How to reduce AI-driven data risk with Microsoft Purview

AI-driven phishing is now 4.5 times more effective than traditional methods¹. Eighty per cent of business leaders cite sensitive data leakage as their primary GenAI concern². IDC projects 1.3 billion AI agents by 2028³, meaning multiple agents for every human user, each representing a potential pathway to sensitive data.

Most security teams already manage 40-80 disconnected tools⁴. AI workloads compound this complexity rather than simplifying it. The explosion of data access points, the multiplication of exfiltration vectors, and the velocity of change demand a different approach to data protection.

Adding yet another point solution only deepens the problem.

The hidden complexity of AI data security

AI workloads introduce new risks that traditional security models weren’t built to address:

• Expanded permissions exposure: AI systems such as Microsoft 365 Copilot retrieve any content users are authorised to access, which can surface overshared sites, outdated documents, and misclassified sensitive data.
• New exfiltration vectors: Sensitive information leaks through AI prompts, responses, or agents accessing unauthorised data.
• Visibility gaps: Traditional monitoring struggles to track AI interactions, agent behaviours, and cross-platform data flows.
• Governance challenges: Stale data, misconfigured permissions, and unlabelled content create risks that multiply as AI scales.

These risks span users, data stores, endpoints, SaaS services, and AI systems simultaneously.

Addressing them effectively requires a unified approach that treats data protection as a system rather than a collection of disconnected controls.

A connected system: The Microsoft Purview approach

Microsoft Purview takes a fundamentally different approach to data security in the AI era. Rather than treating protection, detection, and investigation as separate problems, Purview operates as an integrated system where capabilities reinforce each other.

Data is classified once, and that classification drives enforcement, monitoring, and investigation across human users, AI assistants, and agents.

Four pillars working in concert

Information Protection

Information Protection discovers, classifies, and labels data based on sensitivity. Sensitivity labels can apply encryption that travels with the file, enforcing access controls regardless of where data moves.

For AI workloads, this is vital. Microsoft 365 Copilot respects both permissions and encryption. If a user does not have the appropriate view or extract rights on an encrypted document, Copilot cannot surface that content.

Data Loss Prevention (DLP)

Data Loss Prevention builds on classification. Once data is labelled, DLP policies can prevent unauthorised sharing across email, SharePoint, endpoints, and cloud services.

DLP also extends to AI interactions. Policies can:

• Prevent Copilot from processing content with specific sensitivity labels.
• Block prompts containing defined sensitive information types before they are submitted to the AI.

Some of these AI-specific controls are available today, with others continuing to mature, but together they significantly reduce the risk of inadvertent data exposure through AI queries.

Insider Risk Management

Insider Risk Management adds behavioural context. Through configurable, policy-driven analytics, it monitors user and AI-related activity to help surface potentially risky patterns. This can include the use of sensitive data in AI prompts, classified content appearing in AI responses, or access to high-risk locations such as priority SharePoint sites.

Detection is policy-driven and scoped to organisational risk priorities, rather than blanket monitoring.

Data Security Investigations

Data Security Investigations correlates signals across Purview capabilities to accelerate investigation and response. By combining classification, DLP events, and behavioural indicators, security teams gain the context needed to understand scope and act quickly.

The value here lies in integration. A sensitivity label influences what DLP blocks, what Insider Risk flags, and what investigators can trace, without manual correlation.

Hybrid and multi-platform by design

Purview protects data across on-premises file shares, SaaS applications, devices, browsers, networks, and AI apps and agents.

For Microsoft-native AI services such as Copilot Studio and Azure AI Foundry, controls are applied directly through Purview. For third-party GenAI platforms, including ChatGPT Enterprise, visibility and enforcement are typically mediated through Microsoft Defender for Cloud Apps, browser-based controls, and network enforcement rather than native Purview ingestion.

This approach enables consistent governance across AI ecosystems without overstating native integration.

Three actionable steps to Copilot data security

For organisations deploying Microsoft 365 Copilot, three foundational steps significantly reduce risk:

1. Restrict SharePoint access where necessary

Copilot surfaces anything users have permission to view. SharePoint Advanced Management provides Data Access Governance reports that identify overshared sites and sensitive content. Restricted Access Policies and Restricted Content Discoverability limit exposure at the SharePoint layer, which Copilot automatically respects.

2. Apply sensitivity labels across the organisation

Sensitivity labels raise awareness, enable controls, and integrate with the wider Microsoft security stack. With appropriate licensing, auto-labelling applies classifications based on content analysis without relying on user behaviour.

Labels that apply encryption create portable protection. If users lack the required rights, Copilot cannot retrieve the content.

3. Deploy DLP policies targeting AI workloads

DLP policies can be scoped specifically to Microsoft 365 Copilot to:

• Block processing of content with defined sensitivity labels.
• Block prompts containing sensitive information types before they reach the AI.

Together, access restrictions, labelling, and DLP create layered defences that significantly reduce AI-related data exposure using capabilities many organisations already own.

Beyond Copilot: Unified visibility across AI workloads

Microsoft Purview’s Data Security Posture Management (DSPM) consolidates visibility across AI interactions, providing a single pane of glass for risk assessment and remediation.

DSPM surfaces:

• AI interaction trends: Which AI services are being used, how frequently, and by whom
• Sensitive data flows: What types of classified information appear in AI prompts and responses
• Policy coverage gaps: Which AI workloads lack adequate DLP, classification, or monitoring controls
• Remediation recommendations: Specific policies to create or adjust based on observed risk patterns

Reporting breaks down by service (Copilot, enterprise AI apps, web-based GenAI tools), giving teams full visibility of their AI attack surface. DSPM tracks AI-related policies across Purview solutions (DLP, Insider Risk, Communications Compliance).

While you still need to configure your policies within the respective Purview tools, DSPM consolidates these into a single view, eliminating the need to check each individually.

Preparing for Agents: Security that scales

As organisations move from AI assistants to autonomous agents, security models must evolve. Agents act on behalf of users, make decisions, access data, and trigger workflows without direct human oversight.

Microsoft Purview is already adapting in this area with the introduction of:

• Auditing: Agent activity is logged alongside human actions.
• Behavioural monitoring: Agent-focused monitoring and policy templates are being introduced to help organisations identify risky agent behaviour and tune thresholds separately.
• Policy inheritance: Existing sensitivity labels, DLP policies, and access controls apply to agents by default.
• Lifecycle governance: Retention and deletion policies extend to agent interactions to support compliance requirements.

Capabilities in this area continue to mature, but the governance model remains consistent.

Don’t overlook the AI risk from poor data quality

Poor data quality creates its own risk. AI systems trained on or retrieving from inaccurate, outdated, or incomplete data produce unreliable outputs. When decisions are made based on these outputs, business risk multiplies.

Purview addresses this through two mechanisms:

Data Lifecycle Management automates the removal of stale, outdated content from Microsoft 365. Retention policies can automatically delete documents past a certain age or trigger reviews before deletion. This prevents AI tools surfacing obsolete information and reduces the overall attack surface.

Data Quality Rules in Unified Catalog monitor structured data sources feeding AI systems. For organisations using Azure AI Foundry or custom AI models, the quality of underlying databases directly impacts AI accuracy. Purview’s Unified Catalog applies data quality scans to these sources, flagging missing data, inconsistencies, or degradation over time. Continuous monitoring provides red/amber/green indicators, allowing teams to remediate issues before they impact AI outputs.

These tools ensure AI systems work with current, accurate data, reducing both security and operational risk.

The path forward: integrated security for the AI era

Organisations that approach AI data security as an add-on (another tool, another silo, another team) will struggle to keep pace with both risk and opportunity.

Microsoft Purview offers a unified platform where information protection, DLP, insider risk management, and investigation capabilities work as an integrated system. Data is classified once and that classification drives policy enforcement across every access point (human or AI, on-premises or cloud, endpoint or agent).

This integration delivers:

• Reduced complexity: One platform instead of dozens of disconnected tools
• Better visibility: Correlated signals across AI workloads, users, and data
• Faster response: Automated investigation and remediation based on unified telemetry
• Future-ready security: Controls that extend automatically to new AI capabilities

Successful organisations will be those that secured their data comprehensively enough to unlock AI’s full potential.

Microsoft Purview and AI data security FAQs

References

1: Microsoft Digital Defence Report 2025 2: Gartner 3: Microsoft 4: Security Brief

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