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Cyberace is not a builder of AI agents, but rather a provider of the security infrastructure those agents interact with. Their focus on Web Application Firewalls (WAF) and SOC optimization places them in the defense layer of the agent stack. As agents increasingly automate interactions with web interfaces, the boundary between legitimate automated traffic and malicious bot activity becomes harder to define.
Cyberace provides the tools that manage these boundaries. For those building agents, companies like Cyberace represent the gatekeepers of the target environments. For those using agents, Cyberace provides the security monitoring necessary to ensure that agentic workflows do not inadvertently create vulnerabilities or trigger security alarms that disrupt business operations.
Cyberace is a Managed Security Services Provider (MSSP) headquartered in Nairobi, Kenya. The company focuses on a specific niche within the security market: the optimization of Security Operations Centers (SOC). While many security firms offer broad, generic monitoring, Cyberace targets the operational efficiency of the security stack, aiming to provide customized assurance that aligns with the specific operating environments of its clients. This approach is built on the reality that modern organizations face a unique set of digital threats that cannot be addressed with generic, out-of-the-box configurations.
A primary component of the Cyberace offering is its Web Application Firewall (WAF). In their technical communications, the company describes the WAF as a digital bodyguard that learns and adapts to traffic patterns in real-time. This is particularly relevant in the current era of the web, where a significant portion of traffic is not human but composed of bots, scanners, and automated scripts. The Cyberace WAF is designed to distinguish between these actors, protecting applications from exploits while maintaining performance and uptime. For businesses, this means a reduction in the noise of automated attacks and a more stable environment for legitimate traffic. By using systems that learn from traffic, the company aims to move beyond static, rule-based security toward a model that can handle the evolving nature of web automation.
Operating out of Nairobi, Cyberace is part of a growing technological ecosystem in East Africa. The company serves as a bridge between high-level global security standards—such as those defined by OWASP—and the practical needs of regional enterprises. Their workforce is relatively lean, estimated between 11 and 50 employees, which suggests a high-touch service model. This size allows for the customized security services they highlight as a core differentiator from larger, more rigid global competitors. Their presence in Kenya positions them at the center of the "Silicon Savannah," where digital transformation and the accompanying security risks are accelerating rapidly.
As AI agents become more prevalent, the role of companies like Cyberace changes. Agents are, by definition, automated actors that navigate web applications to perform tasks. This behavior can often trigger security protocols designed to block traditional, malicious bots. Cyberace sits at this intersection, managing the systems that decide which automated entities are allowed to interact with a business's digital assets. Their work in SOC optimization ensures that security teams are not overwhelmed by the increasing volume of automated interactions, but instead have the visibility needed to manage a hybrid environment of human and agentic users. By focusing on adaptive firewalls, they provide the necessary infrastructure for a web where automation is the norm rather than the exception. Their focus on reducing false positives in security alerting is a critical step in making the web accessible to the next generation of automated AI tools.
An adaptive security layer that protects applications from automated threats and bots.
Non-Metric Space Library (NMSLIB): An efficient similarity search library and a toolkit for evaluation of k-NN methods for generic non-metric spaces.
Implementation of random Fourier features for kernel method, like support vector machine and Gaussian process model
Finding the conditional distributions of a Gaussian Mixture Model
Julia package for kernel functions for machine learning
Non-stationary spectral mixture kernels implemented in GPflow
Data Science in Julia course for JuliaAcademy.com, taught by Huda Nassar
Learn the language basics in this 10-part course.
Model Agnostic Confidence Estimator (MACEST) - A Python library for calibrating Machine Learning models' confidence scores
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