---
title: RAI Assessment Template
description: 'Template structure for RAI assessment documents generated by the rai-planner agent'
sidebar_position: 6
author: microsoft/hve-core
ms.date: 2026-03-18
ms.topic: reference
---

This template defines the standard structure for Responsible AI assessment documents.

## Template Structure

````markdown
# RAI Assessment - [Project Name]

## Preamble

_Important to note:_ This Responsible AI assessment cannot certify or attest to the complete safety or fairness of an AI system. This document is intended to help produce RAI-focused backlog items, evaluate against the Microsoft Responsible AI Impact Assessment Guide and NIST AI RMF 1.0, and document assessment findings including security model analysis, impact assessment, and tradeoff decisions.

## System Definition

| Field                | Value                                             |
|----------------------|---------------------------------------------------|
| System Name          | [System name]                                     |
| System Purpose       | [What the system does and the problem it solves]  |
| AI/ML Components     | [Model types, frameworks, training approaches]    |
| Deployment Model     | [Cloud, edge, hybrid, on-device]                  |
| Data Inputs          | [Training data sources, inference inputs]         |
| Data Outputs         | [Predictions, recommendations, generated content] |
| Intended Use Context | [Target users and operational environment]        |
| Out-of-Scope Uses    | [Explicitly excluded use cases]                   |
| Assessment Date      | [YYYY-MM-DD]                                      |
| Entry Mode           | [capture, from-prd, from-security-plan]           |

### AI Component Inventory

| Component        | Model Type                         | Training Approach                               | Inference Pipeline     | Primary RAI Concerns      |
|------------------|------------------------------------|-------------------------------------------------|------------------------|---------------------------|
| [Component name] | [Classification, generation, etc.] | [Supervised, self-supervised, fine-tuned, etc.] | [API, embedded, batch] | [Fairness, privacy, etc.] |

### Stakeholder Roles

| Stakeholder        | Role                                                       | Relationship to System                          |
|--------------------|------------------------------------------------------------|-------------------------------------------------|
| [Stakeholder name] | [Developer, operator, affected individual, oversight body] | [Direct user, indirect subject, reviewer, etc.] |

## Stakeholder Impact Map

| Stakeholder Group | Potential Impact                  | Impact Pathway      | Risk Level                 | Vulnerable Population |
|-------------------|-----------------------------------|---------------------|----------------------------|-----------------------|
| [Group name]      | [Description of potential impact] | [How impact occurs] | [Critical/High/Medium/Low] | [Yes/No]              |

## RAI Standards Mapping

### Microsoft Responsible AI Impact Assessment Guide

| Principle              | Applicable Components | Assessment Criteria                                       | Tooling                                      | Compliance Status  |
|------------------------|-----------------------|-----------------------------------------------------------|----------------------------------------------|--------------------|
| Fairness               | [Components]          | Demographic parity, equalized odds, group-level fairness  | Fairlearn, RAI Dashboard Fairness Analysis   | [Full/Partial/Gap] |
| Reliability and Safety | [Components]          | Cohort error analysis, performance bounds, stress testing | RAI Dashboard Error Analysis, Model Overview | [Full/Partial/Gap] |
| Privacy and Security   | [Components]          | Differential privacy verification, data minimization      | SmartNoise, Microsoft Purview DSPM           | [Full/Partial/Gap] |
| Inclusiveness          | [Components]          | Dataset representation analysis, accessibility testing    | RAI Dashboard Data Analysis                  | [Full/Partial/Gap] |
| Transparency           | [Components]          | Feature importance, SHAP values, model card completeness  | InterpretML, RAI Dashboard Interpretability  | [Full/Partial/Gap] |
| Accountability         | [Components]          | PDF scorecards, model cards, decision audit trails        | RAI Dashboard Scorecard, Audit logging       | [Full/Partial/Gap] |

### NIST AI RMF 1.0

| Function | Category | Subcategory | Applicability    | Current Posture           |
|----------|----------|-------------|------------------|---------------------------|
| Govern   | GV-1     | GV-1.1      | [Applicable/N/A] | [Implemented/Partial/Gap] |
| Govern   | GV-1     | GV-1.2      | [Applicable/N/A] | [Implemented/Partial/Gap] |
| Govern   | GV-2     | GV-2.1      | [Applicable/N/A] | [Implemented/Partial/Gap] |
| Govern   | GV-3     | GV-3.1      | [Applicable/N/A] | [Implemented/Partial/Gap] |
| Govern   | GV-4     | GV-4.1      | [Applicable/N/A] | [Implemented/Partial/Gap] |
| Govern   | GV-5     | GV-5.1      | [Applicable/N/A] | [Implemented/Partial/Gap] |
| Govern   | GV-6     | GV-6.1      | [Applicable/N/A] | [Implemented/Partial/Gap] |
| Map      | MP-1     | MP-1.1      | [Applicable/N/A] | [Implemented/Partial/Gap] |
| Map      | MP-2     | MP-2.1      | [Applicable/N/A] | [Implemented/Partial/Gap] |
| Map      | MP-3     | MP-3.1      | [Applicable/N/A] | [Implemented/Partial/Gap] |
| Map      | MP-4     | MP-4.1      | [Applicable/N/A] | [Implemented/Partial/Gap] |
| Map      | MP-5     | MP-5.1      | [Applicable/N/A] | [Implemented/Partial/Gap] |
| Measure  | MS-1     | MS-1.1      | [Applicable/N/A] | [Implemented/Partial/Gap] |
| Measure  | MS-2     | MS-2.1      | [Applicable/N/A] | [Implemented/Partial/Gap] |
| Measure  | MS-3     | MS-3.1      | [Applicable/N/A] | [Implemented/Partial/Gap] |
| Measure  | MS-4     | MS-4.1      | [Applicable/N/A] | [Implemented/Partial/Gap] |
| Manage   | MG-1     | MG-1.1      | [Applicable/N/A] | [Implemented/Partial/Gap] |
| Manage   | MG-2     | MG-2.1      | [Applicable/N/A] | [Implemented/Partial/Gap] |
| Manage   | MG-3     | MG-3.1      | [Applicable/N/A] | [Implemented/Partial/Gap] |
| Manage   | MG-4     | MG-4.1      | [Applicable/N/A] | [Implemented/Partial/Gap] |

## RAI Security Model Addendum

### AI-Specific Threat Categories

| Category            | Threat Focus                                                |
|---------------------|-------------------------------------------------------------|
| Data poisoning      | Manipulation of training or fine-tuning data                |
| Model evasion       | Adversarial inputs designed to cause misclassification      |
| Prompt injection    | Manipulation of LLM prompts to override instructions        |
| Output manipulation | Altering model outputs in transit or post-processing        |
| Bias amplification  | Model behavior that reinforces or amplifies existing biases |
| Privacy leakage     | Extraction of training data, PII, or sensitive information  |
| Misuse escalation   | System capabilities repurposed for unintended harmful uses  |

### Threat Catalog

| Threat ID              | Category   | STRIDE        | Affected Component | Description          | Likelihood                      | Impact                     | Risk         |
|------------------------|------------|---------------|--------------------|----------------------|---------------------------------|----------------------------|--------------|
| RAI-T-[CATEGORY]-[NNN] | [Category] | [STRIDE type] | [Component name]   | [Threat description] | [Likely/Possible/Unlikely/Rare] | [Critical/High/Medium/Low] | [Risk level] |

### Severity Matrix

| Likelihood \ Impact | Low    | Medium | High     | Critical |
|---------------------|--------|--------|----------|----------|
| Very likely         | Medium | High   | Critical | Critical |
| Likely              | Low    | Medium | High     | Critical |
| Possible            | Low    | Medium | Medium   | High     |
| Unlikely            | Low    | Low    | Medium   | Medium   |
| Rare                | Low    | Low    | Low      | Medium   |

## Control Surface Catalog

| Control ID    | Threat ID            | Principle       | Control Type             | Control Description     | Implementation Status     |
|---------------|----------------------|-----------------|--------------------------|-------------------------|---------------------------|
| [EV-ABBR-NNN] | [RAI-T-CATEGORY-NNN] | [RAI principle] | [Prevent/Detect/Respond] | [What the control does] | [Implemented/Planned/Gap] |

### Control Surface Matrix

| Principle              | Prevent                                   | Detect                                | Respond                          |
|------------------------|-------------------------------------------|---------------------------------------|----------------------------------|
| Fairness               | [Bias testing, balanced training data]    | [Demographic parity monitoring]       | [Retraining pipelines, rollback] |
| Reliability and Safety | [Input validation, adversarial testing]   | [Drift detection, anomaly monitoring] | [Graceful degradation, fallback] |
| Privacy and Security   | [Differential privacy, data minimization] | [Data leakage detection]              | [Breach response, data deletion] |
| Inclusiveness          | [Accessibility testing, diverse research] | [Usage gap analysis]                  | [Content adaptation]             |
| Transparency           | [Model cards, explanation interfaces]     | [Explanation quality monitoring]      | [Explanation correction]         |
| Accountability         | [Role-based access, approval workflows]   | [Compliance monitoring]               | [Escalation procedures]          |

## Evidence Register

| Evidence ID     | Threat ID            | Principle       | Control Type             | Coverage Status    | Verification Status                      | Evidence Source        |
|-----------------|----------------------|-----------------|--------------------------|--------------------|------------------------------------------|------------------------|
| EV-[ABBR]-[NNN] | [RAI-T-CATEGORY-NNN] | [RAI principle] | [Prevent/Detect/Respond] | [Full/Partial/Gap] | [Verified/Unverified/Partially Verified] | [Artifact or document] |

**Legend:**

* Principle abbreviations: FAIR, REL, PRIV, INCL, TRAN, ACCT
* Coverage Status: Full (control exists and covers the threat), Partial (control exists but incomplete), Gap (no control)
* Verification Status: Verified (tested and confirmed), Partially Verified (some test cases pass), Unverified (no testing performed)

## Tradeoff Analysis

| Tradeoff        | Competing Principles            | Description                                  | Resolution Recommendation            |
|-----------------|---------------------------------|----------------------------------------------|--------------------------------------|
| [Tradeoff name] | [Principle A] vs. [Principle B] | [How the principles conflict in this system] | [Recommended approach and rationale] |

Common tradeoff patterns:

| Tradeoff                 | Example                                                                         |
|--------------------------|---------------------------------------------------------------------------------|
| Transparency vs. Privacy | Explaining model decisions may reveal sensitive training data                   |
| Fairness vs. Performance | Debiasing techniques may reduce model accuracy for some populations             |
| Safety vs. Inclusiveness | Conservative safety filters may disproportionately restrict certain user groups |

## Scorecard

### Dimension Scores

| Dimension                   | Score (1-5) | Assessment           |
|-----------------------------|-------------|----------------------|
| Scope Boundary Clarity      | [1-5]       | [Assessment summary] |
| Risk Identification Quality | [1-5]       | [Assessment summary] |
| Control Surface Adequacy    | [1-5]       | [Assessment summary] |
| Evidence Sufficiency        | [1-5]       | [Assessment summary] |
| Future Work Governance      | [1-5]       | [Assessment summary] |
| **Total**                   | **[X]/25**  |                      |

### Outcome Tiers

| Tier                 | Score Range | Meaning                                                               |
|----------------------|-------------|-----------------------------------------------------------------------|
| Approved             | 20–25       | Assessment is comprehensive; proceed with identified mitigations      |
| Conditional          | 15–19       | Assessment has gaps; proceed with conditions and remediation timeline |
| Remediation Required | Below 15    | Significant gaps identified; remediation before proceeding            |

**Assessment outcome:** [Approved/Conditional/Remediation Required]

## Backlog Items

### [Priority] [Work Item Title]

**RAI Principle:** [Principle name] | **Threat ID:** [RAI-T-CATEGORY-NNN or "N/A"]
**Effort:** [S/M/L/XL] | **Control Type:** [Prevent/Detect/Respond]
**Prerequisite:** [Work item ID or "None"]

#### Description

[What needs to be done and why — include the RAI benefit]

#### Implementation Steps

1. [Concrete step with file path or tool reference]
2. [Next step]

#### Acceptance Criteria

- [ ] [Verifiable criterion]
- [ ] [Verifiable criterion]

#### ADO Mapping

- Type: [Epic/Feature/User Story/Task]
- Tags: rai, [principle], [threat-category]

#### GitHub Mapping

- Labels: rai, [principle], [threat-category]
- Milestone: [Milestone name]
````

🤖 *Crafted with precision by ✨Copilot following brilliant human instruction, then carefully refined by our team of discerning human reviewers.*
