How to Build a Risk Assessment Service for AI Readiness: An MSP Guide

As generative AI (GenAI) continues to become more integrated into daily business operations, ensuring robust data protection and governance is critical. According to Omdia, 94% of managed service providers (MSPs) indicate commitment to automation around AI data readiness and compliance services, only 43% report having reached a high level of maturity in delivering AI-ready data environments. These statistics underscore the significant impact that effective data management practices can have on operational efficiency and data protection.

This landscape presents an opportunity for MSPs to provide a comprehensive risk assessment service that can unlock numerous benefits:

• Enhanced client trust. Demonstrating a commitment to data protection and governance builds trust and positions you as a reliable advisor.
• Revenue opportunities. Delivering data governance services as part of AI readiness engagements can open new, repeatable service opportunities.
• Operational efficiency. Implementing automated tools and processes reduces manual workload, increases accuracy, and enhances overall efficiency.
• Competitive differentiation. Offering specialized services like AI readiness through continuous data governance sets you apart from competitors.

Take the next steps to position yourself as a trusted data security advisor and help your clients prepare for AI like Microsoft 365 Copilot.

7 Key Elements for a Scalable Risk Assessment Service

Regulatory demands like the Health Insurance Portability and Accountability Act (HIPAA) and GDPR require continuous control, but compliance can feel overwhelming. Manual evidence collection, exception handling, and policy enforcement drain resources and distract from strategic priorities. In fact, 85% of business leaders say compliance has become more complex over the past three years, and 82% say it distracts from what matters most.  

Here are elements to help in building a risk assessment service:

1. Comprehensive Data Inventory

Every effective risk assessment begins with visibility. Start by building a detailed inventory of data across the customer’s environment.

• Identify where data resides (SharePoint, Teams, OneDrive, Exchange, third-party apps).
• Map who has access (internal users, groups, guests, external domains).
• Understand how data is used and shared (internal vs external, anonymous links, public teams).

This inventory is essential to uncover overexposed content, shadow IT, and compliance gaps before AI adoption amplifies existing risks.

Read this report to help your clients unlock AI opportunities through governance.  

2. Initial Risk Assessment

Once you understand the data landscape, run a structured AI readiness risk assessment. This process involves:

• Scanning the Microsoft 365 environment. Use centralized, multitenant visibility to assess the client’s Microsoft 365 environment at scale. This helps surface common governance and security risks, including oversharing external access, and sensitive content exposure.
• Generating a detailed risk report. Based on the scan, generate a comprehensive risk report that highlights key issues and gaps in the client’s data security posture. This report should categorize risks by severity and provide a clear overview of the current state of data governance.  
• Client consultation. Use the assessment findings to align priority risks, establish a remediation roadmap, and set a baseline for measuring improvement over time.  

3. Risk Remediation and Automation

Once the initial assessment is complete, the next step is to remediate the identified risks. This phase involves several key activities:

• Manual and automated fixes. Address the issues highlighted in the risk report using a combination of manual interventions and automated tools. Baseline management can define, enforce, and restore secure Microsoft 365 standards at scale and automate policy rules to limit oversharing.
• Utilizing additional tools. Depending on the specific issues outlined in the report, it may require additional technology. For instance, data migration and consolidation are necessary to ensure that data is stored in the appropriate locations with the correct permissions. Information lifecycle management can help archive outdated information and ensure compliance with data retention policies.
• Adjusting Microsoft 365 settings. Work with the client to review and adjust their Microsoft 365 settings to align with their desired security and compliance strategy. This may include configuring security groups, setting up conditional access policies, and enabling multi-factor authentication to enhance overall security.

4. Policy and Compliance Enforcement

Effective information management is a critical component of data protection and AI readiness. In this phase, MSPs should focus on:

• Implementing sensitivity labels. Deploy sensitivity labels within Microsoft 365 to classify and protect sensitive information. Sensitivity labels allow clients to control access to their data based on the level of sensitivity, ensuring that only authorized users can view or modify the content. AI-powered data classification can help remove the burden of manual data classification on employees, which could take months, if not years.
• Applying labels to data. Collaborate with clients to apply these sensitivity labels to their existing data. This process may involve bulk applying labels to large datasets and educating users on how to manually label new content as it is created.  
• Training and education. Educate clients on the importance of sensitivity labels and how they affect AI’s behavior. Sensitivity labels help inform how data is accessed and handled within Microsoft 365, supporting more consistent policy applications as AI tools surface and summarize content.  

5. Change Management Workflows

Risk assessment should not be a once-a-year exercise. To keep environments AI-ready, you need continuous oversight through:

• Automated monitoring and alerts. Permissions and sharing settings, creation of public teams, groups, or sites, and deviations from your defined configuration baselines. Continuous visibility ensures nothing slips through the cracks as environments evolve.
• Policy and baseline tools deliver enforcement at scale. They maintain standardized tenant configurations, send real-time drift alerts to both you and the client, and automatically roll back risky changes.

This automation cuts manual workload, enables near real-time risk mitigation, and makes your service scalable and profitable across multiple tenants.

6. Centralized Visibility and Compliance Oversight

To scale AI readiness and risk assessment services across multiple customers, MSPs need centralized, multitenant visibility that enables them to:

• Monitor risk across tenants. Gain visibility on oversharing, access risks, and policy violations from a single dashboard; compare security and governance posture across customers to identify common patterns and standardize remediation at scale.  
• Actionable compliance tracking and prioritization. Track remediation progress over time using tenant-level risk scores and trend reporting, with consolidated alerts that help MSPs prioritize and resolve the highest risk issues first across their entire customer portfolio. This centralized oversight enables MSPs to schedule regular audits to ensure ongoing compliance with data governance policies and regulations. Continuous evaluation helps identify new risks and ensures that data protection practices evolve with changing regulatory landscapes.

7. Ongoing Managed Services

The final step in building a risk assessment service is to provide ongoing managed services. This ensures that clients maintain a strong data security posture over time. Key activities in this phase include:

• Continuous monitoring. You can use AvePoint Confidence Platform - Elements Edition to centrally monitor the client’s Microsoft 365 environment for new risks or compliance issues. Set up alerts and notifications to promptly address any emerging threats.
• Regular reporting. Provide clients with regular reports on their data security status. These reports should include insights into policy compliance, user behavior, and any corrective actions taken. Regular reporting helps clients stay informed about their security posture and the effectiveness of the implemented measures.
• Proactive auditing. Conduct periodic audits to verify that the client’s data security policies are being followed. This proactive approach helps identify potential weaknesses before they become significant issues.
• Consultative support. Offer consultative support to help clients navigate complex data protection challenges. This may include advising on best practices, assisting with policy development, and providing guidance on new security features or updates in Microsoft 365.

Bringing It All Together

AI integration presents a transformative opportunity for businesses. However, ensuring data management practices are secure and compliant is paramount. By building a risk assessment service, MSPs can position themselves as indispensable partners in their clients’ AI journey.

Don’t miss out on this opportunity to elevate your MSP business.