From the project management tools you use at work to the mobile games you play during your free time, you encounter data authorization processes every day. It’s because of this process that only you can edit your social media posts and get access to company information that other teams don’t (and vice versa). Without a data authorization system, companies can’t protect their stakeholders’ sensitive information, criminals will impersonate other people for fraudulent purposes, and—at risk of sounding too dramatic—chaos will abound.
Read on to find out what data authorization is, the difference between data authentication and data authorization, and how AI and machine learning can enhance the data authorization process.
Data Security: What Is Data Authorization?
Authorization is a security mechanism for controlling how much access a person has to system resources, including computer programs, files, data, services, and application features. It’s how you ensure that users have access to their data and are restricted from seeing data that isn’t theirs.
During data authorization, a system grants or denies a user access to various resources based on their identity. For example, in organizations, employees have varying levels of access to different resources. A junior clerk won’t have the same access to company information as the CEO (or any other C-suite executive). Also, employees get access to resources based on the kind of jobs that they do. So while the Human Resources team has access to employees’ bank information, other teams don’t.
By restricting access to certain information, businesses are able to protect their most crucial data, including payroll information, intellectual property, medical records, customers’ identities, and more. Data authorization can also help employees find the information they need quickly without having to sift through a digital pile of documents and folders.
Data Authorization vs. Data Authentication: What’s The Difference?
Many people often use the term “auth” to refer to both data authentication and data authorization. While both mechanisms overlap in application security, they aren’t the same thing.
Data authentication is the mechanism for verifying the identity of a user. For example, if you want to access a social media platform, app, or service, you have to enter your username and password. Behind the scenes, the system compares the username and password you entered to the record it has on its database. If they both match, the system assumes that you are a valid user and grants you access to the information within.
In this example, the system authentication process is predicated on the assumption that you alone would know the correct username and password. So it authenticates you based on information that only you should know.
Authorization, on the other hand, is the mechanism of controlling what a user can do within a system application. This stage often builds on the authentication process, especially when the identity of the user directly correlates with the level of data access they should have.
Here’s an example: Ashley and Sylvia work at a clothes store as a salesperson and store manager respectively. Each morning, they both have to sign in on the app on which they store company information. Since they both serve different purposes in the store, the app grants Ashley and Sylvia varying levels of access. As a salesperson, Ashley is only able to view and fulfill orders through the app. However, Sylvia, being a manager, has access to the daily sales totals and yearly revenue.
Data authentication verifies the users (Ashley and Sylvia) before allowing them access to the app, and data authorization determines what they can do within the app when the system grants them access (fulfill orders and view sales information).
What Are The 3 Types Of Data Authorization?
A typical data authorization layer is designed based on three factors: attributes, roles, and relationships. Here’s how they all work:
- Attribute-based access control
Attribute-based access control (ABAC) is an authorization method that grants users access to data based on their characteristics, such as department, location, and manager.
For example, an online store that sells alcoholic drinks will ask a potential customer to register and provide proof that they’re above the legal drinking age in their country. In this scenario, the store is the resource owner and the alcoholic drinks are the resource. The proof of age is the user’s attribute, which has to be validated for them to be able to order drinks.
With ABAC, access rules are based on if-then statements that define the user, request, and action. In the example above, the boolean logic would be: “If a customer is 18+ years old—or 21+ years old, for Americans—then allow them to order drinks”. But in an organization, it could be: “If user is a human resources manager, then allow them access to company’s payroll information” or “If user is a software developer, then allow them access to the product’s code.”
ABAC allows organizations to protect their data, cloud services, IT resources, and network devices from unauthorized actions or users.
- Role-based access control
Role-based access control (RBAC) allows systems to manage and grant user permissions by grouping them into roles. These roles are a collection of permissions, but the granularity at which these roles apply differs.
For example, in a WhatsApp group, the “admin” or “owner” (an organization-level role) has more access to app features than a member of that group. So while a group member can only send and reply to messages, an admin can send and reply to messages, add and remove people from the group, lock the group so that no one can send messages except them, and more. Those are the features that the role of “admin” has access to.
For resource-specific roles, however, access to information is granted on a more granular level. An example is Google Docs, where a user can control how much access other users have to their document based on the Viewer, Commenter, and Editor roles.
So if a user makes you a viewer on a document, you can see the doc but you can’t make any changes. But if you’re an editor, you won’t only see the doc, but you can comment, make suggestions, and make changes to its contents.
RBAC makes it easier for system managers to manage authorization requests because they can deal with permissions in bulk.
- Relationship-based access control
As applications become more complex, more granular controls are needed to ensure that only the right users get access to resources. That’s where relationship-based access control (ReBAC) comes in.
Just like RBAC, ReBAC also aggregates a set of permissions. But instead of assigning them to groups (or roles), it assigns them based on the relationships between users and resources (e.g., lists, documents, folders, projects, etc.). These relationships can be defined at any level of the resource hierarchy, and they build on the concept of roles.
For example, if you, a user, want to post a picture on Facebook, you can choose who can see it: the public, your friends, some friends, or just you.
All the categories on this list are roles. But when you choose a particular role, Facebook displays your post based on the relationships within that role. So if you choose Friends, Facebook will only show your picture to people whose friend requests you accepted (and vice versa). The accepted friend requests constitute the relationship between you and those who can see your post.
In the same vein, if you create a post on any social media platform, you’re the only one who can edit it because of your relationship to that resource.
Allowing relationships to determine authorization policies significantly lowers the effort it takes to manage data and complex interconnections—which makes for a fine-grained access model. The downside of ReBAC, however, is that the granularity of relationships can cause the application to perform more authorization checks.
What Makes Data Authorization So Difficult?
Data authorization might seem like a straightforward process, but it’s not. And the bigger an application is, the more difficult data authorization is to achieve. Below are some reasons why data authorization is so difficult:
- The omnipresence of authorization logic
An application’s authorization mechanism assesses every action or request a user makes. Therefore, most, if not all, components in the application have to make authorization decisions. This means that authorization logic has to be written everywhere in the application’s codebase, which is a daunting task, especially as that logic has to be isolated from the rest of the application components (to ensure easier updates, if need be).
- Ensuring high-level performance
Since the authorization layer makes decisions for every action or request in an application, it has to deliver these decisions in milliseconds. Making sure that the authorizing mechanism has all the information it needs to make decisions in so little time is difficult.
- Scaling the application
As an application grows, users make increasing authorization requests—the application’s authorization policies must scale. To scale easily, the authorization layer has to be separate from the rest of the application. But this is tricky because the application depends on the decisions made by the authorization layer—and decisions are determined by the data that comes from the application.
- Resolving identities
Many applications use an identity provider, such as AuthO and Okta, to resolve user identities. Since the identity provider is an external device, it’ll need to integrate seamlessly with the authorization layer. However, it’s difficult to guarantee that the identity provider will work at all times or that the response times will be quick.
And since user identities are mapped to specific roles and/or attributes, the identity provider will have to store, maintain, and serve up this information to decision mechanisms as quickly as possible.
- Evolving requirements
When an authorization layer is first created for an application, it’ll include roles and permissions. However, as the application grows, engineers have to update the authorization layer to support new roles and permissions, more streamlined models, and other components.
This means that they’ll have to rewrite the authorization layer multiple times for as long as the application exists. If the authorization layer is coupled to the application, engineers will have to tweak the app’s code as well, which is time-consuming. Since authorization can become quite complicated sometimes, many developers share their insights in their Devblogs, to show their fellow engineers how they came up with the solution to some of these intricate problems.
Intelligent Authorization: How Do AI & Machine Learning Improve Data Authorization?
Many industries have started adapting artificial intelligence and machine learning in their processes, especially as the public perception of AI has turned into a more positive one. Cybersecurity can also do so in terms of data authorization. Here are five ways that AI and machine learning can improve data authorization:
- Enhanced anomaly detection: AI algorithms can learn the normal behaviors of users from historical data, and use it to flag unusual behavioral patterns or deviations. For example, if an employee is trying to access sensitive information that they’ve never tried to access before, the system can label this as an anomaly and prompt more authentication steps or deny them access.
- Predictive analysis: Machine learning models can analyze previous access patterns to predict future user behavior. By analyzing how users normally access data, the system can suggest the right authorization levels. For instance, if a content strategist is promoted to the position of Vice President of marketing, the system can predict which information they might need access to based on the role change.
- Contextual access control: AI systems can apply context to an authorization request based on user roles, locations, and devices. An example of this is an employee with the right clearance being able to access sensitive data within the company’s network, but unable to access that same data while using public WiFi.
- Adaptive learning: AI systems and machine learning models can learn from users’ behavioral data and adapt accordingly. This ensures that the authorization mechanisms are up-to-date and effective against changing user patterns and external threats.
- Continuous monitoring: AI systems can monitor access requests and user behavior nonstop. This ensures that they’re able to detect suspicious activities and emerging threats in real time. For example, if an authorized employee starts accessing data from unusual locations, the application can immediately respond to prevent a data breach.
How To: Implementing AI & Machine Learning Into Existing Data Authorization Processes
If you want to integrate AI and machine learning technologies into your existing data authorization processes while minimizing disruption, here are the steps to take:
- Assessment and goal-setting: Evaluate your existing data authorization processes to identify any component that can benefit from AI and machine learning models. After making your observations, define clear goals for the integration of these technologies—enhancing user experience, improving security, reducing false positives, etc.
- Collection of data: Gather, organize, and clean historical data on user attributes, roles, relationships, behaviors, and permissions. You’ll use this data to train and validate AI and machine learning models
- Choose the right algorithms: Once the data is ready, choose AI and machine learning algorithms that work well for your use cases. For example, to improve anomaly detection, you can use algorithms like Isolation Forest and LSTM networks. But for predictive analysis, algorithms like decision trees and neural networks are effective.
- Run tests: Divide your data into two sets: training data and testing data. Use the training data to teach AI models how to recognize anomalies and patterns. When that’s done, use the testing data to fine-tune and validate your models. Continuously tweak the parameters until you achieve optimal performance.
- Integrate AI and machine learning models with your authorization system: Create an intermediate layer that connects your AI and machine learning models to your current data authorization system. Implement APIs that allow your authorization system to send access requests to your AI technologies, and receive AI-generated recommendations or decisions.
- Continuous improvement: Regularly monitor the impact of your AI and machine learning models. To identify areas for improvement, collect feedback from administrators and users about the performance of these models.
Pro tip: To enhance performance, you can treat your data as a product and implement a real-time monitoring system that perpetually feeds new access data to your AI models. You can set up a feedback loop so that the AI system can learn from its decisions and adjust its models accordingly.
- Security and compliance: As you execute this project, ensure that your AI-enhanced authorization process complies with data protection and security regulations. Also, regularly assess and audit the system’s security to identify and reduce potential weaknesses. In case of anomalies or risky activities, implement a notification system that alerts security personnel in real time.
Enhance Your Data Authorization Process With Artificial Intelligence
Data authorization is an integral part of creating an application. Since it ensures that only the right individuals or entities have access to certain information, data authorization safeguards sensitive information and prevents security threats, as well as reduces the risk of data tampering, misuse, and unauthorized breaches.
This process, however, can be enhanced by the integration of AI and machine learning technologies, which can learn to predict user behaviors, detect anomalies, and monitor access requests in real-time.
