AI Security Essentials: What Companies Worry About and How Microsoft Helps

Across industries, the integration of artificial intelligence raises several important considerations for organisations. These include security and privacy, regulatory compliance, ethical factors, and compatibility with existing IT systems. There are also questions around the long-term impact on employees, transparency and interpretability of AI models, and the management of data quality and governance. Mitigating these concerns is essential for the responsible deployment and successful use of AI within business environments.

Summary of Main Concerns When Adopting AI

Figure 1: Summary of main concerns when adopting AI

Microsoft provides a set of products and services intended to assist organisations at various stages of their AI implementation. These offerings prioritise data security, facilitate regulatory compliance, and incorporate tools to support ethical AI practices. The platforms are designed to integrate with existing IT environments and offer resources for skills development. Additionally, Microsoft’s AI tools feature options to enhance model transparency, explainability, and governance. Utilising these tools may help organisations mitigate risks associated with AI adoption, improve operational confidence, and promote responsible use of AI systems.

 

The following section outlines Microsoft’s suite of tools that correspond to the key concerns described above regarding AI adoption.

Security & Privacy Risks

Data leak or Exfiltration

Microsoft Purview Data Loss Prevention (DLP)

Compliance solution that identifies, monitors, and protects sensitive data—whether at rest, in motion, or in use. It uses advanced content inspection and contextual analysis to detect leaks and enforce policies that prevent unauthorized sharing of financial, health, or intellectual property data across various platforms and user activities. These locations and sources include:

• • Microsoft 365 services such as Exchange, SharePoint, OneDrive, and Teams
  • Office applications such as Word, Excel, and PowerPoint
  • Windows 10, Windows 11, and macOS (three latest released versions) endpoints
  • non-Microsoft cloud apps
  • on-premises file shares and on-premises SharePoint
  • Fabric and Power BI workspaces
  • Microsoft 365 Copilot (preview)

DLP, in conjunction with collection policies monitors and protects data-in-motion to prevent oversharing across network channels. It supports browser and network data security for platforms like OpenAI ChatGPT, Google Gemini, DeepSeek, Microsoft Copilot, and over 34,000 cloud apps listed in the Microsoft Defender for Cloud Apps catalog.

It also integrates with Microsoft Defender and Microsoft Purview solutions to provide unified policy management and incident response. Built-in privacy controls ensure user data is handled securely, while enabling compliance teams to take informed, policy-driven actions. To learn more, see Learn about data loss prevention.

 

Microsoft Security Copilot

A generative AI-powered security solution that helps increase the efficiency and capabilities of defenders to improve security outcomes at machine speed and scale. It provides a natural language, assistive copilot experience and helps support security professionals in various end-to-end scenarios such as incident response, threat hunting, intelligence gathering, posture management, and more. Security Copilot focuses on making the following use cases easy to accomplish.

• • Investigate and remediate security threats - gain context for incidents to quickly triage complex security alerts into actionable summaries and remediate quicker with step-by-step response guidance
  • Build KQL queries or analyze suspicious scripts - eliminate the need to manually write query-language scripts or reverse engineer malware scripts with natural language translation to enable every team member to execute technical tasks
  • Understand risks and manage security posture of the organization - get a broad picture of your environment with prioritized risks to uncover opportunities to improve posture more easily
  • Troubleshoot IT issues faster - synthesize relevant information rapidly and receive actionable insights to identify and resolve IT issues quickly
  • Define and manage security policies - define a new policy, cross-reference it with others for conflicts, and summarize existing policies to manage complex organizational context quickly and easily
  • Configure secure lifecycle workflows - build groups and set access parameters with step-by-step guidance to ensure a seamless configuration to prevent security vulnerabilities
  • Develop reports for stakeholders - get a clear and concise report that summarizes the context and environment, open issues, and protective measures prepared for the tone and language of the report’s audience

Read Use Cases for Security Copilot to delve deeper into the different security team roles like CISOs, threat intelligence analysts, IT admins, and the like, who can benefit from each highlighted use case. Designed with integration in mind, Security Copilot offers a standalone experience and also seamlessly integrates with products in the Microsoft Security portfolio. Security Copilot integrates with products such as Microsoft Defender XDR, Microsoft Sentinel, Microsoft Intune, Microsoft Entra, and other third-party services such as Red Canary and Jamf. For more information, see Security Copilot experiences.

 

Microsoft Defender for Cloud

Offers robust AI threat protection capabilities to secure generative AI applications against various cyber threats. It helps organizations identify and respond to security risks in real-time, leveraging Microsoft's threat intelligence and Azure AI Content Safety Prompt Shields. Key threats addressed include data leakage, data poisoning, credential theft, and jailbreak attacks. Core Capabilities of Defender for Cloud for AI Workloads includes:

1. Real-Time Threat Detection: Defender for Cloud continuously monitors AI workloads for threats such as:

• • • • Prompt injection attacks
      • Credential theft
      • Sensitive data exposure
      • Data leakage and poisoning: These threats are detected using Azure AI Content Safety Prompt Shields and Microsoft Threat Intelligence, which analyze both inputs and outputs of AI models

2.  Contextual Security Alerts: When a threat is detected:

• • • • Defender for Cloud generates context-rich alerts that include:
        • Malicious prompt evidence
        • IP addresses
        • Sensitive data types or credentials accessed
      • These alerts are automatically correlated into incidents within Microsoft Defender XDR or integrated into SIEM tools for investigation and response

3. Protection Across the AI Lifecycle: Defender for Cloud secures AI workloads from development to runtime:

• • • • AI Security Posture Management (AI-SPM) discovers misconfigurations and vulnerabilities in GenAI services, models, and pipelines.
      • It offers agentless scanning and attack path analysis to uncover direct and indirect risks

4. Built-In Safety with Azure AI

• • • • AI apps built with Azure AI Content Safety automatically block jailbreak attempts and malicious prompts.
      • Defender for Cloud ingests these detections and synthesizes them with threat intelligence to raise alerts

Example Scenario: A user inputs a malicious prompt into an app using Azure OpenAI:

1. 1. Azure AI Content Safety detects the jailbreak attempt and blocks the response.
   2. Defender for Cloud ingests this detection, combines it with app context and threat intelligence.
   3. A security alert is generated and sent to Defender XDR.
   4. The SOC team investigates and mitigates the threat using automated workflows

Defender for Cloud also supports Azure, AWS, and Google Cloud Platform and Models from providers like OpenAI, Meta, and Mistral. For more information, see Overview - AI threat protection - Microsoft Defender for Cloud | Microsoft Learn

Inappropriate Use of Personal Data

Microsoft Priva

A privacy management solution that works alongside Microsoft Purview to identify, mitigate, and monitor privacy risks across Microsoft 365 and multi-cloud environments. It offers the following set of solutions that support privacy operations across an organization's data landscape.

• • Privacy Risk Management (PRM): designed to proactively detect and remediate privacy risks such as data hoarding, oversharing, and unauthorized transfers. Its key capabilities include:

• • • Continuous Data Discovery: Automatically scans Exchange, SharePoint, Teams, and OneDrive to locate personal data like credit card numbers, addresses, and health information.
    • Policy-Based Risk Detection:
      • Data Minimization: Flags personal data that hasn’t been accessed or labeled for retention.
      • Data Overexposure: Detects excessive or idle access to sensitive data.
      • Cross-Border Transfers: Identifies personal data shared across departments, regions, or countries and enforces mitigation actions.
    • Custom Alerts: Admins can configure alerts for high-risk violations, such as large volumes of personal data or sensitive regulatory data being mishandled.
    • Employee Empowerment: Sends contextual privacy training and remediation guidance directly to employees involved in risky data handling.

• •  Subject Rights Requests (SRR): automates the discovery, review, and redaction of personal data to fulfill data subject requests under GDPR, CCPA, and other regulations. Its key capabilities include:

• • • Automated Data Discovery: Finds personal data across Microsoft 365 and flags conflicts like legal holds or confidentiality issues.
    • Built-in Redaction Tools: Enables secure review and redaction of sensitive content before sharing.
    • Secure Collaboration: Uses Microsoft Teams and other protected platforms for request fulfillment.
    • Audit-Ready Reporting: Tracks every step of the request lifecycle for compliance and transparency
  • Consent Management (preview): empowers organizations to effectively track consumer consent across their entire data estate, including structured, unstructured, and multicloud data. Consent management provides customizable consent models and a centralized process for publishing consent models at scale to multiple regions.
  • Privacy Assessments (preview): automates the discovery, documentation, and evaluation of personal data use across your entire data estate. Using this regulatory-independent solution, you can automate privacy assessments and build a complete compliance record for the responsible use of personal data.
  • Tracker Scanning (preview): empowers organizations to automate the identification of tracking technologies across multiple web properties, driving the efficient management of website privacy compliance. With Tracker Scanning you can automate scans for trackers, evaluate and manage web trackers, and streamline compliance reporting.

Together, these solutions help your organization:

• • Consolidate privacy protection across your data landscape.
  • Standardize compliance and streamline regulation adherence.
  • Encourage greater user confidence, accelerate digital transformation, and mitigate privacy risks.

Additionally, Microsoft Priva integrates seamlessly with Purview to apply sensitivity labels and data loss prevention (DLP) policies, classify sensitive information types (SITs) such as Social Security numbers, health records, and financial data, and enforce encryption and access controls across devices and applications. It also supports compliance with global privacy regulations—including GDPR, CCPA, LGPD, and PHIPA—and enables organizations to define roles and responsibilities for effective privacy governance.

Regulatory violations

Microsoft’s approach to AI governance is built on a layered ecosystem of tools, standards, and operational practices that span development, deployment, and oversight. This ecosystem is designed to help organizations meet global regulatory requirements while fostering innovation.

• Microsoft Purview Solutions Provides a unified platform to discover, protect, and govern data across AI applications, including Microsoft Copilot, Azure AI, and third-party generative AI tools like ChatGPT and Google Gemini.

• • Data Loss Prevention (DLP) prevents sensitive data from being exposed in AI interactions:
    • Prompt Protection: Automatically blocks or warns users when sensitive data (e.g., SSNs, credit card numbers) is pasted into AI prompts or responses.
    • Label Inheritance: AI-generated content inherits sensitivity labels from source documents, ensuring encryption and access controls persist throughout the AI lifecycle.
    • Cross-Platform Enforcement: DLP policies apply across Microsoft 365, Azure, and third-party apps like ChatGPT and Gemini, including browser-based interactions.

• • Communication Compliance uses machine learning to detect and respond to risky or non-compliant AI usage:

    • Classifier-Based Detection: Flags prompts that may lead to harassment, threats, or regulatory violations (e.g., SEC, FINRA).
    • Privacy by Design: Includes pseudonymization, role-based access controls, and audit logs to protect user privacy during investigations.
    • Adaptive Scopes: Enables targeted monitoring of specific departments, roles, or regions for AI-related risks.

• •  Audit & eDiscovery ensure traceability and legal defensibility of AI interactions:
    • Audit Logs: Capture detailed records of AI prompts, responses, and user actions for forensic analysis.
    • eDiscovery: Supports legal teams in identifying and reviewing AI-generated content as part of case workflows.
    • Lifecycle Management: Automates retention and deletion of AI data to reduce risk and storage costs.

• • Data Security Posture Management (DSPM) for AI provides visibility and control over AI data usage
    • Discovery & Classification: Identifies sensitive data in AI prompts and responses, including interactions with Microsoft Copilot and third-party agents.
    • Risk Assessments: Highlights oversharing risks and recommends remediation actions like access restrictions or policy updates.
    • One-Click Policies: Admins can deploy preconfigured policies to block risky behavior or enforce encryption instantly.
    • Cross-App Coverage: Supports Microsoft 365 Copilot, Security Copilot, Azure AI, ChatGPT, Gemini, and other AI platforms.

Limited visibility into AI components and vulnerabilities

• AI Security Posture Management (AI-SPM) in Microsoft Defender for Cloud

  Specialized capability within Microsoft Defender for Cloud that helps organizations secure generative AI workloads across multicloud environments including Azure, AWS, and Google Cloud. Its key Capabilities include:

  • AI Bill of Materials (AI BOM): Automatically discovers and maps AI components—models, data, APIs, and infrastructure—used across your cloud estate.
  • Attack Path Analysis: Identifies misconfigurations and vulnerabilities in AI pipelines, such as exposed endpoints, weak identity controls, or unpatched libraries (e.g., TensorFlow, PyTorch, Langchain).
  • IaC Misconfiguration Detection: Scans Infrastructure-as-Code templates for risky configurations that could expose AI services to unauthorized access.
  • Cross-Cloud Visibility: Provides unified insights into AI workloads across Azure OpenAI, Azure Machine Learning, Amazon Bedrock, and Google Vertex AI.

  This enables security teams to proactively reduce risk, remediate threats, and strengthen AI defenses from code to runtime.

  For more information, refer to Overview - AI security posture management - Microsoft Defender for Cloud | Microsoft Learn. 

 

• Data Security Posture Management (DSPM) for AI allows to discover, protect, and govern AI interactions across Microsoft Copilot, third-party AI apps, and enterprise-built agents

  • Unlabeled Data Discovery: Automatically scans SharePoint sites and other repositories accessed by Copilot to identify sensitive data that lacks classification or protection.
  • Oversharing Assessments: Evaluates AI prompts and responses to detect potential data leaks or unauthorized access to sensitive content.
  • Activity Explorer: Provides detailed logs of AI interactions, including who accessed what data, when, and through which AI app (e.g., Contoso Chatbot).
  • One-Click Policies: Enables rapid deployment of DLP and compliance controls to mitigate risks in AI usage.
  • Third-Party AI Governance: Extends protection to apps like ChatGPT, Gemini, and DeepSeek via browser DLP and endpoint policies.

  It helps organizations gain visibility into AI data flows, enforce compliance, and respond to incidents with precision.

Access and Control Challenges

Over-permissioned access to AI applications 

Microsoft’s ecosystem addresses these risks through a layered approach combining identity governance, data protection, threat detection, and responsible AI practices.

• Microsoft Entra Suite

Comprehensive identity and network access platform built to support Zero Trust security, identity governance, and secure collaboration across both internal workforce and external users. It enables organizations to securely connect any identity to any resource—cloud, on-premises, or hybrid—while maintaining strong security and compliance controls.

• • Microsoft Entra ID Protection: Provides real-time detection and mitigation of identity compromise using machine learning and behavioral analytics. It monitors risky sign-ins, flags suspicious activity, and integrates with Conditional Access to block or challenge access based on risk levels.
  • Microsoft Entra ID Governance: Automates identity lifecycle management, access reviews, and entitlement workflows. It ensures users have the right access at the right time and supports least-privilege principles across apps and services.
  • Microsoft Entra Verified ID: Enables organizations to issue and verify digital credentials using decentralized identity standards. It supports biometric validation (e.g., Face Check), secure onboarding, and credential portability across platforms.
  • Microsoft Entra Internet Access: Acts as a Secure Web Gateway (SWG) for SaaS and internet traffic. It filters malicious content, enforces compliance policies, and integrates with Microsoft Defender to protect users from threats while accessing external resources.
  • Microsoft Entra Private Access: Replaces traditional VPNs with identity-based access to on-premises and private cloud apps. It supports adaptive access policies, integrates with Conditional Access, and works across Windows, macOS, iOS, and Android.
  • Microsoft Entra External ID: A next-generation Customer Identity and Access Management (CIAM) platform designed to manage external identities such as customers, partners, and citizens. It supports:
    • Custom sign-up/sign-in flows with social logins, OTP, and email.
    • B2B collaboration with granular access controls and federation.
    • Security and lifecycle management for external users.
    • Multitenant architectures for hybrid CIAM and B2B scenarios.

For more information, refer to Microsoft Entra documentation | Microsoft Learn.

Vulnerabilities in the AI supply chain

Microsoft addresses supply chain risks through AI-powered insights, collaboration tools, and platform-level security.

• Dynamics 365 Copilot in Supply Chain Center

  • Flags external risks (e.g., weather, geopolitical events) using AI algorithms.

  • Generates predictive insights and automated supplier communications to mitigate disruptions.

For more details, refer to Copilot and generative AI in Dynamics 365 - Dynamics 365 | Microsoft Learn.

• Azure AI Foundry Agent Service

  • Supports multi-agent orchestration for complex supply chain tasks.

  • Enables continuous evaluation of agent performance and system health via Azure Monitor.

For more details, refer to Azure AI Foundry: Your AI App and agent factory | Microsoft Azure Blog.

• Microsoft Purview & Sentinel

  • Purview DSPM: Detects oversharing and applies policy-based protections in AI interactions.

  • Sentinel: Monitors threat intelligence pipelines and correlates alerts for supply chain-related incidents.

• Model Context Protocol (MCP)

  • Standardizes AI interactions with external systems and includes security controls to prevent prompt injection and token misuse.

For more details, refer to Introducing Model Context Protocol (MCP) in Copilot Studio: Simplified Integration with AI Apps and Agents | Microsoft Copilot Blog.

Malicious or compromised models

Microsoft Defender for Cloud Apps

Helps security teams monitor and control AI app usage & provides real-time threat detection for generative AI workloads:

• • Shadow AI Detection: Identifies unauthorized or unmanaged AI apps used within the organization.
  • App Risk Scoring: Evaluates AI apps for security posture and flags high-risk behavior.
  • App Blocking: Allows admins to block access to malicious or non-compliant GenAI apps.
  • Prompt Injection Detection: Uses Azure AI Content Safety and Prompt Shields to block malicious prompts before they reach the model.
  • Security Alerts: Flags threats like data leakage, model poisoning, jailbreaks, and credential theft.
  • Defender XDR Integration: Correlates AI alerts with broader security incidents for full attack visibility.

Azure AI Studio

• • Prompt Shields: Detect and block prompt injection attacks, including indirect attacks hidden in documents or emails.
  • Safety Evaluations: Simulate adversarial prompts to assess model vulnerability.
  • Groundedness Detection: Identifies hallucinations and ensures outputs are based on trusted data.
  • Offers various Model Safety & Evaluation tools to test and secure models:

For more information, refer to Announcing new tools in Azure AI to help you build more secure and trustworthy generative AI applications | Microsoft Azure Blog.

Responsible AI Toolbox

An open-source framework that integrates multiple mature tools into a unified dashboard. It enables users to identify, diagnose, and mitigate risks in AI models and make informed decisions about their deployment. Key Components and Capabilities include:

• • Fairness & Explainability: Diagnose bias and understand model decisions.
  • Counterfactuals & Causal Analysis: Explore how small changes affect predictions.
  • Error Analysis: Identify high-error cohorts and improve model reliability.

These tools help developers audit models for malicious behavior and unintended risks.

For more information, refer to the Responsible AI Toolbox Overview.