The rapid adoption of generative AI in the workplace has moved faster than most companies’ ability to manage what data these tools can access. The numbers make that gap clear.
IBM reported that employee use of GenAI tools surged to 96% between 2023 and 2024, and a sizable share (38%) of those employees admit they’ve pasted sensitive information into an AI system without authorization. It’s a simple mistake with real consequences. Once data goes into a prompt, organizations often lose visibility, and in some cases, control, over where it ends up.
That’s why data-redaction rules are a core component of AI Policy 201. Instead of debating whether to use AI, leaders now need to define the guardrails that let Copilot, Gemini, and similar tools operate safely.
This guide walks you through how to build those rules so they protect private information without slowing your teams down.
The pace of AI adoption has opened a significant governance gap. IDC and Microsoft found that enterprise GenAI adoption rose from 55% to 75% in just one year.
Yet Accenture’s 2025 research shows that only 22% of organizations have formal AI-use policies or employee training. That disconnect explains why teams often feel both energized and uneasy as they deploy new AI tools.
Data redaction is the bridge between those two reactions. It sets clear boundaries around what can and cannot be entered into a prompt, surfaced in a generated response, or stored in meeting transcripts and system logs.
When most people hear “redaction,” they imagine black boxes on a PDF. In the AI world, it’s far more dynamic. Redaction can mean stripping identifying details from meeting recordings, masking financial account numbers before an LLM reviews a spreadsheet, or filtering sensitive fields before anything reaches a model.
Another reason this matters is that user behavior creates far more risk than system failures. Proofpoint’s 2024 report found that 71% of all data-loss incidents came from well-meaning but careless actions. And because AI tools are now embedded in everyday workflows, Teams chats, documents, email, dashboards, those same mistakes easily carry over into prompts.
At the same time, AI adoption often rises alongside broader modernization efforts. As organizations pursue digital transformation or invest in more advanced data strategies, AI naturally becomes part of the mix. Redaction rules make sure those upgrades don’t unintentionally expand the organization’s exposure surface.
Setting redaction rules isn’t about blocking everything. It’s about defining categories, use cases, and boundaries so people can use these systems without guessing what’s allowed.
The following foundational steps can help guide the process:
Start with the data classes that should never appear in prompts under any circumstances. Most organizations settle on similar categories:
Not all workflows carry the same level of risk. Drafting an internal announcement is very different from summarizing a contract or reviewing customer data.
Many organizations classify AI use into three tiers, with redaction thresholds aligned to the tier rather than the tool itself:
Copilot offers organizations several control points when properly configured. Sensitivity labels determine which files Copilot can index or surface. Purview DLP rules can detect sensitive information in prompts or generated responses, either blocking the action or masking the data before it reaches the model. Some teams even apply these rules to meeting transcripts, so PII is removed automatically before summaries or action items are created.
One added benefit is improved operational efficiency. When AI is configured correctly, teams spend less time correcting errors and more time on productive work, something that often comes up in conversations about IT automation benefits.
Google’s ecosystem relies heavily on its classification and redaction engine. Sensitive Data Protection can identify and mask PII in text, images, documents, and storage systems. Gemini-powered Workspace features (Docs, Gmail, Meet, Drive) inherit those controls when configured properly. Some organizations tokenize sensitive identifiers before storing them in BigQuery or Drive so Gemini can still analyze trends without exposing raw data.
This often surprises people, but redaction is effective only when it’s integrated into a broader data protection strategy. Netskope’s 2025 research highlighted that GenAI sites are among the fastest-growing destinations for outbound data risk.
Proofpoint observed a similar pattern: if DLP and insider-risk tools aren’t configured to monitor prompts or uploads, redaction rules alone won’t catch every risk.
User training can help prevent mistakes. A brief, timely reminder when someone starts entering sensitive data into an AI prompt often redirects them before any formal policy enforcement is needed.
It’s useful to view these rules as ongoing work rather than a one-time task. Monitor what gets flagged, where redaction gaps appear, and whether users repeatedly encounter the same issues. Regularly reviewing these signals helps teams address problems early and keeps day-to-day AI use running more smoothly.
The ultimate goal of redaction is straightforward: empower users to leverage AI without worrying about accidentally exposing sensitive information. When employees understand what’s safe to share, they can get more value from Copilot and Gemini while keeping data secure. Clear data categories, practical use-case tiers, and properly configured DLP tools form the foundation of that trust.
If you’re looking for guidance on creating AI-ready governance or need support aligning your data classification and protection strategy with Copilot and Gemini workflows, we can help. Vudu Consulting will work with you to build a safer, more responsible AI environment that aligns with your systems and organizational culture. Start the conversation today.
