How MSPs should evaluate AI security

AI is already incorporated into most of your clients’ workflows. Employees are using chatbots and other built-in GenAI tools to draft emails, analyze data and automate work. The challenge? Much of that activity is happening outside your formal security controls, and that creates a new risk layer. 

For managed service providers (MSPs), the question is no longer whether to secure AI adoption for their clients, but how to evaluate the right AI security solution. You need a solution that works across multiple clients, supports your business model and addresses emerging threats, including prompt injections and shadow AI. This article introduces a practical framework to help MSPs compare AI security solutions with confidence. 

Traditional security tools weren’t built for the way people use GenAI today. Data loss prevention (DLP), identity access management (IAM) and endpoint protection still matter, but when these systems are siloed, they leave some of the biggest AI-specific risks exposed: 

• Employees pasting sensitive data into ChatGPT or Copilot. 

• Unsanctioned “shadow AI” tools running across client environments. 

• Prompt injection attacks that manipulate AI outputs. 

• Lack of visibility into siloed AI interactions and workflows. 

In most cases, MSPs can get by with fragmented DLP, IAM and endpoint protection tools, but the tools require complex, heavy integration. 

AI security is a solution designed to monitor, control and protect how AI is used across an organization. Depending on the use case, this can include: 

• End-user customer businesses (consumers): Securing how employees use GenAI tools such as ChatGPT or Copilot, including prompts, data inputs, outputs and user access. 

• Builders: Protecting AI integrations and workflows built on platforms like OpenAI, Microsoft or AWS APIs. 

• Developers: Securing AI models and infrastructure, including training data, model integrity and application vulnerabilities. 

While these use cases span different layers of AI adoption, most MSPs focus on the consumer and builder scenarios. In consumer scenarios, managing how end users interact with AI tools is critical for preventing data leakage, ensuring compliance and enabling safe AI adoption. In builder scenarios, AI is integrated into applications, workflows and services. 

For most MSPs, it’s a matter of striking a balance between the services your business and clients demand while keeping costs low and reducing complexity. This is where many solutions fall short. They are often built for enterprise security teams but not MSP business models. 

Where Acronis GenAI Protection shines are cost and ease of management, specifically in consumer and builder scenarios. The solution gives service providers simple, integrated controls to secure their clients’ expanding GenAI environments.  

Acronis GenAI Protection takes an endpoint-first approach, enabling MSPs to securely support both consumer and builder AI use cases by providing improved visibility, control and protection across client endpoint environments:  

• The solution discovers and monitors GenAI applications, including shadow AI tools and user behavior across siloed systems, while enabling reporting and policy enforcement.  

• It protects sensitive data by inspecting prompts for confidential information and preventing unauthorized transfers to unsanctioned AI tools with flexible detect or block controls. 

• It defends against AI threats by detecting and blocking harmful prompts and prompt injection attacks, ensuring safer AI interactions and reducing the risk of misuse or unintended outcomes, helping MSPs drive safe AI adoption with greater confidence and governance. 

Also, six other things you’ll want to consider: 

Many AI security solutions are built for single organizations, not MSPs managing multiple client environments. For MSPs, operational efficiency and scalability are critical. 

Consider: 

• A unified console across all tenants. 

• Flexible policy customization (client, environment or department level). 

• Ease of policy creation and ongoing management. 

• Seamless onboarding and integration with your existing stack. 

Data protection remains the foundation of AI security. As AI becomes embedded into applications and services, this extends beyond traditional monitoring to how data flows through prompts, APIs and AI-driven workflows. 

Consider: 

• How is sensitive data identified and protected across AI interactions and integrations? 

• Can policies be applied consistently across clients and use cases? 

• How scalable and manageable are these controls across environments? 

For MSPs supporting AI-driven services, visibility is less about discovering every tool and more about understanding how AI is used within workflows, applications and environments. 

Consider how the solution: 

• Provides visibility into AI-enabled tools, integrations and usage patterns. 

• Enables monitoring across environments for investigation and auditing. 

• Supports actionable insights that can be operationalized.  

AI security is not just a risk — it is also a new revenue opportunity. These solutions should support MSPs in delivering AI security as a scalable, client-facing service. 

Consider a solution that: 

• Offers AI security as a branded service. 

• Maintains flexible pricing and margins. 

• Enables you to scale services without heavy operational overhead. 

AI introduces new governance and compliance challenges, especially when embedded into client-facing services and workflows. Controls must remain enforceable while scaling across environments. 

Consider: 

• Role-based access control for AI services and integrations. 

• Audit logs for AI interactions and activity. 

• Alignment with frameworks such as NIST AI RMF, EU AI Act or ISO 42001. 

As AI is integrated into workflows and applications, input-related risks such as prompt injection and misuse become more relevant. 

When evaluating solutions, consider: 

• How are suspicious or harmful inputs identified? 

• Is there visibility into prompts and outputs for monitoring and auditing? 

• How well do these controls integrate into broader security workflows? 

AI adoption is accelerating faster than most security strategies can keep up. For MSPs, this gap creates both risk and opportunity. In the end, securing AI is about control, data protection and scalable risk management. MSPs that simplify this will deliver more value and stay ahead. 

Acronis GenAI Protection helps MSPs close that gap by bringing better control, visibility and protection to how AI is used across client endpoint environments. Built natively into the Acronis platform, it enables MSPs to monitor GenAI usage, detect shadow AI, block sensitive data from being shared with public AI tools and prevent harmful misuse of prompts.  

Acronis GenAI Protection enables MSPs to:  

• Reduce data leakage risks, strengthen compliance and protect against emerging AI-driven threats.  

• Turn AI security into a scalable, client-facing service. 

• Help support safe AI adoption for their clients. 

• Expand their own service offerings in new markets.  

Securing your clients starts with endpoints in an AI era, and protection boils down to securing how AI is used, how data flows through it and how your MSP business brings risk under control.