Microsoft Copilot promises to accelerate drafting, analysis, and decision support, but its non‑deterministic behavior introduces a new class of risk that legacy controls were never designed to handle. Large language models (LLMs) do not “look up” exact answers; they generate new text based on probabilistic patterns, which means they can hallucinate plausible‑sounding but factually incorrect content. At the same time, they can blend snippets from a wide range of internal sources in ways that even a careful reviewer may struggle to fully unwind under time pressure.  

In a typical enterprise, that combination is manageable if usage is low and confined to experimentation. Once Copilot is integrated into day‑to‑day workflows, such as contract drafting, financial analysis, case summaries, incident write‑ups, customer communications, the stakes change dramatically. A hallucinated clause in a contract, a misrepresented control in a SOC report, or a generated summary that quietly includes regulated personal data can all create legal, regulatory, or reputational exposure. These errors are often subtle and discovered only after they have already been shared with customers, regulators, or the board.  

Complicating matters, most compliance frameworks were not written with AI‑generated content in mind. Regulations such as SOX, HIPAA, GDPR, PCI, CCPA, and industry‑specific rules focus on how organizations collect, process, store, and disclose sensitive data, but they do not prescribe exactly how AI assistants like Copilot should behave. Security and compliance leaders are left to interpret how existing obligations apply to output that may be partly accurate, partly hallucinated, and derived from a blend of multiple internal systems.  

Meanwhile, visibility into AI behavior is limited. Many teams cannot answer basic governance questions, such as: 

  • Which data sets are being used as “knowledge” for Copilot and other AI systems? 
  • When Copilot surfaces regulated or customer‑specific information, who sees it and in what context? 
  • How often are AI tools generating content that should be treated as records subject to retention, legal hold, or audit requirements?  

Getting the Answers 

Without a unified, contextual view of how AI systems access, transform, and expose data, organizations cannot reliably demonstrate to auditors, regulators, or their own boards that Copilot is under control. Log files and point‑in‑time scans are not enough when content is being generated and shared dynamically, at scale.  

Bonfy is designed to provide that missing oversight layer for Copilot and other AI systems. It analyzes content with entity‑aware context across email, files, Microsoft 365, Copilot, SaaS apps, and AI agents, understanding not just what the text contains but who it belongs to and which trust boundaries apply. When AI outputs contain sensitive or regulated data tied to specific people, customers, or regions, Bonfy can enforce policies in real time, blocking, quarantining, or modifying content before it becomes a problem.  

Equally important, Bonfy tracks which humans, systems, and AI agents interacted with sensitive content, providing the auditability and explainability that internal AI governance frameworks increasingly demand. This creates a defensible record that shows how AI‑generated content was governed, which controls were in place, and how specific incidents were detected and remediated.  

Understand Your Risk 

Oversight starts with knowing where you stand. Use the Microsoft Copilot Risk Assessment to identify where hallucinations, AI‑generated content, and gaps in visibility could translate into real compliance incidents, and what to fix first.