Lagrange Labs is now a registered supplier within @LockheedMartin's vendor ecosystem, positioning Lagrange to support AI assurance and Zero Trust data-integrity initiatives across aerospace and defense programs. Learn more: 🧵

2/ Lockheed Martin, responsible for the F-35 Lightning II, F-16 Fighting Falcon, and C-130 Hercules, increasingly relies on machine learning across avionics, mission planning, and ISR analysis. As AI becomes embedded in these systems, the ability to verify AI behavior is now mission-critical.

3/ Lagrange’s DeepProve attaches a cryptographic proof to every AI model output. This replaces assumption with certainty. DeepProve proves that: • The model used authorized, unaltered inputs • Outputs followed approved safety boundaries • The inference path is reproducible and auditable • No parameters or sensitive telemetry were exposed

4/ This unlocks new possibilities for AI assurance and Zero Trust data-integrity: • Avionics & Flight-Critical Systems: For avionics and operator-in-the-loop workflows, correctness must be verified at machine speed. DeepProve ensures every model action can be proven, not guessed. • Mission Planning: Mission-planning tools must show that outputs were produced using approved logic and constraints. DeepProve generates tamper-evident records that strengthen certification, verification, and accreditation. • ISR Fusion: ISR systems depend on sensitive, multi-source data. DeepProve preserves confidentiality while proving the output was produced correctly, critical for intelligence workflows. • Coalition Operations: Coalition missions require trust without sharing classified telemetry. DeepProve enables partners to exchange validated outcomes, not raw data, maintaining security while enabling interoperability. • After-Action Review & Lifecycle Traceability: DeepProve verifies correctness during testing, binds proofs to outputs in deployment, and provides reproducible evidence for after-action review. This creates continuous operational integrity from development → deployment → audit.