This post continues from part 1 of our blog series, which examined AI governance in 2025 and highlighted key trends to watch for in 2026.
While regulations like the EU AI Act and frameworks like the NIST AI RMF are establishing a foundation for AI governance, they can’t keep pace with the speed of AI adoption, or the risks that come with it. Data leakage, biased decision-making, intellectual property exposure, and unvetted third-party tools are just a few of the urgent threats created by uncontrolled AI adoption, especially within small and mid-sized businesses.
For MSPs, this gap between regulation and reality creates a powerful opportunity. Waiting for compliance mandates is a reactive posture that leaves your clients exposed. Instead, you can fill the void by becoming a proactive advisor, guiding clients through the complexities of AI risk now. By shifting from a compliance-first mindset to a risk-first strategy, you can differentiate your services, deepen client trust, and build a scalable, high-value AI governance offering.
Implement a Clear AI Usage Policy
The first step in managing AI risk is establishing clear rules of engagement. Many employees are already using generative AI tools like ChatGPT with or without official approval, sometimes inputting sensitive company or client data into public models. Without a policy, you have no control.
Help your clients develop an Acceptable Use Policy (AUP) for AI that provides practical and scalable guidelines. This is analogous to the early days of Bring Your Own Device (BYOD) governance, where the goal was to enable productivity while mitigating risk. Your policy should define:
- Approved vs. Restricted AI Tools: Create a list of sanctioned AI applications that have been vetted for security and data privacy. Prohibit the use of unapproved tools for business purposes.
- Data Handling Protocols: Explicitly state what types of data can and cannot be used with AI tools. Forbid the input of Personally Identifiable Information (PII), client data, intellectual property, or other sensitive information into public AI models.
- Human Review Requirements: Mandate human oversight for any AI-generated output used in critical decision-making, external communications, or client-facing deliverables.
- Documentation Standards: Require employees to document when and how AI was used to generate content or support a decision, ensuring transparency and accountability.
By helping clients build and implement a practical AI policy, you provide immediate value and establish a foundation for more advanced governance.
Integrate AI Risk into Standard Assessments
AI is not a separate category of risk but an extension of your existing cybersecurity environment. Integrating AI-related checks into your standard client risk assessments makes risk management more efficient and provides clients with a holistic view of their threat landscape. This approach ensures AI is treated as a core component of the overall security posture, not an afterthought.
Your updated assessment process should include:
- Inventory All AI Assets: Go beyond obvious tools like chatbots. Identify AI-powered features embedded in CRMs, marketing automation platforms, security tools, and other SaaS applications. Map out APIs, custom models, and any other form of AI in the environment.
- Identify and Categorize Risks: For each AI asset, evaluate its associated risks. Use categories from the NIST AI RMF, such as privacy, bias, reliability, and explainability, to structure your analysis. Consider vendor dependencies and the potential impact of model failure or manipulation.
- Prioritize Controls: Use the assessment findings to prioritize the implementation of controls. The NIST AI RMF serves as a practical checklist for evaluating and selecting appropriate safeguards based on risk level.
- Schedule Periodic Reviews: AI models and their associated risks evolve. Establish a cadence for reviewing AI assets and updating risk assessments to ensure governance keeps pace with technological change.
Manage Third-Party and Supply Chain AI Risk
One of the greatest AI-related exposures for your clients comes from their vendors. Many SaaS platforms are embedding generative AI features into their products, often without explicit transparency about how customer data is used. This introduces significant supply chain risk that must be managed through an expanded Third-Party Risk Management (TPRM) program.
Update your vendor due diligence questionnaires to include AI-specific inquiries:
- Does the vendor use customer data to train its AI models? If so, is there an opt-out?
- How does the vendor handle AI model change management and testing?
- What data segregation and privacy controls are in place for AI-processed information?
- What is the vendor’s incident response protocol for AI-related failures, such as model hallucinations or data leakage?
For MSPs, managing this manually across dozens of vendors for each client is not scalable. This is where a dedicated platform becomes essential. Cynomi’s TPRM capabilities allow you to automate vendor assessments, reuse due diligence data across your client base, and even assign real-time risk scores to AI vendors, streamlining the entire process.
Prepare for AI-Related Incidents
AI systems introduce new and unfamiliar failure modes that traditional incident response (IR) plans may not cover. A compromised AI model can produce biased outputs, a large language model can leak sensitive data from its training set, and a prompt injection attack can cause an AI agent to perform malicious actions.
MSPs must extend their IR plans to address these unique scenarios. Your AI incident playbook should include procedures for:
- Issuing Data Removal Requests: Know how to formally request that AI providers delete sensitive client data that may have been inadvertently submitted.
- Containing AI Systems: Establish a process for quickly pulling a compromised or malfunctioning AI system offline to prevent further damage.
- Communicating AI-Driven Errors: Prepare communication templates for transparently notifying stakeholders and clients about errors or breaches caused by an AI system.
By developing these capabilities, you can offer a powerful differentiator: “We don’t just respond to cyber incidents, we respond to AI incidents, too.” This demonstrates a forward-thinking approach that builds immense client confidence.
Educate and Empower Your Clients
Your role extends beyond simply implementing technical controls. To be a true strategic advisor, you must educate clients on the evolving nature of AI risk. Many business leaders are enthusiastic about AI’s potential but unaware of its pitfalls.
Position your team as educators by:
- Offering Client Briefings: Host regular webinars or include a dedicated section in your reports on AI best practices and emerging threats.
- Translating Technical Concepts: Explain complex AI threats like prompt injection, model theft, and agentic autonomy in plain language that business leaders can understand.
- Demonstrating Business Impact: Connect AI risks to tangible business outcomes, such as reputational damage, regulatory fines, or loss of competitive advantage.
Educating clients builds credibility and transforms your relationship from that of a vendor to a trusted partner. It reinforces the value of your advisory service and positions you as an indispensable guide in the age of AI.
Turn Proactive Governance into a Service
Proactive AI risk management can be a significant revenue opportunity. By formalizing your approach, you can create a scalable, high margin “AI Risk Management” offering. This package can be a standalone service or a premium tier of existing advisory, risk management, or cybersecurity management offerings.
A comprehensive service could include:
- AI Policy Development and Implementation
- Quarterly AI Risk and Inventory Reviews
- Continuous Third-Party AI Vendor Risk Tracking
- AI Incident Response Planning and Testing
- Automated Evidence Collection for Audit Readiness
Platforms like Cynomi are designed to help you operationalize and scale these services. By leveraging automation for assessments, policy generation, workflows, and client reporting, you can deliver consistent, high-impact AI governance without adding headcount. This allows you to efficiently manage more clients, boost productivity, and establish a clear competitive advantage in a crowded MSP market.
The time to act is now. For additional strategies, see our blog on Navigating the New Frontier: AI Security Frameworks for MSPs and MSSPs.
