Wallaroo.ai's orchestration could integrate with "Cyberspatial Teleseer"

 

Review - Wallaroo.ai's orchestration could integrate with "Cyberspatial Teleseer" for applications within the U.S. Space Force (USSF).

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1. Understanding the Technologies

• Wallaroo.ai: A production AI and machine learning platform that specializes in deploying, managing, and observing AI models at scale.¹ Its orchestration is configured using human-readable YAML (.yml) files, which define the end-to-end AI pipeline from data ingestion to inference and output. Wallaroo.ai has a direct relationship with the USSF, having been selected for the SDA TAP Lab Apollo Accelerator program and having worked on "on-orbit AI" solutions.²

• Cyberspatial Teleseer: This is a real-world network visibility and analysis tool.³ It uses next-gen packet analysis to map, discover, and visualize network assets and traffic, providing a "visual intelligence layer" for IT and security teams.⁴ Cyberspatial has also worked with the Department of the Air Force to map "Mission Relevant Terrain in Cyberspace," which is a key concept for both the Air Force and the Space Force.⁵

2. The Integration and Value Proposition for the USSF

The integration would combine the strengths of both platforms to create a powerful, automated system for space domain awareness (SDA) and cybersecurity. The USSF needs to process vast amounts of data—both from physical space and from its own networks—and AI is essential for this.⁶

The core of the integration lies in using Wallaroo.ai's orchestration to consume data and insights from Cyberspatial Teleseer and then run AI models on that data to identify threats, anomalies, or actionable intelligence.

Hypothetical Integration Workflow:

1. Data Ingestion (Teleseer): Cyberspatial Teleseer continuously analyzes network traffic from the USSF's ground-based or space-based networks.⁷ It provides real-time visibility and a "knowledge graph" of network assets and their connections.⁸ This data can include threat insights, traffic anomalies, and passive asset discovery.⁹

2. Wallaroo.ai Orchestration (YML Files): A Wallaroo.ai pipeline would be defined in a .yml file. This file would specify a series of automated steps:

   • Data Connector: A custom connector would be defined to pull data and insights directly from the Teleseer API. This bypasses manual data export, ensuring the AI model is working with the most current information.

   • Pre-processing: The orchestration would perform any necessary pre-processing on the Teleseer data.¹⁰ This might involve structuring the data for the AI model, enriching it with other context, or preparing it for inference.

   • Inference: The data would be fed into a trained AI model deployed by Wallaroo.ai. This model could be designed to:

     • Identify Cyber Threats: Detect sophisticated cyberattacks or insider threats based on network anomalies identified by Teleseer.¹¹

     • Predict Vulnerabilities: Analyze the network topology and flag potential vulnerabilities before they are exploited.

     • Automate Responses: In a fully automated loop, the model could recommend or even initiate actions based on a detected threat.

   • Output and Alerting: The results of the inference would be outputted. Wallaroo.ai's observability features would monitor the model's performance and output, triggering alerts if a high-priority threat is detected.¹²

3. Why This Integration is Valuable to the USSF

This type of integration directly addresses several key USSF strategic priorities:

• Enhanced Space Domain Awareness (SDA): The USSF needs to maintain superiority in space, and this includes defending its own cyber assets. By combining network visibility with AI-driven threat analysis, the USSF can better protect its command and control systems from both state and non-state actors.

• Operational Efficiency: Automating the data-to-decision loop with Wallaroo.ai's YML-driven pipelines reduces the manual effort required from Guardians. This is critical in an organization where data volume is immense and the ratio of Guardians to satellites is constantly increasing.

• Speed and Agility: The ability to rapidly deploy new AI models to address emerging threats is essential. The .yml file approach makes it easy to update or swap out models in the pipeline, allowing the USSF to adapt its defensive posture quickly.