SPACE EDGE AI

 

Wallaroo.ai has an existing relationship with the U.S. Space Force and its innovation arm, SpaceWERX. Wallaroo.ai has been selected for programs and partnerships to explore and demonstrate the use of its platform for various space-related applications, particularly in the realm of "edge AI."

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Here's how Wallaroo.ai's capabilities could be extended to the U.S. Space Force, Space Systems Command (SSC), and Space Operations Command (SpOC):

1. Space Systems Command (SSC)

SSC is responsible for acquiring, developing, and delivering resilient capabilities for the U.S. Space Force. Wallaroo.ai's platform, which is designed to deploy and manage AI models at scale, could be used to:

• Integrate AI into new systems: As SSC develops and acquires new satellites and ground systems, Wallaroo.ai could provide the "last-mile" solution for operationalizing machine learning models on those platforms. This includes ensuring models run efficiently on specialized, low-power hardware in space.

• Enhance Space Domain Awareness (SDA): Wallaroo.ai has already been involved in the USSF SDA TAP Lab Apollo Accelerator program. This partnership could be expanded to a broader range of SDA applications, such as identifying and tracking space debris, monitoring adversary movements, and analyzing satellite imagery for actionable intelligence.

• Improve logistics and maintenance: AI can be used to optimize logistics and predictive maintenance for space assets. Wallaroo.ai's platform could help SSC deploy models to analyze data from satellites to predict potential failures, optimize supply chains, and streamline maintenance operations.

2. Space Operations Command (SpOC)

SpOC is the field command responsible for generating, presenting, and sustaining combat-ready forces. Their mission involves space operations, cyber operations, and intelligence. Wallaroo.ai's edge AI capabilities could be particularly valuable for SpOC in the following areas:

• On-orbit processing for real-time insights: Instead of downlinking massive amounts of raw data from satellites, which is slow and bandwidth-intensive, Wallaroo.ai's platform can enable AI models to process data directly on the satellite. This allows SpOC to receive only critical, pre-filtered insights in near real-time, enabling faster decision-making for orbital warfare, missile warning, and other time-sensitive missions.

• Space Electronic Warfare: AI models could be deployed at the edge to analyze and react to electronic signals in real-time, helping SpOC to identify and counter jamming or spoofing attempts.

• Cyber Operations: Wallaroo.ai's ability to run models in secure, demanding environments could be applied to cybersecurity within space and ground systems, enabling rapid detection and response to cyber threats.

3. U.S. Space Force (USSF) as a whole

The U.S. Space Force's mission is to organize, train, and equip forces to conduct global space operations. Wallaroo.ai can help the entire USSF ecosystem by:

• Simplifying AI deployment: Wallaroo.ai's platform is designed to streamline the process of deploying and managing AI models, reducing the engineering time and costs associated with operationalizing AI. This could help the USSF rapidly integrate new AI-powered capabilities across its various commands and units.

• Ensuring security and compliance: The platform's features for observability, security, and governance are crucial for military applications. Wallaroo.ai's ability to monitor live models, detect drift, and provide audit logs would support the USSF's need for secure and reliable AI systems.

• Building a talent pipeline: By making it easier to operationalize AI, Wallaroo.ai can help the USSF and its partners leverage a broader pool of talent, including data scientists and engineers, to develop and deploy cutting-edge capabilities.

In summary, Wallaroo.ai's existing work with the USSF, particularly in on-orbit and edge AI, demonstrates a clear potential for expanding its use to a wider range of applications within Space Systems Command and Space Operations Command to enhance military effectiveness and decision-making in the space domain.

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.