Salt Security: Detecting Threats, Anomalies & Vulnerabilities in API Traffic with AI

In a strategic move to amplify their operational efficiency, Salt Security collaborated with Qwak. Recognizing the burgeoning needs of Salt's data science team, Qwak stepped in to transition them towards self-sufficiency, minimizing their reliance on DevOps and engineering units. This wasn't just about streamlining processes; it was about empowering the team to scale their model deployment seamlessly, all while maintaining engineering resource stability. This integration signified not only technological advancement but also an organizational evolution towards agility and autonomy.

About Salt

Salt Security delivers end-to-end protection for APIs throughout build, deploy, and runtime phases by combining comprehensive coverage with an ML/AI-driven big data engine. The platform not only stops attackers in the early stages of an attempted attack but also enhances API security posture by discovering all APIs, preventing unauthorized access, and offering remediation insights for development teams.


Cyber Security, Software/Technology

Use Case

LSTM Network Model

Model Frameworks

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We had the data and we solved the problem. Qwak allowed our data science teams to deliver the models into production with ease and efficiency


Model Monitoring

Streaming Inference

By Design


Engineering Dependency


With its accelerated growth, Salt Security expanded its Data Science team, leading to the generation of advanced ML models. However, transitioning these models to production presented multiple challenges:

  • The AWS SageMaker framework demanded consistent involvement from the Infrastructure team for each new model deployment.
  • Preliminary PoC experimentation often necessitated the expertise of a seasoned engineer.
  • The data science teams, skilled in their field, lacked the engineering experience crucial for deployment.
  • Integrating with Kafka's event-based architecture was intricate, especially when endpoints needed to manage and output prediction streams.



Uniformity via Model Deployment: Introduced a standard code structure for all models, enabling Salt to systematically deploy them to production.

Clarity with Qwak's Interface: Through its streamlined terminology and interface, Qwak simplified the complexities of the ML infrastructure. The addition of a robust API ensured easy access to all functionalities.

Real-time Assessment with Model Monitoring: These tools, combined with feedback loops, allowed Salt's teams to promptly evaluate model behavior after deployment.

Integration Efficiency with Model Serving: Facilitated the deployment of model endpoints integrated with Kafka. This allowed Salt's personnel to deploy new model versions as endpoints adept at handling prediction streams.

The partnership with Qwak enhanced Salt Security's deployment efficiency, addressing key challenges and streamlining processes.