1. Overview
  2. Getting Started
  3. How Peridot Works

How Peridot Works

Overview

Peridot operates as a control layer that sits between your systems and AI providers.

It ensures that every AI interaction is visible, governed, and auditable—without requiring changes to existing applications.

The Core Architecture

Peridot uses a hybrid architecture:

  • Control Plane — Centralized governance and policy logic

  • Data Plane — Execution layer within your environment

This separation ensures that governance is centralized while data remains under your control.

Request Lifecycle

Every AI request follows a consistent lifecycle:

1. Request Initiation

A request originates from:

  • An internal application

  • A user interacting with an AI tool

  • An automated workflow

2. Context Collection

Peridot collects context about the request:

  • User identity and role

  • Application or system

  • Data classification

  • Environment

  • Requested model

3. Policy Evaluation

Policies are evaluated in real time:

  • Conditions are checked

  • Rules are applied

  • Constraints are enforced

This determines whether the request is allowed, modified, or blocked.

4. Routing

If the request is allowed:

  • It is routed to an approved model or provider

  • Routing decisions are based on policies

This ensures consistent and compliant model usage.

5. Execution

The request is executed in the data plane:

  • Models process the request

  • Data remains within your environment

  • Integrations may be triggered

6. Enforcement

If a policy is triggered:

  • Actions are applied (block, reroute, approve, log, etc.)

  • Incidents may be created

7. Logging and Audit

All activity is recorded:

  • Request metadata

  • Policy decisions

  • Enforcement actions

  • Outcomes

This creates a complete audit trail.

What Makes This Different

Traditional systems rely on application-level logic for AI usage.

Peridot centralizes control:

  • Policies are defined once and applied everywhere

  • Routing is dynamic and policy-driven

  • Enforcement happens before execution

  • Auditability is built in

Real-World Example

A user attempts to send sensitive data to an external model:

  1. Request is initiated

  2. Data is classified as sensitive

  3. Policy blocks external model usage

  4. Request is rerouted to approved internal model

  5. Event is logged

  6. Incident is created

The entire process happens automatically.

Performance and Latency

Policy evaluation and routing are designed to operate in milliseconds.

This ensures:

  • No noticeable delay in user experience

  • Real-time enforcement

  • Scalable performance across environments

Failure Handling

Peridot includes safeguards for failure scenarios:

  • If no policy matches → default rules apply

  • If routing fails → fallback models can be used

  • If integrations fail → events are logged and surfaced

This ensures consistent operation even under edge conditions.

In Production

In a deployed environment:

  • All AI interactions pass through governance

  • Data remains within the customer environment

  • Policies are enforced consistently

  • Incidents are automatically generated

  • Audit logs capture every action

Why This Matters

Without a control layer:

  • AI usage is fragmented

  • Data exposure risk increases

  • Policies are inconsistently applied

  • Auditability is incomplete

Peridot solves this by making control centralized and enforceable.

Next Steps

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