Lagrange Labs Joins Lockheed Martin’s Supplier Ecosystem to Advance AI for Aerospace and Defense

Lagrange Labs is now a registered supplier within Lockheed Martin’s vendor ecosystem, positioning Lagrange to support AI assurance and Zero Trust data-integrity initiatives across aerospace and defense programs. As one of the United States’ largest defense contractors responsible for platforms including the F-35 Lightning II, F-16 Fighting Falcon, and C-130 Hercules, Lockheed Martin represents the technological backbone of American airpower and multi-domain operations.

As defense and aerospace systems increasingly incorporate machine learning for avionics support, mission planning, and ISR analysis, the need for technologies that can verify AI behavior is growing rapidly. Lagrange’s DeepProve addresses this challenge by providing cryptographic proofs that accompany AI model outputs, enabling a higher degree of confidence in the correctness and integrity of automated decision-making.

Provable Reliability for Mission-Critical Defense Systems

DeepProve, Lagrange’s zero-knowledge proof framework for AI assurance, brings cryptographic integrity to the decision-making processes embedded inside modern defense platforms. Rather than trusting that an AI model produced the right output—or relying on manual validation after the fact—DeepProve attaches a cryptographic proof to each model inference.

This enables a new class of AI-driven systems that can prove:

• The model operated on authorized, unaltered input data
• The output followed approved decision boundaries and safety constraints
• The full inference path is reproducible and auditable
• No parameters, telemetry, or sensitive inputs were exposed

For avionics, mission planning, and ISR fusion, this level of assurance is no longer optional. As AI becomes embedded into flight-critical and operator-in-the-loop workflows, the ability to verify correctness at machine speed becomes a national-security requirement.

Strengthening Certification, Safety, and Auditability

Defense programs must undergo rigorous safety review, verification, and accreditation. DeepProve generates tamper-evident, reproducible records for each inference, enabling certifiers and auditors to confirm that:

1. Models executed exactly as intended
2. Constraints and rules of engagement were respected
3. Inputs and outputs remained consistent with program-authorized logic

These proofs preserve model confidentiality and operational security while giving oversight teams the ability to validate correctness without intrusive data access.

Coalition-Ready AI Without Exposing Classified Data

Coalition operations, whether in NATO, joint task forces, or multi-agency intelligence missions, require coordination without revealing sensitive data or proprietary models. DeepProve enables this by allowing partners to exchange validated outcomes, not raw telemetry. A cryptographic proof confirms that a) the partner system’s result is authentic, b) the underlying mission data was not tampered with, and c) the inference followed an approved model and logic path. This allows for interoperability and trust across domains while maintaining strict data-sovereignty boundaries.

‍Lifecycle Traceability From Development to Deployment

DeepProve maintains traceability from model creation through field deployment. Proofs verify correctness during testing, bind to outputs generated in live environments, and provide reproducible evidence for after-action review. This unified approach ensures that AI-driven systems maintain verifiable integrity throughout their operational lifespan.

Advancing Accountable Autonomy

The future of aerospace and defense will not be defined only by faster or smarter autonomous systems, but by accountable autonomy: systems that perform, and systems that can prove they performed correctly. Lagrange’s work with Lockheed Martin aims to strengthen a new standard for AI in national security: systems that deliver performance with proof. DeepProve provides the cryptographic foundation required for trustworthy AI in environments where operational integrity and accuracy must be engineered, not assumed.