Idea
A secure AI governance platform that enforces tamper-proof compliance to protect high-risk AI systems from compromise.
Research Paper
Core Innovation
This paper introduces the Governable AI framework that uses cryptographic enforcement and a secure super-platform to guarantee AI compliance and prevent tampering. Unlike prior work, it provides formal security proofs and ensures non-bypassability under extreme threat models. The approach integrates a rule enforcement module with governance rules to maintain AI safety externally and structurally.
Market Size (TAM)
$2–10B TAM, $1–2B SAM; assumption: Growing demand for secure AI governance in regulated industries and critical infrastructure.
Potential Customers & Pain Points
- Enterprises deploying high-stakes AI systems needing provable safety
- Government agencies requiring secure AI governance
- AI developers facing risks of AI subversion or tampering
Business Model
Subscription-based platform licensing with tiered pricing for enterprises and government clients; consulting for integration and compliance auditing.
Competitive Landscape
- OpenAI Safety Team
- Anthropic
- Conjecture
Implementation Challenges
- Complexity of integrating cryptographic enforcement with AI models
- High cost of deploying secure super-platforms
- Regulatory acceptance and standardization challenges
Validation Strategy
- Develop full prototype integrating REM and GSSP modules
- Pilot deployment with select enterprise partners in regulated sectors
- Conduct formal security audits and publish validation results
Research Paper Overview
Governable AI: Provable Safety Under Extreme Threat Models
Summary
This paper proposes a Governable AI (GAI) framework that ensures AI safety under extreme threat models by enforcing externally controlled structural compliance using cryptographic mechanisms. The framework includes a rule enforcement module (REM), governance rules, and a secure super-platform (GSSP) that guarantees tamper-resistance and non-bypassability, preventing AI compromise or subversion. The approach is formally proven secure and validated through prototype testing in high-stakes scenarios.