognition is a good API option for you. It‘s a robust API solution to add image and video recognition features in your app. Amazon Rekognition offers the following features:
- It can identify objects, people, scenes, etc. from images and videos.
- Amazon Rekognition offers accurate facial analysis and recognition, moreover, it‘s fast and scalable.
- This API solution uses robust deep learning capabilities, moreover, it‘s an easy-to-use API.
Read “Getting started with Amazon Rekognition” to learn how you can use it, and review the Amazon Rekognition pricing plans before you buy.
Example #2: IDnow for faster verification of identity documents
Does your proposed app promise faster verification of identity documents? IDnow offers a robust API solution for this, and it‘s powered by AI, facial recognition and ML.
IDnow is an established provider of AI-powered digital identity solutions, e.g., they already work with major financial services institutions for KYC verification. This platform offers several advantages, e.g., faster onboarding, compatibility with a wide range of devices, security, performance, and scalability.
Read the IDnow API documentation to learn how to use this solution.
8. Sign-up for pCloudy to improve your test coverage
Your proposed web app should work with all available browsers and their different versions. You need to test the web app against different browsers for that, therefore, sign-up for pCloudy. It offers a wide range of browsers and mobile devices on the cloud, which helps to test web and mobile apps.
9. Design a user-friendly UI
Your app needs a user-friendly UI. I recommend that you follow the right guidelines to design one, and “User interface design guidelines: 10 rules of thumb” is a good starting point.
10. Web app development
I recommend that you code the proposed web app using Node.js, which is a popular open-source runtime environment. It‘s scalable and performant.
You should use Eclipse IDE for coding the app, however, you also need the “Enide (Studio) 2015 – Node.js, JavaScript, Java and Web Tools” plugin.
You need to read “Adding a database to your Elastic Beanstalk environment” to understand how you can add databases to your app on AWS Elastics Beanstalk. Integrate the relevant APIs with your code, and test the app.
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AWS Elastic Beanstalk offers detailed guidance on how to deploy your web app. Read “Deploying Node.js applications to AWS Elastic Beanstalk” to learn more.
Planning to launch an AI based solution to digital identity?
AI can add significant value to your digital identity management solutions, however, developing such a solution can be hard. Consider engaging reputed AI development companies for such identity management projects that can offer your product development services in full scope from advanced analytics, AI technical skills to privacy regulations complaince, etc.
Read our guide “How to find the best software development company?” to find such a company.
DevTeam.Space can help you with developing a identity management solution with secure access using the latest cutting-edge technologies.
Get in touch via this quick form describing your initial digital identity management project requirements in detail. One of our technical managers will get back to you to answer your questions in detail and to connect you with skilled software developers experienced in developing high-quality digital identity solutions.
Frequently Asked Questions
Digital identity refers to any digital information that relates to your identity. This could be anything from a digital copy of your passport to address information.
The more we interact with the web the more and more data that we provide our service providers. Facebook, for example, monitors its users’ accounts to build up a detailed picture of them including who they are messaging and what they are messaging about. This means they have detailed digital identities of their users.
The most sophisticated examples of AI include chatbots such as Siri. Other examples include the latest AI-powered fighter aircraft in the U.S air force that don’t require pilots.
