AliasPath is a verification-boundary security layer for GenAI and agentic systems that allows sensitive workflows to run on realistic aliased data instead of raw identities, so enterprises can preserve AI utility without surrendering data sovereignty. For a technical buyer, the key value is architectural: AliasPath reduces the blast radius of prompts, RAG pipelines, tool calls, and agent memory by ensuring the model never sees the original regulated data in the first place. AliasPath intercepts context before it reaches the model or agent runtime, including user input, retrieved documents, and tool outputs, and transforms sensitive values into plausible contextual replacements that remain semantically useful for downstream reasoning. Unlike redaction, tokenization, or simple masking, the goal is to preserve workflow utility, so a person, address, or identifier is replaced with a believable stand-in rather than an unusable placeholder. Its differentiation is that protection happens at the verification boundary, not as an after-the-fact monitoring layer or a conventional DLP checkpoint. That means the AI system, third-party processor, or autonomous agent operates on safe surrogates by default, while re-identification is handled separately under explicit rules tied to role, purpose, session, or destination. For regulated environments, this design directly addresses the core deployment blocker: how to use powerful external models and agent frameworks without leaking personal, financial, health, or other high-risk data into systems you do not fully control. It also changes the risk model for agentic AI, because even if an agent over-collects, persists memory, or accesses connected systems too broadly, the exposed context is aliased rather than the original sensitive record.