Secure model control planes — continuous audits + runtime enforcement

Enterprises that build and serve ML models increasingly treat models as critical control planes, yet most lack continuous auditability and runtime enforcement for model behavior, provenance, and access. This problem is acute in roughly 100,000 organizations running private or self-hosted models—particularly in finance, healthcare, regulated SaaS, and large enterprises—where a single compromise can cause regulatory fines, data exfiltration, or major operational disruption. You could build a secure model control plane that combines continuous cryptographic attestation of artifacts, tamper-evident audit trails, and low-latency runtime policy enforcement, exposed through policy-as-code and turnkey compliance reporting for both ML and security teams. The product would include provenance hooks into training and inference stacks, agents (and agentless options) for inference servers and GPU infra, and integrations with SSO, SIEM, and ticketing systems to shorten time-to-audit. The market conditions are favorable: a $12.0B addressable market (100,000 enterprises × $120,000 ACV), a market score of 95/100, revenue potential 94/100, and accelerating drivers—self-hosted LLMs, NIST/EU AI Act requirements, and the shift-left security trend—that directly map to continuous MCP auditing needs. You can differentiate by optimizing for enterprise constraints—guaranteed low-latency enforcement paths, formal cryptographic attestations, certified compliance profiles for NIST/EU AI Act, and an extensible policy framework that reduces alert fatigue for SREs and security teams. Be honest about the challenges: competition is medium, sales cycles will be long, integrations are complex, and regulatory guidance will continue to evolve; success hinges on proving measurable risk reduction and shortening audit timelines from months to days to justify a $120k ACV to cautious buyers.