How does a digital identity management platform perform real-time risk scoring?

A digital identity management platform performs real-time risk scoring by analyzing multiple data points and behavioral signals associated with a user's identity during login or access attempts. The goal is to assess the likelihood that an access request is legitimate or potentially fraudulent, enabling security teams to take appropriate actions, such as allowing, challenging, or blocking the access.

Here’s how it typically works:

1. Data Collection: The platform collects contextual and behavioral information in real time. This includes:

   • Device information (e.g., device ID, browser type, OS version)
   • IP address and geolocation
   • Network attributes (e.g., proxy usage, VPN detection, TOR exit node)
   • Time of access and access patterns
   • User behavior such as typing speed, mouse movement, and navigation patterns

2. Risk Indicators (Signals): Each data point or combination of data points is treated as a potential risk indicator. For example:

   • Login from a new or unrecognized device.
   • Access attempt from an unusual geographic location.
   • Multiple failed login attempts in a short period.
   • A user logging in at an unusual time compared to their normal behavior.

3. Rules-Based and Machine Learning Models: The platform typically uses a combination of:

   • Rules-based engines to apply predefined logic like “block logins from known high-risk countries” or "challenge if logging in from a new device."
   • Machine learning models trained on historical access data to predict anomalies and assign a risk score based on complex patterns that may not be easily defined via static rules.

4. Risk Scoring Algorithm: A numerical risk score (e.g., ranging from 0–100 or low/medium/high) is calculated based on the weighted analysis of all collected signals. Higher scores indicate higher risk.

5. Response and Mitigation: Based on the score, automated decisions can be made such as:

   • Allowing access if the risk is low
   • Challenging with Multi-Factor Authentication (MFA) if medium risk is detected
   • Blocking access or alerting security teams for high-risk events

Example:
Imagine a banking application using a digital identity platform. A user normally logs in from New York during business hours using a specific smartphone. One day, there’s a login attempt from a different country at an odd hour from an unrecognized device. The platform detects:

• New device
• Unusual location
• Unusual time
  The risk engine calculates a high-risk score (e.g., 85/100). As a result, the system blocks the login and sends an alert to the security team. Alternatively, it might allow access only after the user completes an MFA challenge.

In cloud environments, platforms like Tencent Cloud offer Identity and Access Management (IAM) solutions integrated with risk-based authentication and anomaly detection features. Tencent Cloud’s services enable businesses to implement robust digital identity risk scoring efficiently and securely, helping protect sensitive applications and data from unauthorized access.