# Why AI Governance Is Full Data Path Governance
You can't govern what you can't see. 95% of AI initiatives go nowhere — not because the models are wrong, but because the infrastructure underneath them is ungoverned, fragmented, and flying blind.
Enterprise AI is moving fast. But speed alone is a fast way to fail.
Two AI workloads are reshaping enterprise infrastructure right now — GenAI and Agentic AI — and both bring a completely different set of connectivity, cost, and governance challenges. Custom LLM wiring is slowing teams down. Uncapped token consumption is quietly destroying margins. Agents acting autonomously without guardrails are creating cascading failures. And 86% of organizations are completely blind to their AI data flows.
The root cause isn't the models. It's fragmentation. Governance is scattered across API gateways, agent frameworks, MCP routers, event pipelines, and context stores — each with its own policies, visibility, and failure modes. None of them talk to each other. None of them give you the full picture.
In this session, we'll break down what full data path governance actually means — and what it takes to unify it across every layer of the stack.
**We'll cover:**
- - **The two AI workloads reshaping enterprise infra** — and why GenAI and Agentic AI demand entirely different governance models
- - **Why fragmentation is the governance killer** — how siloed tools, teams, and context stores make production AI impossible to manage at scale
- - **The full AI data path** — from APIs and events to LLMs, agents, context, and memory — and every control point in between
- - **Cost, speed, and risk at every layer** — token consumption, PII exposure, prompt injection, and the margins math that makes ungoverned AI unsustainable
- - **A unified AI control tower** — how to bring LLM governance, MCP and agent governance, API management, and context management under one platform
