Enterprise AI Governance Best Practices

Why Vendor and Third-Party AI Requires Governance

Enterprise AI Governance must explicitly govern AI consumed from Vendor Products, Vendor Services, SaaS platforms, cloud services, APIs, managed services, outsourced services, and other third-party offerings.

Vendor-provided AI creates a governance challenge because the enterprise may not build the model, host the infrastructure, define the roadmap, control the training process, manage the runtime environment, or determine when features change. However, the enterprise may still use the AI in business processes, expose enterprise data to it, rely on its outputs, make decisions based on its results, provide it to employees or customers, or become accountable for how it affects stakeholders.

This means vendor AI cannot be treated as “someone else’s AI.” If the enterprise uses it, enables it, exposes it, configures it, depends on it, or allows it to process enterprise data, it must be visible in the enterprise governance model.

Vendor AI should be inventoried, classified, related, controlled, monitored, evidenced, and reassessed over time.

What Vendor and Third-Party AI Includes

Vendor and third-party AI includes any AI capability supplied, hosted, embedded, operated, managed, or materially controlled by an external provider.

This may include AI embedded in SaaS products, AI assistants in productivity suites, AI capabilities in collaboration tools, AI search in knowledge platforms, AI summarization in service-management systems, AI recommendations in CRM platforms, AI analytics in business-intelligence platforms, AI-enabled cybersecurity tools, and related items.

Vendor AI may be obvious or hidden. A vendor may market an AI assistant prominently. Another vendor may add AI to search, summarization, routing, analytics, automation, or workflow behavior without the enterprise initially recognizing the product as AI-enabled.

The governance requirement is practical: if a vendor capability uses AI to affects enterprise data, users, stakeholders, outputs, decisions, processes, obligations, or controls, the enterprise must govern it.

Vendor AI as a Relationship Across Existing Inventories

Vendor AI should be modeled through relationships across existing enterprise inventories.

The enterprise should relate Vendor Products and Vendor Services to AI Use Cases, AI Agents, AI Models where known, AI Prompts where configurable, Data and Information, Applications, Platforms, APIs, Workflows, Automations, Locations / Jurisdictions, Regulations, Regulatory Obligations, Controls, Evidence Records, Risks, Incidents, and Contracts.

A vendor AI feature may support an AI Use Case. It may be embedded in an Application. It may expose an AI Agent. It may process Data and Information. It may operate in a Location / Jurisdiction. It may be governed by Contract terms. It may satisfy or violate a Regulatory Obligation. It may require Controls and Evidence. It may create or contribute to an AI Incident.

The enterprise should avoid treating vendor AI as an isolated procurement record. Vendor AI becomes governable when it is connected to the business, technical, regulatory, data, operational, and evidence relationships that surround it.

Enterprise Role and Accountability

The enterprise must understand its role when consuming vendor AI.

Depending on the context, the enterprise may act as an enterprise customer, deployer, operator, controller, processor, business owner, configured user, provider to downstream stakeholders, or accountable user of a vendor-provided capability. The exact labels may vary by regulation, contract, industry, and operating model.

The key governance question is: what is the enterprise responsible for?

The vendor may provide the model or platform, but the enterprise may decide which users receive access, which data is shared, which workflows use the AI, which outputs are relied on, which customers or employees are affected, which locations are served, which controls are configured, and which business decisions are influenced.

Vendor-provided AI therefore does not eliminate enterprise accountability. It changes the accountability model. Enterprise AI Governance must identify what the vendor controls, what the enterprise controls, what is shared, what is unknown, and what evidence is required.

Vendor Due Diligence and AI Capability Review

Vendor AI should be reviewed before approval and periodically thereafter.

Vendor due diligence should evaluate the vendor’s AI capabilities, data-use practices, security controls, privacy posture, model governance, change-management practices, logging capabilities, retention practices, regional processing, subprocessor involvement, incident notification commitments, audit rights, contractual terms, and support for enterprise evidence needs.

The review should also determine what the vendor AI does. Does it summarize, classify, recommend, predict, generate, retrieve, route, decide, automate, or act? Does it process sensitive data? Does it affect customers or employees? Does it support regulated decisions? Does it allow configuration of AI Prompts or model behavior? Does it expose AI outputs externally? Does it permit the enterprise to disable, restrict, monitor, or evidence the capability? Which gaps require action?

Vendor review should not be a one-time questionnaire. Vendor AI changes over time. The enterprise should define reassessment triggers for new AI features, model changes, data-processing changes, subprocessor changes, regional changes, contractual changes, incident history, control failures, and expanded use.

Data Use, Training, Retention, and Processing

Vendor AI governance must address how enterprise data is used.

The enterprise should understand whether vendor AI uses enterprise data for inference, retrieval, training, fine-tuning, product improvement, analytics, support, monitoring, evaluation, or debugging. It should know whether prompts, responses, outputs, transcripts, metadata, embeddings, logs, files, or retrieved content are retained by the vendor.

It should understand whether enterprise data is used to train vendor models or improve vendor services, whether the enterprise can opt out, whether data is segregated from other customers, whether data is encrypted, whether data is deleted at the end of retention, whether data is available for legal hold, and whether the vendor provides evidence of deletion or retention compliance.

These concerns should connect vendor AI governance to Data and Information governance, privacy, security, legal, records management, AI retention policy, contractual obligations, and Evidence Records.

Regional Availability and Data Processing Locations

Vendor AI must be governed by location and jurisdiction.

A vendor may make AI features available in some countries or regions but not others. It may process data in specific cloud regions. It may use subprocessors located in different jurisdictions. It may apply different terms, retention rules, logging capabilities, model options, privacy commitments, or compliance controls by location.

The enterprise should capture where vendor AI is available, where it is enabled, where data is processed, where data is stored, where logs are retained, where subprocessors operate, and where affected stakeholders are located.

This location information should connect Vendor Products, Vendor Services, Contracts, Data and Information, AI Use Cases, AI Agents, Applications, Locations / Jurisdictions, Regulatory Obligations, Controls, Evidence Records, and Risks.

Without this relationship model, the enterprise may incorrectly assume that a vendor AI capability has the same governance posture everywhere.

Vendor AI Feature Drift

Vendor AI feature drift occurs when a vendor’s AI capabilities, models, configurations, defaults, data-processing practices, regional availability, subprocessors, contractual terms, or controls change over time.

Feature drift can create governance risk even when the original vendor review was appropriate. A vendor may add a new AI assistant, enable summarization by default, expand a feature to new users, change a model, introduce tool-calling behavior, change retention terms, add subprocessors, change logging capabilities, or move processing to a new location.

The enterprise should define monitoring and reassessment practices for vendor AI feature drift. This may include review of release notes, administrative settings, product roadmaps, contract updates, privacy notices, subprocessor lists, security reports, vendor attestations, and usage data.

Material vendor AI changes should trigger governance reassessment when they affect data, stakeholders, outputs, locations, obligations, controls, retention, evidence, or risk.

Contractual Controls and Evidence Rights

Contracts are a major governance mechanism for vendor AI.

Contracts should address AI-related data use, training restrictions, retention, deletion, confidentiality, security, privacy, subprocessors, regional processing, audit rights, logging, incident notification, model changes, feature changes, service availability, intellectual property, output ownership, compliance commitments, data residency, and termination support where relevant.

The enterprise should seek evidence rights where possible. It may need vendor attestations, audit reports, data-processing documentation, security certifications, model documentation, subprocessor records, incident reports, logging exports, configuration evidence, deletion certificates, or control evidence.

Contractual controls should be connected to the Enterprise Model. A Contract may create obligations that apply to a Vendor Product, Vendor Service, AI Use Case, Data Source, Location / Jurisdiction, Control, or Evidence Record.

User Access, Configuration, and Administrative Controls

Vendor AI often depends on enterprise configuration.

The enterprise may be able to enable or disable AI features, restrict user access, select model options, configure data-sharing settings, define retention periods, disable training, control regional availability, manage prompts, set logging levels, integrate with identity management, and configure security controls.

These administrative controls should be owned, documented, reviewed, and evidenced.

Enterprise AI Governance should define who may enable vendor AI features, who approves configuration changes, how settings are reviewed, how access is provisioned and deprovisioned, how exceptions are handled, and what evidence proves that vendor AI controls are operating.

If vendor AI can be enabled by local administrators without governance approval, the enterprise has a Shadow AI risk inside an approved vendor product.

Monitoring, Incidents, and Vendor Accountability

Vendor AI must be monitored for usage, behavior, incidents, and vendor accountability.

Monitoring may include which users access vendor AI, which features are enabled, what data is processed, which outputs are produced, which errors occur, which incidents are reported, which locations are affected, which logs are available, and whether contractual controls remain in effect.

Vendor AI incidents may involve data exposure, incorrect outputs, harmful recommendations, service outage, unauthorized feature enablement, regional processing violation, contractual breach, model behavior change, security issue, or failure to provide required evidence.

Incident response should define how vendor incidents are detected, escalated, investigated, contained, remediated, reported, and evidenced. The enterprise should understand which responsibilities belong to the vendor and which remain with the enterprise.

Governance Questions for Vendor and Third-Party AI

For Vendor and Third-Party AI, governance should answer what exists, who owns it, what is affected, which risks, obligations, controls, evidence, incidents, changes, and gaps require action.