The Enterprise Guide to AI Vendor Due Diligence and Risk Mitigation

Artificial intelligence is now embedded in core enterprise operations, from decision automation to proprietary model development. While AI delivers measurable efficiency and competitive advantage, it also introduces new categories of vendor risk that traditional procurement and legal frameworks were not designed to address.

For enterprise buyers, AI vendor due diligence must go beyond standard financial and security reviews. It requires deeper visibility into model ownership, source code dependencies, data integrity, and long term operational viability. This guide outlines how organizations can approach AI vendor due diligence with a risk management mindset and where escrow and verification services play a critical role.

Why AI Vendor Risk Is Different

AI vendors operate under a fundamentally different risk profile than traditional software providers. Many rely on proprietary models, complex training pipelines, third party data sources, and rapidly evolving infrastructure. If a vendor fails, is acquired, or materially changes its business, enterprises may lose access to systems that are deeply embedded in mission critical workflows.

Key AI specific risk factors include:

• Lack of transparency into model architecture and training data
• Dependency on specialized talent rather than documented processes
• Rapid iteration cycles that outpace governance controls
• Unclear intellectual property ownership of models and outputs
• Vendor reliance on external APIs or cloud platforms

Without structured safeguards, these risks can translate into operational disruption, regulatory exposure, and loss of strategic IP.

Core Due Diligence Areas for AI Vendors

Enterprise buyers should evaluate AI vendors across several critical dimensions.

Intellectual Property and Ownership

Confirm who owns the AI models, training data, and derivative works. Many AI agreements contain ambiguous language around model improvements and output rights. Enterprises should ensure they have clear contractual protections and contingency access if the vendor can no longer perform.

Technology escrow, including AI specific escrow structures, provides a mechanism to secure critical assets such as source code, model weights, and documentation. Learn more about PRAXIS escrow solutions here.

Operational Continuity

AI vendors often depend on a small group of engineers or data scientists. If key personnel leave, the ability to maintain or support the platform may degrade quickly. Enterprises should assess documentation quality, knowledge transfer practices, and business continuity planning.

Escrow combined with verification services helps validate that deposited materials are complete, usable, and aligned with contractual obligations. PRAXIS verification services are designed to support these continuity objectives. 

Data Governance and Compliance

AI systems frequently process sensitive or regulated data. Enterprises must understand how data is collected, stored, used for training, and protected. This includes evaluating compliance with industry regulations and internal data governance policies.

Due diligence should also assess how model updates affect data usage over time, particularly for regulated industries.

Technical Maturity and Transparency

Not all AI vendors operate at the same level of engineering rigor. Buyers should evaluate whether the vendor can demonstrate repeatable deployment processes, version control, testing frameworks, and rollback capabilities.

Independent technical verification provides assurance that what is contractually promised is what has actually been delivered. PRAXIS supports enterprise buyers through structured technical verification programs.

The Role of Escrow in AI Risk Management

Escrow is no longer just a bankruptcy safeguard. For AI and emerging technology, escrow functions as a governance and risk mitigation tool.

Modern escrow arrangements can include:

• Source code and model escrow
• Data schemas and training artifacts
• Deployment scripts and infrastructure documentation
• Release conditions aligned to AI specific failure scenarios

Automated Escrow™ further streamlines deposit management, change tracking, and compliance monitoring for vendors with frequent updates. This is especially valuable for AI platforms that evolve continuously.

Learn more about how AI escrow is structured here.

Aligning Procurement, Legal, and Technology Teams

Effective AI vendor due diligence requires cross functional alignment. Procurement teams focus on commercial risk, legal teams address contractual exposure, and technology leaders assess architectural soundness. Escrow and verification services serve as a neutral foundation that supports all three perspectives with objective evidence and enforceable safeguards.

By embedding escrow requirements early in the vendor selection process, enterprises avoid costly retrofits and strengthen their negotiating position.

Building a Repeatable Due Diligence Framework

Enterprise buyers should treat AI vendor evaluation as an ongoing lifecycle, not a one time event. A mature framework includes:

• Initial technical and legal due diligence
• Escrow and verification onboarding
• Periodic verification and audit checkpoints
• Clear exit and transition strategies

PRAXIS Technology Escrow supports enterprises across each stage of this lifecycle, helping organizations adopt AI with confidence while maintaining control over critical assets.