Enforce zero-trust & human oversight for agentic AI systems

Enterprises building agentic AI—LLM-driven automation that composes tools, executes actions, and queries external systems—are facing a new class of operational and compliance risks: unauthorized data exfiltration, runaway actions, and invisible policy drift during runtime. This problem is concentrated in mid-to-large organizations (the addressable set we estimate at ~100,000 enterprises globally) that currently lack standardized runtime controls and human-in-the-loop enforcement for autonomous chains. You could build a cloud-native SaaS platform that enforces zero-trust controls and mandatory human oversight across agentic workflows: low-latency policy agents that intercept tool calls, fine-grained capability tokens, human-gating and approval UIs, immutable audit trails, and out-of-the-box compliance templates mapped to CISA guidance and emerging EU rules. Targeting an average contract value of ~$120K ACV yields a $12.0B TAM and aligns with the market score (92/100) and revenue potential (88/100) noted for AI governance solutions today. The timing is compelling because regulators (CISA, EU regulators) are publishing guidance that will drive procurement, agentic automation is expanding attack surface, and cloud vendors now expose telemetry that makes centralized enforcement practical. To stand out you must deliver runtime enforcement (not just post-hoc detection), deep cloud integrations, and validated human-oversight workflows with measurable SLOs and compliance attestations—differentiators relative to medium-competition offerings that focus on policy libraries or model monitoring. Strengths include clear product-market fit and predictable ACV economics; realistic challenges are integration complexity across diverse toolchains, long enterprise sales cycles, and the need to continuously adapt to evolving regulations and adversarial behaviors.