Microsoft Purview is Microsoft’s unified solution for enterprise data governance, compliance, and risk management in hybrid environments. It integrates information protection, lifecycle management, and access controls into a single interface. The core purpose is to help organizations protect sensitive data while complying with regulatory standards such as GDPR, HIPAA, and ISO/IEC 27001. According to Microsoft, by late 2023, approximately 70% of Fortune 500 companies had adopted Microsoft 365 Copilot, which relies on Microsoft Purview for compliance and governance.
Purview’s three main pillars are:
While established governance capabilities were sufficient for managing file- and user-level access control in a pre-AI world, they were not without limitations. Complex edge cases and cross-platform inconsistencies still challenged administrators. Now, with the rise of enterprise-grade AI like Microsoft Copilot, the threat landscape has evolved dramatically. The shift toward AI assistants exposed the limits of Purview’s current capabilities, which were not designed to govern the nuances of AI-driven inference and knowledge generation.
While Microsoft Purview offers resilient tools for data governance, including sensitivity data labeling and access controls, these measures may not entirely prevent unintentional data exposure through AI-generated content. Studies have highlighted vulnerabilities in AI systems like Copilot, whereby sensitive information can be disclosed despite existing security protocols. For instance, research has shown that AI models can infer and generate responses based on protected content, leading to potential data leaks. Purview data governance focuses on static data governance; it doesn't interpret how or why an AI model generates a response. This gap lies within the 'knowledge layer', where semantic exposure, inference leakage, and intent misalignment can occur.
Microsoft Purview offers a suite of tools designed to protect and manage enterprise data. From sensitivity labeling to activity monitoring, these capabilities help organizations enforce compliance, secure sensitive information, and maintain visibility over data flows. The following components illustrate how Purview delivers structured, policy-driven governance in complex environments.
Microsoft Purview's Sensitivity Labels provides classification and data protection based on sensitivity. Users can apply these labels manually or automatically through policies. Once used, these labels can support encryption, restrict access, and apply visual markings to documents and emails. However, sensitivity labels do not automatically persist across downstream actions, such as file copies, exports, or AI-generated summaries, unless specific inheritance or auto-labeling rules are configured within the environment. Additionally, DLP policies in Purview require Sensitivity Labels to prevent the unintentional sharing of sensitive information. With defined rules that detect specific sensitive data types, such as credit card numbers or health records, DLP policies can block or warn users before data is shared externally. Finally, it is important to highlight that these policies are essential for compliance with regulations like GDPR and HIPAA.
Purview integrates with various Microsoft 365 services, including SharePoint, Teams, and OneDrive. This integration ensures that data stored and shared within these platforms is subject to the organization's data governance policies. For instance, when a document in SharePoint is labeled as confidential, Purview makes sure that only authorized users can access it, and that any sharing actions are monitored and controlled. Moreover, Purview's integration with Microsoft 365 allows for real-time monitoring and protection as data moves across different services.
Purview employs RBAC to manage permissions within the organization. RBAC helps limit access based on roles, butadministrators must define roles and assign permissions accordingly. In addition, Purview provides basic eDiscovery capabilities which enable organizations to identify, hold, and export content relevant to legal cases or investigations. eDiscovery helps legal teams search across Microsoft 365 services to find pertinent information and ensure compliance with legal and regulatory requirements.
Purview's audit logging and insider risk tools work in tandem to monitor user and administrator behavior. These audit logs track file access, sharing events, and permission changes, and offer limited visibility into how data is used within the organization. Purview's Insider Risk Management features help detect and mitigate risks posed by internal users, by analyzing behavior and identifying anomalies.
By correlating these insights, organizations can defend against potential internal threats, such as data exfiltration or high-risk user activity, and improve their overall security posture.
Microsoft Purview's sensitivity labels are designed to classify and protect data. However, their effectiveness depends on their correct and complete application by users. Mislabeling or inconsistent application can lead to sensitive information being inadequately protected. For instance, if a document containing confidential information isn't labeled appropriately, it might not receive the necessary protections, making it accessible to unauthorized users.
Moreover, when sensitivity labels are applied at the container level, such as to Teams or SharePoint sites, they do not automatically propagate to the individual items within those containers. This means that documents, emails, or chats inside a labeled Team or SharePoint site do not inherit the container's sensitivity label. As a result, these items may lack the intended protection, potentially exposing sensitive information if not individually labeled.
Even when files are protected with sensitivity labels and encryption, Microsoft 365 Copilot can access and expose this content if the user has excessive permissions. The Copilot security approach operates within the user's access rights, meaning it can retrieve and present information from protected documents, potentially surfacing sensitive data in responses without explicit user intent. Furthermore, Copilot's ability to infer content based on internal data may inadvertently expose proprietary information, trade secrets, or internal strategies. If this generated content is shared externally or stored insecurely, it can lead to intellectual property theft.
Traditional access controls in Purview are based on user roles and permissions, focusing on who can access specific data. However, they don't account for how AI models like Copilot retrieve and process data. Copilot uses semantic pattern recognition to infer relationships between seemingly innocuous data fragments and generate responses that can infer sensitive information from multiple data sources, even if each source individually doesn't contain sensitive data. This limits what LLM access controls can prevent.
While Purview data governance provides audit logs and monitoring tools, there is limited visibility into how LLMs like Copilot use the data once it is accessed. Administrators can see that data was accessed, but may not know how it was processed or whether sensitive information was included in AI-generated outputs. This lack of transparency creates challenges around detection and oversharing protection, as well as the misuse of sensitive data by AI tools.
Traditional security tools were built for static files and predictable access patterns, but AI tools, especially LLMs, change the equation entirely. They introduce dynamic risks by generating responses that go beyond document-level access. Governing this new inference layer requires a shift from static controls to real-time, context-aware enforcement.
Context-aware enforcement is now essential. It’s no longer just about who can open a file; it’s about understanding why they need access and how they intend to use that information: their “need-to-know”. Traditional models based on static labels or roles fall short in dynamic environments, where users with similar access might have very different responsibilities. For example, an assistant in finance should not be able to view legal insights simply because they share a repository with legal assistants.
Continuous validation of LLM outputs is critical. Once AI models can respond to prompts using internal enterprise data, the need shifts from controlling document access to governing knowledge. Just because a user can access specific files doesn’t mean every AI-generated response is appropriate or secure. Without oversight, AI can infer connections across multiple sources and unintentionally surface sensitive insights.
Red-teaming is a common but insufficient approach. Most organizations still depend on manual testing to identify oversharing risks, but this approach is slow, reactive, and incomplete. A 2023 Gartner survey of 200 IT and data leaders revealed that only 12% had formal AI governance frameworks. Meanwhile, a 2025 study of enterprise prompt data across Copilot and ChatGPT found that 8.5% of prompts risked exposing sensitive information. The exposed data included customer info (45.8%), employee PII (26.8%), legal and financial data (14.9%), and security details (6.9%).
Static access models no longer scale. As enterprise data environments grow more complex, a “need-to-know” approach must replace broad role-based access. This shift requires access decisions based on actual usage patterns and task context, enabling precise, situation-aware enforcement that considers not just who the user is, but what they are doing in the moment.
Knostic doesn't replace Purview. It fills the blind spots that the Purview data governance approach was never built to cover. While Purview governs data at rest (managing files, emails, and cloud storage), LLMs like Copilot infer answers by connecting information across sources. This creates a new exposure layer that static policy tools can't see. Knostic addresses this gap by simulating how LLMs behave with real user access scopes, revealing where tools like Copilot, Glean, or Slack AI may inadvertently expose sensitive knowledge from Teams, SharePoint, or OneDrive.
This exposure often slips past traditional labels. A document marked “safe” can still contribute to an output that reveals a trade secret. Knostic monitors these LLM outputs directly, not just access logs, enabling enforcement of real-time, context-aware controls beyond what data labeling alone can achieve.
By integrating organizational access policies and business context, Knostic dynamically blocks LLM responses. It flags outputs that misalign with intent, even when users technically have file access. Administrators can then easily remediate by updating sensitivity labels or adjusting access policies.
Knostic also creates a governance feedback loop. While Purview logs which data was accessed, Knostic adds visibility into what was generated. It analyzes LLM behavior in real-world usage and recommends refinements to sensitivity labels or DLP rules, helping organizations rapidly evolve policy.
Enterprises are accelerating Copilot rollouts, but few have visibility into what it’s actually saying. Purview sets the ground rules: labels, access, policies. But AI doesn’t follow rules like humans do. It infers, combines, and generates.
That’s why pairing Purview with Knostic’s Copilot Oversharing Solution is critical. Knostic runs simulated LLM queries to identify where Copilot exposes more than it should. The solution applies real-time controls that dynamically block LLM responses based on user role, access patterns, and current task context, not just static file metadata or container labels.
This is where governance must move: from static protection to dynamic enforcement at the moment of inference. Security teams should start auditing AI-generated outputs across M365 and evaluating semantic exposure risks. Then, they should trial Knostic's real-time oversight to automatically identify blind spots in Copilot behavior.
To research further this topic, see this solution brief diagram for how Knostic detects and intercepts risky Copilot responses in real time.
Purview offers enterprise-grade data discovery, static sensitivity labels, and audit trails. It excels at managing structured policy enforcement across Microsoft 365. But it operates at the storage and user levels, not the language layer. It doesn’t evaluate what AI generates in real time.
Not by itself. Purview protects files, not AI outputs. If a user has access to content, Copilot can surface it. This creates blind spots where LLMs generate insights that were never meant to be shared, even though they are technically accessible.
Knostic simulates and tests LLM responses across user access profiles to detect oversharing patterns. It evaluates AI-generated answers based on user context and access policy, not just underlying file permissions. Knostic dynamically blocks responses in real time, ensuring they align with organizational roles and compliance requirements. It also informs Purview by recommending labeling and access strategies updates, turning static governance into adaptive protection.