While policymakers debate AI ethics and researchers chase alignment breakthroughs, defense contractors face a more immediate crisis: they cannot mathematically prove their AI systems work correctly when lives depend on them.
This isn’t theoretical. AI already powers target identification, threat assessment, and autonomous navigation in military applications. These systems make split-second decisions in contested environments where adversaries actively attempt to manipulate inputs and exploit vulnerabilities. Yet most defense AI generates outputs without cryptographic verification of correct execution.
The consequences of this verification gap extend beyond individual system failures. In modern warfare, the ability to trust your AI systems while denying adversaries that same confidence becomes a strategic advantage. This creates what I call the AI Defense Transparency Paradox: how do you verify AI correctness without revealing operational capabilities?
Why Traditional AI Safety Fails Military Requirements
The AI safety approaches dominating civilian discourse collapse under military constraints. Explainable AI, while useful for understanding model behavior, reveals architectural details and decision logic that adversaries can exploit. Regulatory compliance frameworks provide process validation but not mathematical certainty. Model interpretability techniques require exposing internal states that compromise operational security.
Defense applications demand a fundamentally different approach. When an AI system identifies a potential threat or navigates through contested airspace, statistical confidence intervals aren’t sufficient. Military operators need cryptographic proof that the correct model processed accurate data and generated unaltered, tamper-proof outputs, all without revealing how the system works.
This verification challenge intensifies as AI systems become more sophisticated. The defense industry increasingly deploys transformer-based models and large language models for intelligence analysis, mission planning, and real-time decision support. These complex architectures make traditional verification methods even less practical while raising the stakes of undetected failures.
The Cryptographic Verification Solution
Zero-knowledge proofs offer a path forward. This cryptographic technique enables mathematical verification that computations are executed correctly without revealing the underlying data, model architecture, or intermediate states. For defense AI, this means proving system integrity while maintaining operational security.
The technology has matured rapidly. At Lagrange, we recently achieved a breakthrough by proving a full inference for Google’s Gemma 3 model, a modern transformer architecture with 2 billion parameters. This represents a major step toward verifiable AI at production scale, demonstrating that cryptographic verification can handle the complexity of real-world models used in defense applications.
The implications extend beyond technical feasibility. Cryptographic verification enables several critical capabilities for defense AI.
First, it provides mathematical certainty that approved models are running in production systems. This prevents unauthorized model substitutions or modifications that could compromise mission effectiveness or introduce vulnerabilities.
Second, it verifies data integrity throughout the inference pipeline. Adversaries cannot manipulate inputs or tamper with outputs without detection, even when systems operate in contested electromagnetic environments.
Third, it enables auditability without compromising operational security. Defense organizations can prove to oversight bodies that AI systems function within defined parameters without revealing classified model details or operational data.
The Geopolitical Verification Arms Race
The strategic value of verifiable AI becomes clear when examining historical parallels. During the Cold War, the ability to verify treaty compliance while protecting sensitive capabilities shaped superpower dynamics. Nuclear verification protocols balanced transparency requirements with national security imperatives.
AI verification presents a similar challenge with higher stakes. Unlike nuclear arsenals with observable physical signatures, AI capabilities remain largely invisible until deployed. Nations that master cryptographic verification will demonstrate trustworthy AI systems to allies while denying adversaries insight into operational parameters.
This dynamic already influences defense procurement. Organizations working with our team at Lagrange understand that verifiable AI isn’t a future requirement but a current necessity. The defense contractors integrating cryptographic verification today are building strategic advantages that will compound over time.
China and other nations invest aggressively in both AI capabilities and the cryptographic infrastructure to verify them. The U.S. risks falling behind without coordinated efforts to maintain leadership in this domain. The Department of Defense’s SIEVE program recognizes this challenge, but private sector innovation in zero-knowledge proofs has outpaced government initiatives.
The Window for Action
Defense contractors face a choice. They can continue deploying unverifiable AI systems and accept the strategic vulnerabilities this creates. Or they can demand cryptographic verification as a baseline requirement for mission-critical applications.
The technical barriers have fallen. Systems like DeepProve demonstrate that verifiable AI works at production scale with performance approaching real-time requirements. The remaining obstacle is institutional inertia and the perception that verification adds unacceptable overhead.
That perception is wrong. The cost of integrating verification now is minimal compared to retrofitting existing systems later or accepting the consequences of unverifiable AI in contested environments. Every defense AI deployment without cryptographic verification is a strategic liability.
The countries and organizations that recognize this reality first will control the next phase of AI-enabled warfare.
The verification arms race has begun. The question is whether the defense industry will lead or follow.
Ismael Hishon-Rezaizadeh is the Co-founder and CEO of Lagrange, a company focused on bringing trust and safety to our AI with zk proofs. With a background in AI and blockchain, he led John Hancock’s crypto practice at just 21, developing decentralized insurance and reinsurance solutions. After transitioning to venture capital, Ismael honed his skills in identifying market opportunities, but his focus on building decentralized technologies led him to found Lagrange. As CEO, he focuses on growth, business development, partnerships, and setting the strategic direction while staying closely connected to the market and customers.
