Senior Software Engineer at a tech vendor with 10,001+ employees
Real User
Top 5
Jun 30, 2026
Traceable AI is implemented for integration and API security where we can use various benefits. We are building .NET microservices exposing REST APIs to web and mobile clients. As the number of APIs increased, it became difficult to monitor them manually. We have implemented Traceable AI to discover all APIs, monitor traffic in real time, detect security threats, and identify vulnerabilities such as broken authentications, SQL injection attempts, bot attacks, and API abuses. For example, when customers log in to an API, we ensure it is threat-based and SQL injection is taken care of properly. We have provided authentication techniques so it cannot be broken easily and we are detecting threats through bots. We are also providing shadow API detection. These capabilities are really helpful for security. Other use cases include token abuse detection, bot detection, and XSS script protection. We are also providing broken authentication detection and whenever needed we can do integration with Kubernetes and AKS as well as various microservices applications. These are the primary areas where we are focusing. Traceable AI has provided reduced security risk, improved operational efficiency, and automated API monitoring. Before implementing it, our security team spent significant time manually reviewing logs and investigating API-related incidents. After implementing Traceable AI, we gained automated API discovery, real-time threat detection, and faster incident response. These improvements reduced manual effort, improved developer productivity, and lowered the risk of security incidents that could have financial and reputation impact.
API Security is essential for safeguarding data exchanged through APIs, preventing breaches, and ensuring compliance.APIs have become a backbone for modern applications, enabling seamless integration and interaction between software systems. Ensuring their security is crucial to protect sensitive data from unauthorized access, attack vectors, and potential breaches. With APIs increasingly used across various platforms, enterprises must prioritize robust security measures to mitigate risks....
Traceable AI is implemented for integration and API security where we can use various benefits. We are building .NET microservices exposing REST APIs to web and mobile clients. As the number of APIs increased, it became difficult to monitor them manually. We have implemented Traceable AI to discover all APIs, monitor traffic in real time, detect security threats, and identify vulnerabilities such as broken authentications, SQL injection attempts, bot attacks, and API abuses. For example, when customers log in to an API, we ensure it is threat-based and SQL injection is taken care of properly. We have provided authentication techniques so it cannot be broken easily and we are detecting threats through bots. We are also providing shadow API detection. These capabilities are really helpful for security. Other use cases include token abuse detection, bot detection, and XSS script protection. We are also providing broken authentication detection and whenever needed we can do integration with Kubernetes and AKS as well as various microservices applications. These are the primary areas where we are focusing. Traceable AI has provided reduced security risk, improved operational efficiency, and automated API monitoring. Before implementing it, our security team spent significant time manually reviewing logs and investigating API-related incidents. After implementing Traceable AI, we gained automated API discovery, real-time threat detection, and faster incident response. These improvements reduced manual effort, improved developer productivity, and lowered the risk of security incidents that could have financial and reputation impact.