Question 1

What are the main challenges of running AI at the edge?

Accepted Answer

Key challenges include managing decentralized infrastructure, handling varied data formats and versions, and ensuring data lineage and provenance. Reliable connectivity is also a critical hurdle when trying to bring edge-generated data back to centralized systems.

Question 2

How is AI being applied at the edge in government and military use cases?

Accepted Answer

Edge AI supports surveillance, sonar detection, and maintenance automation by enabling real-time tagging and filtering of data where it’s generated, even with intermittent connectivity. It allows for local training and decision-making to improve responsiveness in mission-critical scenarios.

Question 3

What is the SENA framework and how does it secure edge environments?

Accepted Answer

SENA (Secure Edge Native Architecture) is a reference architecture that addresses key security concerns at the edge, including software vulnerabilities, hardware tampering, and data protection. It supports compliance with government directives such as the AI Executive Order.

Question 4

How do open source technologies like Kubernetes and ChatGPT accelerate edge AI innovation?

Accepted Answer

Kubernetes has become the standard for managing infrastructure at the edge, while open-source LLM interfaces like ChatGPT's API have democratized access to AI. Together, they enable faster development, broader adoption, and interoperable solutions across agencies and vendors.

Question 5

What is continuous Authority to Operate (cATO) and why is it important?

Accepted Answer

cATO allows systems to remain secure and compliant without bureaucratic delays, by automating security validation and enabling rapid software updates in dynamic edge environments—essential for AI systems operating in disconnected or sensitive settings.

Question 6

How are Spectro Cloud and Hitachi Vantara Federal working together on edge AI?

Accepted Answer

Spectro Cloud provides the infrastructure and lifecycle management platform for Kubernetes at the edge, while Hitachi Vantara Federal’s Pentaho supports data discovery, quality, and operations. Together, they enable robust, secure, and performant AI/ML solutions for government missions.

Question 7

How does Palette help manage diverse edge hardware securely and efficiently?

Accepted Answer

Palette uses Cluster Profiles to model and deploy full-stack Kubernetes environments tailored to specific hardware setups. It supports disconnected operations, secure bootstrapping, and updates via Kairos, an immutable infrastructure upgrade system with rollback capabilities.

Advancing AI at the Edge to Transform Missions