data-leakage-prevention

Name: data-leakage-prevention
Availability: InStock
Author: Akshay

Map data flows and identify leakage risks across AI systems.

4|Updated Apr 27, 2026

Search Tags:#security #logging #risk-assessment #privacy #redaction #pii #data-leakage

Ready to run

npx skills add https://github.com/maruakshay/mii-ai-security --skill data-leakage-prevention

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: data-leakage-prevention
Source: https://github.com/maruakshay/mii-ai-security/tree/main/skills/data-leakage-prevention
Command: npx skills add https://github.com/maruakshay/mii-ai-security --skill data-leakage-prevention

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Data leakage prevention helps identify and mitigate the accidental or deliberate exposure of sensitive information such as secrets, PII, tenant data, embeddings, logs, traces, memory, and training artifacts across AI systems and deployment models.

Core Features & Use Cases

Data inventory and flow mapping to locate leakage surfaces across prompts, logs, memories, and datasets.
Threat modeling and remediation guidance including redaction, access control, and retention policies.
Use Case: assess a multi-tenant AI service to ensure tenant data and secrets do not leak through retrieval, logging, or training datasets.

Quick Start

Audit data flows in your AI system and implement redaction and access controls to mitigate leakage.

Frequently Asked Questions about data-leakage-prevention

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I prevent data leakage in multi-tenant AI systems?▼

Preventing data leakage in multi-tenant AI systems requires mapping data flows across prompts, logs, and embeddings to locate exposure surfaces. You can then apply remediation controls like redaction, access policies, and retention limits to isolate tenant data.

What is data leakage surface mapping in AI deployments?▼

Data leakage surface mapping is the process of inventorying sensitive information across retrieval pipelines, observability logs, and memory to identify unauthorized visibility. It generates a matrix showing where PII or secrets might accidentally cross tenant boundaries.

How do I redact PII and secrets from LLM prompts and logs?▼

To redact PII and secrets from LLM prompts and logs, audit your data pipelines and apply automated redaction rules before data reaches retrieval or observability layers. This ensures sensitive information is masked before being stored in memory or training datasets.

Can I use this approach to secure data across model training and retrieval pipelines?▼

Yes, this approach assesses data flows across both model training datasets and retrieval pipelines to prevent unauthorized visibility. It provides remediation guidance including access controls and retention policies to secure multi-tenant enterprise AI deployments.

What's the best way to assess data leakage risks in enterprise AI observability data?▼

Assessing data leakage risks in enterprise AI observability data involves creating a data inventory of traces and logs to locate exposure surfaces. You then apply retention controls and access policies to mitigate the risk of sensitive tenant data exposure.

When do I need a data leakage risk assessment for my AI application?▼

You need a data leakage risk assessment when deploying multi-tenant AI services where tenant data, PII, or secrets could accidentally expose through retrieval, logging, or training artifacts. It maps unauthorized visibility and guides remediation across all system components.