What is our primary use case?
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.
What is most valuable?
When we are using the platform, it provides better security and reduces breach risk. It has faster incident response and better compliance. It provides API discovery, attack detection, API inventory, and risk scoring.
We are primarily using API discovery and attack detection.
Traceable AI has had a positive impact on both security and operational efficiency. It automatically discovers APIs and monitors traffic in real time while detecting suspicious activity such as bot attacks and authentication abuse. It has reduced manual monitoring, accelerated incident response, improved compliance, and allowed developers and security teams to work more efficiently.
We evaluated a few dedicated API security platforms along with the capabilities already available in our existing infrastructure. The comparison focused on API discovery, real-time threat detection, ease of integration, scalability, and reporting. Traceable AI provided a good balance of API discovery, behavioral analytics, and enterprise integrations.
What needs improvement?
Traceable AI can be improved on various platforms. It is a strong API security platform but there are areas for improvement such as AI explanations for why a request was flagged, more out-of-the-box integrations, improved dashboards for executives, and more flexible pricing which would enhance the overall experience. For very large environments, simpler policy management and better automation for alert tuning would also be valuable.
Better AI explainability would be beneficial. Currently it detects suspicious activity, but it could be improved by explaining why the API was flagged with confidence scores and recommended remediation steps. For example, with suspicious logins, it should show details such as login rate from a new location using 500 unique usernames within 10 minutes with a confidence score of around 90 percent and indicate whether it is a false positive. Initially, AI has to generate extra alerts. It could be improved with better automatic tuning to learn application behavior faster and reduce unnecessary alerts. Current dashboards are useful but could be improved with accessible executive dashboards. There are also more integrations that could be done with CI/CD, ticketing systems, and cloud security platforms.
For how long have I used the solution?
I have been using Traceable AI for the last two years.
What do I think about the stability of the solution?
Traceable AI was stable once deployed and properly configured. It continuously monitors API traffic without affecting application runtime performance. Since it operates as an API security monitoring platform rather than serving application requests directly, it did not introduce any downtime for our customer-facing services.
What do I think about the scalability of the solution?
Traceable AI can be scaled in various environments. It can monitor thousands of APIs and process high-volume API traffic across microservices, Kubernetes clusters, and multi-cloud deployments. In our project, it scaled well as new APIs were added because API discovery and monitoring were automatic. We did not need to redesign our security approach each time a new service was deployed.
How are customer service and support?
Customer support was responsible for initial product onboarding, integration with API gateways, API discovery configurations, alert tuning to reduce false positives, product updates, new configuration troubleshooting, and connectivity issues. The support team has strengths such as responsive technical engineers, good documentation, and a knowledge base that helps implement solutions.
There are areas of improvement in the customer service offering. I would rate customer service as four out of ten.
Which solution did I use previously and why did I switch?
We have used a combination of tools such as web application firewalls, API gateways, SIEM tools, and manual log analysis to monitor API security. While other tools were effective for general network and application security, they did not provide complete visibility into API and application-specific risks. It was difficult to identify shadow APIs, detect business logic attacks, or understand API behavior in real time. We switched to Traceable AI because it is purpose-built for API security. It automatically discovers APIs, monitors API traffic continuously, detects abnormal behavior using APIs, identifies undocumented APIs, and provides detailed risk insights. This significantly reduced manual effort and improved our overall API security posture.
What other advice do I have?
Traceable AI supports enterprise workloads and can scale to large API ecosystems. However, achieving that scale required proper infrastructure planning, policy management, and ongoing tuning. It is a strong platform, but scaling is not completely effortless, which is why I would not rate it a perfect ten.
I would recommend Traceable AI to organizations that have a large number of APIs, microservices, and public-facing applications. Before implementing it, it is important to first understand your API landscape and identify which API processes contain sensitive data. Start with a pilot in development, tune the security policy based on your application's normal behavior, and then gradually expand to production. It is also important to integrate the platform with your existing SIEM, incident management, and CI/CD processes to get the maximum value. I would rate this product seven out of ten.
Which deployment model are you using for this solution?
Hybrid Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
