A mitigation plan translates risk assessment into action. It is the bridge between identifying a vulnerability and deploying a fix. This sample provides a structured, actionable template for documenting how your organization will address specific findings from an AI red teaming engagement. Use it as a starting point for your own internal processes.
AI System Risk Mitigation Plan
Project: Sentinel Content Moderation API (v2.1)
Document ID: SMP-2024-003
Date: 2024-10-28
Status: Draft for Review
1.0 Executive Summary
This document outlines the mitigation strategy for critical and high-risk vulnerabilities identified during the Q3 2024 Red Team engagement (Report ID: RT-2024-Q3-SENTINEL). The plan details specific technical and procedural actions, assigns ownership, and establishes timelines to reduce the risk exposure of the Sentinel Content Moderation API to an acceptable level as defined by the organizational risk appetite framework.
2.0 Scope
This mitigation plan applies exclusively to the following system components:
- Model:
sentinel-moderator-v2.1-prod - API Endpoint:
/v2/moderate - Environment: Production (US-East-1 Region)
Out of scope for this plan are the model training pipeline and the v1 API, which is scheduled for deprecation.
3.0 Mitigation Action Details
The following table details the actions required to mitigate the identified risks. Each action is tied to a specific finding from the red team report.
| Risk ID | Vulnerability Description | Proposed Mitigation | Owner | Timeline | Status |
|---|---|---|---|---|---|
| R-007 | Prompt Injection (Jailbreaking): System can be manipulated via “role-play” instructions to bypass safety filters and generate harmful content. | Implement a defense-in-depth approach:
|
ML Security Team | 2024-11-15 | In Progress |
| R-011 | Adversarial Text Evasion: The model fails to classify harmful text that uses homoglyphs, invisible characters, or deliberate misspellings. | Develop and deploy a text normalization pre-processing module. This module will sanitize user input by converting homoglyphs to standard characters, stripping zero-width spaces, and correcting common adversarial misspellings. | ML Engineering | 2024-11-30 | Not Started |
| R-012 | Data Poisoning (Training Data): Lack of verification on a third-party data source (CommunitySift-Lite) could allow biased or malicious examples to degrade model performance and fairness. | Establish a data sanitization and verification pipeline for all third-party datasets. Suspend use of CommunitySift-Lite until the pipeline is operational and the dataset has been re-evaluated. | Data Science Team | 2024-12-15 | Not Started |
4.0 Technical Deep Dive: Mitigation for R-011
To address the adversarial text evasion (R-011), the text normalization module will be implemented as a mandatory step before the input is passed to the tokenizer and model.
Pseudocode for Normalizer
The core logic will follow this structure:
# Pseudocode for the text normalization function
function normalize_text(input_string):
# 1. Unicode normalization to handle visual similarities
text = unicode_normalize(input_string, 'NFKC')
# 2. Define and replace common homoglyphs
# Example: Cyrillic 'а' -> Latin 'a'
text = replace_homoglyphs(text, homoglyph_map)
# 3. Strip invisible characters (e.g., zero-width spaces)
text = remove_invisible_chars(text)
# 4. Correct common adversarial misspellings
# Example: 'h4te' -> 'hate'
text = correct_misspellings(text, misspelling_dict)
return text
# --- Example Usage ---
raw_input = "I hаtе yоu with zеrо-width space"
sanitized_input = normalize_text(raw_input)
# sanitized_input should become: "I hate you with zero-width space"
5.0 Resource Allocation
- Personnel: 2 FTE (ML Engineer), 1 FTE (ML Security Analyst) for 4 weeks.
- Compute: Additional resources for deploying and testing the new pre-processing microservice.
- Tools: Subscription to an updated adversarial text dataset for testing and verification.
6.0 Approval
Upon review and acceptance of this plan, the undersigned authorize the allocation of resources and commencement of the mitigation activities described herein.