Prevent data leaks from LLMs by enforcing company AI usage policies

Executive Summary

Employees increasingly use public LLMs for day-to-day work, creating a real and rising risk of accidental exfiltration of IP and client data; security, compliance and procurement teams at SMBs and enterprises are left to prove controls and avoid regulatory or contractual penalties. Existing DLPs and governance tools are not designed for prompt-level telemetry, so many organizations have a visibility and enforcement gap. Build a SaaS policy enforcement layer that intercepts and classifies LLM inputs and outputs across endpoints, web apps and integrations, applying rules (block, redact, route to private model) and generating tamper-evident audit logs for auditors and procurement. Integrations with DLPs, CASBs and SIEMs plus a programmable policy engine and agentless deployment would keep friction low and target a $3K ACV for mid-market customers. The timing is strong: a $6.0B addressable market (2M businesses × $3K ACV), an 88/100 market score, and rising regulatory scrutiny mean buyers are more willing to pay for provable AI governance today. You can differentiate by focusing on cross-tool enforcement and provable, explainable logs that legacy DLPs don’t capture, while emphasizing ease of deployment; challenges include keeping up with evasion techniques, integrating with a proliferating set of LLMs, and a 12–18 month enterprise sales/process to reach maturity.