How to Classify and Protect Sensitive Data in AI Models

As artificial intelligence (AI) and large language models (LLMs) become embedded in more enterprise workflows via Microsoft 365 Copilot, Azure OpenAI, Copilot Studio, Fabric, and custom agents there is a heightened risk of accidental data exposure, model inversion, or leakage. Organizations must now treat AI as both a tool and a potential vector for sensitive data exposure.

Microsoft has responded by integrating classification, data protection, and governance directly into its AI stack, especially via Microsoft Purview’s AI-aware controls.

In this post, we’ll walk you through how IT admins, solution architects, and security teams can classify sensitive data before it enters AI pipelines and protect it during inference, training, and storage.

Why This Matters

Data risk amplifies in AI workflows. AI systems can unintentionally regurgitate sensitive data (e.g., PII/invoice info) or infer patterns about underlying datasets.
Regulatory & compliance exposure. Privacy laws (GDPR, HIPAA, CCPA) hold you accountable if personal or regulated data leaks even via model output.
Model security & intellectual property. Training data, model weights, prompts, and embeddings are high-value assets; leaking them can be damaging.
Trust & adoption. Teams won’t adopt generative AI if they don’t trust that their data is safe. Embedding classification and protection fosters confidence.

In short: classification + protection = foundation for responsible AI in the Microsoft ecosystem.

Key Capabilities / Features

Here’s a matrix of Microsoft capabilities to classify and protect sensitive data in AI contexts:

Capability	Description	Relevance in AI Models / Scenarios
Microsoft Purview AI-aware classification & protection	Uses sensitivity labels, classifiers, context-based rules, and DSPM to discover and protect sensitive data across Microsoft 365, Azure, Copilot, and more.	Ensures that data entering generative AI systems is labeled and that labelled content is not exposed to unauthorized users or models.
Azure AI Language PII / entity detection	Detects, classifies, and redacts PII (names, emails, SSNs, license plates, etc.) in text or conversational input.	Remove or mask sensitive entities in real time before they are passed to downstream LLMs
Data Security Posture Management (DSPM) for AI	Provides visibility into AI data flows, recommends controls, and surfaces blind spots via unified governance.	Helps you understand which AI apps or services may be crossing sensitivity boundaries and where you need to enforce protection.
Purview DLP & AI-aware DLP policies	Policies to detect and block sensitive data being uploaded or pasted into AI tools or channels.	Prevents inadvertent or malicious data exfiltration into generative AI systems (Copilot Studio, agents, etc.).
Encryption, access control, and network isolation	Use Azure RBAC, private endpoints, encryption (at rest/in transit), and zero-trust design on AI training/serving pipelines	Ensures that only authorized systems or users see sensitive data or artifacts.
Model safety & Microsoft’s AI model ranking	Microsoft now plans to add a “safety” dimension to its model leaderboard (alongside cost, quality, throughput). Financial Times	Helps architects choose AI models with stronger safety guarantees and less risk of leakage or bias.

Note: For Azure OpenAI / AI Foundry, Microsoft guarantees that your prompt, completions, embeddings, and training data are not used for other customers or to train foundation models without your permission. Azure Direct Models in Azure AI Foundry.

Best Practices

Below are field-tested and Microsoft-recommended best practices for classification and protection in AI workflows:

Define a clear sensitivity taxonomy and labeling strategy
Use 3–5 top-level sensitivity labels (e.g., Public / Internal / Confidential / Highly Confidential) and map which data types belong where. Microsoft recommends limiting labels to keep UI manageable.
Use automated classification & trainable classifiers
Don’t rely entirely on manual labeling. Leverage Purview’s built-in classifiers, trainable classifiers, keyword/dictionary rules, and context signals.
Use explicit PII/Entity redaction before AI ingestion
Before feeding text or customer content into models, run PII detection/redaction pipelines (e.g., via Azure AI Language). Mask or replace sensitive entities with placeholders.
Apply sensitivity labels as metadata to datasets, documents, and prompts
This metadata should “travel” with the content so that downstream systems (e.g., Copilot, agents, LLM) can enforce policies based on classification.
Use AI-aware DLP policies and context rules
For example, block or alert on pasting highly confidential content into a Copilot prompt or disallow exporting labelled datasets to public AI services.
Isolate sensitive workloads
Use private endpoints, virtual networks, and strict RBAC around AI pipelines. Keep the processing environment logically segmented from less sensitive systems.
Audit, monitor, and use DSPM insights
Continuously monitor AI interactions, examine logs for policy violations, and follow recommendations surfaced by DSPM for policy tightening.
Test model outputs for leakage and vulnerabilities
Perform red-team tests (e.g., prompt attacks, membership inference, attribute inference). Understand Microsoft’s vulnerability severity classification to gauge risk.
Consider synthetic or anonymized data for training
When possible, replace real sensitive inputs with synthetic or masked datasets to reduce the risk surface.
Govern and review regularly
As your AI usage evolves (new agents, datasets, models), revisit labels, policies, and configurations. Use Purview’s “What’s new” roadmap to stay current.

Real-World Scenarios

Here are example scenarios to illustrate how classification and protection can be applied:

Scenario 1: Copilot in Finance Division

A finance team uses Microsoft 365 Copilot to generate budget forecasts, but sometimes pastes internal financial statements or vendor contracts. By implementing a DLP rule that blocks “Highly Confidential” content from entering Copilot prompts, they ensure that sensitive financials are never leaked to the AI.

Scenario 2: Agent in Legal/Contract Review

Your organization builds a Copilot Studio agent to assist with contract drafting. The agent ingests past contract corpus, some of which contains client PII. By classifying documents, redacting client identifiers, and labeling residual text as “Internal,” you reduce the risk that the AI will expose client info. In parallel, audit logs capture any sensitive usage.

Scenario 3: Training Custom LLMs

Your data science team is training a domain-specific model using internal product data, some of which is proprietary. You classify the training dataset, restrict access by role, encrypt storage, and run leakage tests (e.g., prompt-based extraction attacks). Any output artifacts that might inadvertently contain sensitive patterns are post-processed or withheld. By combining classification and encryption, you preserve IP integrity.

Scenario 4: SaaS Copilot across Partner Boundaries

You on-board third-party partners into a Microsoft Copilot environment. You apply sensitivity labels and DLP boundaries to ensure partner users cannot access or ask about “Confidential” internal data from your tenant. DSPM helps you monitor cross-tenant interactions for any anomalies.

How Olive + Goose Can Help

At Olive + Goose, we help organizations turn Microsoft’s AI data protection and governance capabilities into practical, secure, and scalable solutions. Our expertise ensures that sensitive data stays protected while AI innovation moves forward.

Deploy Microsoft Purview sensitivity labels, DLP, and DSPM to classify and monitor sensitive data across Microsoft 365, Copilot, and Azure AI.
Conduct AI governance and security workshops aligned with Microsoft’s Responsible AI principles.
Perform Copilot readiness assessments to validate tenant, labeling, and DLP configurations before rollout.
Design and implement secure Azure OpenAI environments with encryption, private endpoints, and automated PII redaction pipelines.
Provide training and change management programs to help IT and compliance teams manage Purview labeling and DSPM insights effectively.
Offer continuous advisory and compliance support to align with Microsoft’s evolving AI roadmap and best practices.

By combining technical depth with field-proven Microsoft expertise, Olive + Goose empowers enterprises to adopt AI securely, responsibly, and confidently balancing innovation with governance and trust.

Talk to us!