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Executive Summary

Enterprises running LLMs in production face a clear gap between static guardrails and dynamic runtime behavior: models and agents executed in browsers, mobile clients, plugins, and edge containers can bypass server-side filters, causing data leakage, policy violations, and compliance risks. This affects platform engineering teams, ML risk and compliance officers, security teams and CISOs at the largest 100,000 enterprises that are estimated to allocate roughly $18B to comprehensive LLM governance (about $180K ACV each), and a single audit failure or exploit can cost millions in regulatory fines and reputational damage. You could build a runtime-aware LLM safety platform that instruments models and agents across environments with lightweight SDKs and attestations, correlates telemetry into a unified policy engine, and provides provable execution traces, anomaly detection, and real-time mitigations (block, rollback, or sanitization) tailored per runtime. Key components would include pluggable runtime hooks for browser/mobile/WASM and server, a high-throughput policy evaluator with deterministic fallbacks, cryptographic attestations for tamper evidence, and audit-ready logs designed to satisfy regulators and internal auditors. Market conditions make this attractive now: the EU AI Act and sector-specific guidance are raising auditability and runtime-control requirements, decentralized runtimes materially increase attack surface, and modern observability tooling has lowered the cost of cross-runtime monitoring and anomaly detection. With an addressable TAM of $18B, a market score of 95/100 and revenue potential of 94/100, enterprises are primed to pay in the $100K–$250K ACV bracket for solutions that demonstrably reduce incident risk and meet regulatory needs. To stand out you must emphasize verifiable runtime enforcement and minimal-friction integrations—deliver client-side SDKs and cryptographic attestations that produce audit-grade traces, pair deterministic fallback policies to reduce false positives, and integrate tightly with model vendors and MLOps platforms—while being upfront about challenges: runtime fragmentation, privacy constraints, performance/latency trade-offs, and the engineering burden of supporting heterogeneous execution environments.

Executive Summary

Runtime-aware LLM safety: verify guardrails across environments

Executive Summary

Runtime-aware LLM safety: verify guardrails across environments

Executive Summary

Market Validation

Market Opportunity

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