Goal: Address the unique engineering and policy challenges and opportunities of implementing artificial intelligence at the point of mission critical data collection and analysis, which is essential for government operations in remote or bandwidth-constrained environments. Become the epicenter of state of the art and best or facets in edge AI for the Defense Sector worldwide.

Areas of focus for the Defense Working Group (DWG) include both raising the profile/awareness of edge AI in policy efforts AND tackling the technology ecosystem challenges inherent in “mission critical” edge AI.

The DWG will start with a US focus but many of these elements would apply to NATO, FVEY, and other aligned nation efforts.

Membership

• Focus on EDGE AI FOUNDATION strategic partners
• Think tanks driving policy 
• Research Universities
• Relevant partner orgs, e.g. NATO DIANA

Outcomes

• White papers/best practices
• Livestream/online programs
• In person events (annual event in DC)
• Educational programs (e.g. Edge AI Fundamentals online)

Tactical and security applications

• Defense and national security: Enabling real-time threat detection, battlefield intelligence, and autonomous systems by processing sensor data directly on-device. This reduces latency and improves operational security in contested environments.
• Border and transportation security: Enhancing security screenings with immediate AI analysis of X-rays and other sensor data at ports of entry, empowering personnel with real-time insights.
• Surveillance and public safety: Improving situational awareness for law enforcement and emergency responders by processing video and sensor data locally for facial recognition, anomaly detection, and rapid response coordination. 

Infrastructure and operational efficiency

• Smart infrastructure: Optimizing the management of critical assets like power grids, military vehicles, and smart city technology through AI-powered predictive maintenance and real-time monitoring.
• Disaster response: Using edge AI to process satellite imagery and sensor data to assess damage and allocate resources efficiently during an emergency, even when network connectivity is limited.
• Energy innovation: Developing next-generation AI models for federal energy technology by harnessing scientific data from national laboratories. 

Policy, ethics, and security

• Security and privacy: Addressing the new security risks introduced by a distributed network of edge devices. This would involve developing strategies for data/model encryption, endpoint security, and implementing zero-trust security models.
• Governance and ethics: Establishing transparent policies and protocols for AI systems to ensure accountability and avoid bias. This includes defining clear roles for data management, monitoring outcomes, and implementing review boards to prevent unintended harm.
• Regulatory frameworks: Creating and supporting a legislative framework that supports AI and edge technology development without excessive regulation. The group could help shape policy that encourages responsible risk-taking and public-private partnerships. 

Implementation challenges

• Bridging research and development: Reducing the gap between AI research and real-world deployment. This would involve creating tools and resources that enable researchers to demonstrate their work effectively for government adoption.
• Workforce training: Addressing the need for public and private sector employees to gain the skills necessary to use and manage edge AI solutions effectively.
• Standards: Supporting the development of industry-led best-practices for interoperability and supply chain resiliency for edge AI solutions.

Adoption and scale: Identifying clear and compelling use cases to start small and demonstrate success. The group would develop strategies for scaling up effective AI projects across agencies to create a centralized resource pool.