AI Vendor Evaluation Guide

A comprehensive framework for evaluating AI solutions with critical questions and security considerations

Purpose of This Guide

This guide provides a structured approach to evaluating AI vendors and their proposed solutions. It addresses two critical areas:

MCP Server Security Considerations: Understanding the technical and operational requirements for Model Context Protocol implementations
Vendor Evaluation Questions: Comprehensive due diligence questions to assess any AI solution provider

Thorough vendor evaluation benefits both parties—it helps you make informed decisions about AI adoption, and it helps vendors understand your specific requirements and constraints. Use this guide to ensure comprehensive assessment and alignment between your needs and vendor capabilities.

Technical Considerations: MCP (Model Context Protocol) Servers

Executive Summary

MCP (Model Context Protocol) servers, while designed to simplify AI development and agent orchestration, introduce substantial technical and security requirements that need careful assessment before implementation. These considerations stem from the protocol's ability to grant AI agents access to sensitive data and tools, creating security and operational responsibilities that extend beyond traditional application deployments.

Core Security and Technical Risks

1. Prompt Injection

Malicious prompts injected into data processed by the MCP server can manipulate AI behavior, potentially leading to unintended actions, data leakage, or system compromise. Mitigation requires input validation, sandboxing, and careful prompt design.

2. Tool Poisoning

Vulnerabilities in tools accessed by the MCP server can be exploited as attack vectors to compromise the server or connected systems. Each tool integration becomes a potential security surface requiring validation and monitoring.

3. Privilege Escalation

AI agents operating with elevated privileges create risk if the MCP server is compromised, potentially allowing unauthorized access escalation. Proper privilege scoping and least-privilege principles are essential.

4. Data Exposure

MCP servers require access to data for AI processing. Without proper security controls, sensitive information could be exposed to unauthorized parties, leading to data breaches and compliance violations.

5. Supply Chain Risks

MCP servers that integrate with multiple third-party services create dependencies that must be secured and monitored. Each integration point requires vetting and ongoing security assessment.

6. Observability Requirements

Without proper monitoring and logging, detecting and responding to security incidents becomes difficult. MCP deployments require comprehensive observability infrastructure for audit trails and anomaly detection.

7. Access Control Complexity

MCP implementations need robust mechanisms to restrict AI access to tools and data based on context and user permissions. This requires careful architecture and ongoing governance.

8. Source Verification

MCP servers and their components must be verified for security vulnerabilities before deployment. This includes vetting third-party integrations and maintaining security updates.

Additional Strategic Considerations

Standards Evolution: The MCP concept is still evolving without universal implementation standards. This creates challenges around interoperability, security practices, and long-term compatibility that must be evaluated.
Data Governance & Residency: AI agent deployments via MCP servers can result in data movement across regions or into cloud services. Ensuring alignment with data residency requirements (PCI, HIPAA, GDPR) requires careful architecture and policy enforcement.
Identity & Access Management: MCP servers orchestrate identity and access to tools on behalf of AI agents. Proper identity scoping, authentication, and authorization mechanisms are critical to prevent impersonation or unauthorized access.
Agent Behavior Management: MCP deployments can enable recursive or unpredictable AI behaviors if not properly scoped and monitored. Safeguards, circuit breakers, and monitoring are necessary to prevent runaway automation.
Third-Party Dependencies: MCP servers often delegate actions to third-party APIs and tools. Each dependency requires security assessment, monitoring, and contingency planning for failures.
Vendor Architecture Transparency: Understanding how MCP vendors implement their systems is important for assessing long-term viability, migration paths, and operational requirements. Ensure visibility into system architecture and AI decision-making processes.
Explainability & Audit Requirements: AI agents acting through MCP systems make decisions based on prompt context and tool outputs. Tracing decision paths for audit, compliance, or troubleshooting requires comprehensive logging and explainability features.

Assessment Requirement

MCP servers function as orchestration layers for autonomous agents with significant access to systems and data. Successful deployment requires careful architectural planning, security controls, and ongoing governance. Thorough technical assessment and vendor evaluation are essential before adopting any MCP-based system.

Required Internal Capabilities and Expertise

Successfully deploying and managing an MCP server requires cross-functional capabilities with strong security, development, and AI governance expertise. The oversight extends beyond initial implementation to include continuous monitoring, threat assessment, policy enforcement, and incident response.

Core Capabilities and Roles

AI Systems Architect: Design secure integration pathways between AI agents, internal systems, and third-party tools.
Cybersecurity Analyst: Evaluate threat vectors such as prompt injection, privilege misuse, and tool abuse. Monitor security posture continuously.
DevSecOps Engineer: Build secure CI/CD pipelines and enforce containerization, access controls, and observability standards.
Software Developer (API/Enterprise Integration): Ensure secure and reliable connections between the MCP server and core business systems like ERP or CRM.
Compliance Officer: Monitor and enforce regulatory alignment (e.g., PCI, HIPAA, GDPR) when AI agents process sensitive business data.
AI/ML Governance Lead: Define acceptable use boundaries, oversee agent behavior policies, and manage data scope and retention policies.

Note: These capabilities may be distributed across existing staff, require new hires, or be provided through vendor partnerships. Organizations should assess whether current resources can support these requirements or if additional investment is needed.

Staffing and Resource Considerations

MCP server infrastructure—particularly self-hosted implementations—introduces operational and security responsibilities including agent governance, secure tool orchestration, compliance management, and AI-specific behavior monitoring.

Organizations should assess whether current IT capacity and expertise can manage these responsibilities. The technical scope and security exposure may require:

Addition of 1–2 dedicated personnel with AI governance, cybersecurity, compliance, and DevSecOps expertise
Contracted expertise or managed services for specialized AI security oversight
Investment in training existing staff on AI-specific security and governance practices

Understanding these resource requirements is essential for evaluating the total cost and feasibility of MCP technology deployment.

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Comprehensive Vendor Evaluation Questions

Use these questions to conduct thorough due diligence on any AI vendor. These questions help you understand the true capabilities, costs, implementation requirements, and operational characteristics of proposed AI solutions.

Technical Details

What is the underlying technology stack, including AI frameworks and cloud infrastructure?
What measures are in place to ensure the security and privacy of our data? Are you compliant with regulations such as GDPR, CCPA, or other applicable data protection laws?
You mentioned that you support SSO. Please provide details on the supported protocols (SAML, OAuth, OIDC).
How does your solution handle data ingestion and processing from multiple sources?
Is your solution available as an on-premises, cloud-based, or hybrid deployment?
If our deployment is on-premises and we experience an internet outage, can we still process critical business operations? What happens if there is an outage while users are logged into your SaaS interface and our internal systems?
How does your solution integrate with our enterprise systems (ERP, CRM, etc.)? Do you use middleware, APIs, or data import/export services?
What is the most current version of our enterprise system that your solution supports, and how do you ensure compatibility with frequent system updates and releases?
What specific database tables, views, or data structures does your solution require access to?
Are there any specific system configurations or required fields that need to be set up for your solution to function properly? If so, please specify which fields should be configured.
What version of database technology (SQL, etc.) does your solution require, and what level of access do you need?
For any proof-of-concept import request, please provide sample data files with the expected layout and format specifications.
Please provide detail on any document processing workflows (e.g., purchase orders, invoices). How do documents get transferred to your system? Are we expected to forward documents manually, or is there an automated integration?

Performance and Scalability

How does your solution scale with increased data volume and user load?
What are the system performance benchmarks under heavy usage?
Do you offer performance guarantees or SLAs related to uptime, response times, or transaction processing speed?

Customization and Flexibility

Can your solution be customized to meet our specific business needs and workflows?
How configurable is the AI model? Can we adjust its parameters, or must we rely on your team for modifications?
Does your system support custom rule creation for AI-driven automation, or does it operate as a black-box model?

AI Model Training and Accuracy

Is your AI model pre-trained, or does it require training on our historical data?
If training is required, how long does it take to achieve accurate predictions?
Can we fine-tune the AI model based on our own datasets? If so, what expertise is required on our end?
What accuracy rates have you observed in production environments, and how do you handle false positives/negatives in AI predictions?

Integration and Data Flow

How frequently does your system sync data with our enterprise systems (real-time, batch processing, scheduled updates)?
Does your system introduce any additional latency to our existing workflows?

Data Security and Privacy

What specific security controls protect our data from unauthorized access, modification, or deletion?
Where exactly is our data stored and who has access to it? Specifically: (a) What are the geographic locations where data resides? (b) Does data ever leave our specified jurisdiction? (c) Is data shared with third parties, parent companies, affiliates, or technology partners? Please provide complete disclosure.
What encryption standards do you use for data at rest and in transit?
How is access to customer data managed and monitored within your solution?
Can you provide details on your incident response plan in the event of a data breach?
Do you conduct regular security audits or vulnerability assessments? If so, can clients review the results?
Given recent concerns about AI security and data privacy in the news, what specific measures does your company take to protect against unauthorized access or data misuse?
Can you provide information about your company's ownership structure, key stakeholders, and any relationships with foreign technology companies or entities?

Compliance and Legal Considerations

Do you provide a Data Processing Agreement (DPA) outlining responsibilities for data protection?
In case of vendor failure, how do we retain access to our data and AI models?
Do you have liability coverage for data breaches or system failures that may impact our operations?

Functionality and Benefits

What specific functionalities does your solution provide, such as predictive analytics, process automation, or anomaly detection? Please elaborate in detail.
Can you provide examples or case studies that demonstrate how your solution has improved operational efficiency for other clients in our industry?

Support and Training

What is the typical implementation timeline, and what support do you provide during the process?
Where are your support and implementation teams based? Is support available 24/7?
Do you offer training for our team, and is it included in the pricing?
What support channels do you offer (e.g., live chat, email, phone), and what are your response times?

Cost and ROI

Please provide your complete pricing structure (e.g., licensing, subscription, usage-based, transaction-based, per-user, monthly, yearly).
What is the average total financial commitment for implementing your solution (please provide specific dollar ranges)?
Are we locked into a contract? If so, what is the contract term, and is there a cancellation fee?
Are there any additional costs, such as setup fees, ongoing maintenance, updates, customization requests, or after-hours support?
How do you determine and measure ROI for your solution?

Hidden Costs & Future Scalability

Are there any API call limits, data storage restrictions, or usage-based fees that could increase costs over time?
How does pricing change as our data volume, transactions, or users scale?
If we need additional customizations or integrations in the future, how are those priced?

Competitor Differentiation

What differentiates your AI solution from competitors offering similar enterprise integrations?
What are the most common reasons for potential customers choosing not to proceed with your solution?

Stability & Reputation

How long has your company been in business, and how many clients are currently using your solution?
Have there been any significant leadership changes, mergers, or acquisitions in the past three years that may impact product development or support?

AI Transparency & Explainability

How does your AI make decisions? Can you provide transparency into the model's reasoning and outputs?
Do you offer an audit trail or logging for AI-generated decisions, especially in cases where human intervention is needed?
What methods are available to correct inaccurate AI outputs, and how does the system improve over time?

Disaster Recovery & Business Continuity

What happens if your company goes out of business? Do you provide source code escrow or a contingency plan?
What is your disaster recovery strategy in case of a system failure or data center outage?
Can you provide documentation on system uptime and availability from existing clients?

Long-Term Viability

What is your product roadmap, and how do you plan to evolve the solution in the future?
Do you assign a dedicated account manager or customer success representative?
Do you have partnerships with other technology providers or enterprise system vendors? If so, please list them.
Please provide references and testimonials from clients in our industry who have successfully implemented your solution. We would like to contact them prior to further discussion.

Send all questions and MCP considerations to your vendor:

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