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Prompt Privacy

Protect sensitive data before it reaches AI models.

A technical discipline that sits between AI usage and AI governance: reduce prompt-level exposure at the gateway, before any third-party model sees the data.

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Definition

What is Prompt Privacy?

Prompt privacy is a narrower, more technical concept than "AI security." It is concerned with one specific surface: the prompt body, in transit, in the moment it leaves the application boundary.

Why it matters

Why Prompt Privacy matters

Every LLM call is a data egress event. Prompts routinely carry names, emails, customer identifiers, ticket bodies, internal documents, source code and credentials — content that would not be sent to a third-party API in any other part of the system without review.

Acceptable-use policies and security reviews matter, but policy alone cannot intercept a prompt mid-flight. Prompt privacy is the technical layer that does.

The prompt is the new egress point. Treat it like one.

Category boundaries

Prompt Privacy vs. AI Security

AI security is the broader program: model abuse, infrastructure, output handling, evaluations, governance. Prompt privacy is one discipline inside that program — focused on what the model is allowed to receive.

Framework

Where prompt privacy sits

  1. 01

    AI security

    Broad program covering model, infrastructure, output and governance.

  2. 02

    Prompt privacy

    Narrow discipline focused on what data reaches the model in the prompt body.

  3. 03

    Shared boundary

    Both rely on a central control point — typically a gateway — to enforce policy consistently.

See: Prompt privacy vs. AI security.

Category boundaries

Prompt Privacy vs. Prompt Injection

Prompt privacy

A data-exposure problem. Sensitive values leave the application in the prompt body and reach a third-party model. Addressed with detection, masking and provider boundaries.

Prompt injection

An adversarial-input problem. Untrusted content tries to hijack the model's instructions. A different threat model, addressed with content-handling techniques. Privian does not claim to defend against prompt injection.

Category boundaries

Prompt Privacy vs. Compliance

Compliance certifies a posture against a framework. Prompt privacy is a technical control. Compliance frameworks may reference prompt-privacy controls, but adopting prompt privacy is not, by itself, a compliance statement and does not certify HIPAA, SOC 2, PCI or any other framework.

Category boundaries

Prompt Privacy vs. Governance

Governance describes how AI is adopted: which tools, by whom, for what. Prompt privacy describes what happens to the data inside the tools governance has approved. The two are complementary; neither replaces the other.

How it works

How Prompt Privacy works

A prompt-privacy implementation typically lives at the gateway between the application and one or more AI providers. The flow is deterministic and per-request.

A user produces a prompt. The gateway detects sensitive entities, masks them with deterministic placeholders, forwards the masked prompt to the LLM provider, and rehydrates placeholders in the response before returning it to the user.01User02Prompt03Detection04Masking05Provider06Rehydration
Prompt Privacy flow— Sensitive values are masked before egress and rehydrated on the return path.

Framework

Per-request flow

  1. 01

    Detect

    Identify supported sensitive entities in the prompt body.

  2. 02

    Mask

    Replace originals with deterministic placeholders before egress.

  3. 03

    Route

    Forward only the masked prompt to the configured AI provider.

  4. 04

    Rehydrate

    Restore originals in the response before returning to the application.

  5. 05

    Discard

    Drop the per-request mapping; never persist raw prompt or response bodies.

Control stack

Prompt Privacy control stack

Prompt privacy is not a single mechanism. It is a small, well-defined stack of controls that compensate for each other's gaps.

From top to bottom: policy, detection, masking, provider controls and retention controls — the five categories that define a prompt-privacy posture.01 · PolicyWhat is allowed in a prompt02 · DetectionRecognise sensitive entities03 · MaskingReplace before egress04 · Provider controlsTier, region, account boundary05 · Retention controlsZero raw-prompt persistence
Prompt Privacy control stack— Five categories of control. None is sufficient alone.
A raw prompt enters at the top. Application minimization drops unnecessary fields. Detection identifies sensitive entities. Masking replaces originals with placeholders. Only the masked content crosses the provider boundary.Raw prompt01 · Application minimizationDrop fields the prompt never needs02 · DetectionIdentify supported sensitive entities03 · MaskingReplace originals with deterministic placeholders04 · Provider boundaryOnly masked content crosses BYOK→ masked prompt to LLM provider
Prompt exposure model— Each layer reduces what ever reaches the LLM provider.

Where Privian fits

Where Privian fits

Privian implements prompt privacy as a privacy-first LLM gateway. It detects supported sensitive entities, replaces them with deterministic placeholders, forwards the masked prompt to the configured provider using your own keys (BYOK), and rehydrates the response on the way back. Raw prompts and responses are not persisted.

See Prompt Security, PII Masking and Rehydration for the implementation surface, and the data path for what Privian does and does not retain.

Scope

What Prompt Privacy does NOT solve

  • Prompt injection

    Prompt privacy does not defend against adversarial input crafted to hijack a model's instructions.

  • Content moderation

    It does not enforce safety policies on model output or filter generated content.

  • Model hallucination

    It does not evaluate factual accuracy or constrain what the model says.

  • Downstream misuse

    It cannot control what an application or user does with the model's response once returned.

  • Governance

    It does not decide which tools, models or use cases are permitted in the organization.

  • Compliance posture

    It does not certify HIPAA, SOC 2 or PCI compliance on its own.

FAQ

Frequently asked questions

What is prompt privacy?
Prompt privacy is the practice of reducing prompt-level exposure — the sensitive data carried inside a prompt — before that prompt reaches an AI model. It focuses on what leaves the application in the prompt body, not on adversarial input or model behavior.
Is prompt privacy the same as AI security?
No. AI security is the broader program: model abuse, infrastructure security, output handling, governance. Prompt privacy is a narrower discipline focused specifically on the data carried in the prompt body before egress.
Is prompt privacy the same as prompt injection defense?
No. Prompt injection is an adversarial-input problem — an attacker trying to hijack model instructions. Prompt privacy is a data-exposure problem — sensitive values being forwarded to an AI provider in plaintext.
Is prompt privacy a compliance program?
No. Prompt privacy is a technical control that complements compliance and governance work. It does not certify a posture, replace a DPA, or substitute for an internal AI policy.
Where does prompt privacy fit in an AI stack?
Between the application and the model provider — typically at a gateway. That is the last point where sensitive values can be detected and replaced before they cross a third-party boundary.
What does prompt privacy not solve?
It does not prevent prompt injection, content-policy violations, model hallucination, downstream misuse, or governance gaps in how AI is adopted. It addresses one specific surface: what data the model actually receives.

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