In complex security environments, relying on a single analytical framework can create blind spots and limit threat detection capabilities. The deployment of multiple inference models provides a more robust and nuanced approach to data analysis. By leveraging a variety of specialized models, organizations can achieve higher accuracy, greater flexibility, and more comprehensive insights, which are critical for defending against diverse and evolving threats.
A single inference model, no matter how advanced, operates with inherent biases and limitations. A multi-model strategy overcomes this by combining the outputs of several distinct models, each trained for specific tasks or data types. This method creates a system of checks and balances, validating findings and uncovering patterns that a monolithic approach would miss. The result is a more resilient and reliable analytical engine for critical decision-making.
Improved Accuracy and Reduced False Positives:
By cross-referencing the conclusions of different models, the system can more confidently distinguish between genuine threats and benign anomalies. This ensemble approach significantly reduces the rate of false positives, allowing security teams to focus resources on credible incidents.
Enhanced Flexibility and Specialization:
Different models excel at different tasks. Our platform utilizes specialized models for various functions—such as natural language processing for phishing detection, image analysis for deepfake identification, and behavioral analytics for insider threats. This ensures the right tool is always used for the job.
Greater Resilience to Adversarial Attacks:
Threat actors can learn to evade a single, static detection model. A multi-model architecture is inherently more difficult to circumvent, as an attacker would need to devise a method to fool multiple, independent analytical systems simultaneously.
Scalability for Evolving Data Landscapes:
As new data sources and communication channels are introduced, new inference models can be seamlessly integrated into the platform without disrupting existing operations. This ensures your security infrastructure remains effective as your organization grows and technology evolves.
Our platform orchestrates a suite of inference models that work in concert to analyze data from all communication streams. This integrated process provides a holistic view of potential risks, enabling more effective and proactive security measures.
More importantly, Netarx uses proprietary techniques and technology to tune the outputs of these models as needed for each use case…providing the most accurate detection possible.
Model Orchestration Engine:
At the core of the system is an engine that directs incoming data to the most appropriate inference models. This engine intelligently routes information based on its type, source, and context for optimal analysis.
Specialized Analytical Units:
The platform includes a library of pre-trained and custom-trainable models. Each is optimized for a specific analytical function, such as:
Behavioral Analysis:
Models that establish and monitor baselines for user and entity behavior to detect anomalies indicative of a compromise.
Content Classification:
Models that analyze text, images, and video to identify malicious content, including phishing attempts and synthetic media.
Threat Correlation:
Models that identify relationships between seemingly disparate, low-level alerts to uncover sophisticated, multi-stage attack campaigns.
Consensus and Scoring Mechanism:
The outputs from individual models are fed into a consensus module. This component weighs the findings based on model confidence and other contextual factors to generate a single, actionable risk score for each event.
Ensemble AI:
Ensemble AI (when multiple models “vote,” “average,” or “stack” their outputs to make a final decision) is leveraged. Netarx is the only system to leverage different ensemble AI based on the conditions. It’s like consulting several experts — instead of relying on one model’s biases or blind spots, you aggregate multiple viewpoints to reach a more reliable conclusion.
Continuous Model Refinement:
The system employs feedback loops to continuously retrain and update the models based on new threat intelligence and incident response outcomes. This ensures the platform adapts to emerging threats and maintains peak analytical performance.
