Advisory
AI Governance Assessment

Know what needs to be in place before your AI use case moves forward.

Assess the data, controls, ownership, and evidence behind a proposed or existing AI use case. Leave with clear findings, recommended conditions, and prioritized actions for the people responsible for the decision.

Discuss Your Governance QuestionSee what we assess
  • Fixed scopePriority use cases and deliverables agreed upfront
  • Named ownersResponsibility connected to each action
  • Evidence-backedFindings and decision conditions documented

Where is the decision getting stuck?

A focused assessment starts with the decision your current evidence cannot support.

  • Before deployment

    The team needs evidence to support approval of a proposed use case or vendor.

  • Already in use

    Ownership, oversight, and technical controls have not kept pace with adoption.

  • Before expansion

    A new audience, provider, or data source changes what needs review.

Follow the use case
from data to decision.

We trace how the system actually works, then compare documented policy with the controls and evidence in place.

Start with the use caseA defined purpose. An accountable owner.Who uses it, who is affected, and what the output is allowed to influence.

Data

What information can enter—and who can access it?

Sources, permissions, sensitivity, retention, and deletion.

Model and provider

What can the provider retain, reuse, or change?

Training terms, configuration, service boundaries, and vendor controls.

Output and use

What does the output influence—and who reviews it?

Decision impact, permitted uses, exceptions, and escalation.

Human oversight

Named people can review, challenge, escalate, and stop use.

Evidence and monitoring

Tests, logs, approvals, and review points make decisions traceable as the system changes.

Oversight and evidence apply across the full path, before deployment and throughout use.

WHAT YOU RECEIVE

A clear recommendation. Accountable next steps.

The findings, evidence, owners, and decision conditions stay connected, so your team can challenge the reasoning and act on it.

Risk register
Prioritized findings, evidence gaps, and affected stakeholders.
Control recommendations
Practical changes to technical, vendor, and operating controls.
Governance decision record
Recommended conditions, assumptions, and unresolved questions.
Prioritized action plan
Owners, dependencies, and review points for remediation.
Illustrative governance recordExample only
Finding · Access controls

Access restrictions are not verified in retrieval.

Evidence gap
Role-based test results are missing.
Recommended action
Test retrieval against approved permissions, including restricted records and user roles.
Owner
Application and security leads.
Decision condition
Resolve the gap before expanding access.

Illustrative structure. Findings, priorities, and ownership depend on the assessed use case. Approval remains with your organization.

From scope to recommendation

We agree the use cases, evidence, working team, deliverables, timing, and fee before work begins.

  1. InventoryIdentify priority systems and owners.
  2. TraceMap data, provider, and decision boundaries.
  3. EvaluateTest policy against controls and evidence.
  4. PrioritizeSet actions, dependencies, and decision conditions.

The recommendation follows the evidence. Changes may involve policy, vendor terms, oversight, monitoring, or architecture. A different hosting model—including I/O Sovereign AI—is considered when the findings justify it.

REVIEW CONTEXT

Same technology.Different obligations.

Jurisdiction, data, organizational role, and decision impact shape the review. We organize the relevant evidence for your legal, privacy, and risk stakeholders.

  • Privacy and sensitive data

    When sensitive information enters the workflow, we examine permissions, access, retention, and provider terms.

  • Automated decisions and profiling

    When AI influences outcomes for people, we examine decision records, human review, testing, and ways to challenge an outcome.

  • AI-specific requirements

    Where role and jurisdiction bring AI obligations into scope, we organize system classification, intended-use, and oversight evidence.

  • Credit and fair lending

    When AI influences lending decisions, we examine decision factors, fairness testing, reason codes, and adverse-action support.

  • Internal and contractual controls

    Where policies and agreements set additional requirements, we examine ownership, approvals, audit rights, and exit terms.

Review areas and evidence depend on the agreed scope. The assessment does not provide legal advice or certify compliance.

Understanding your AI governance assessment

What kinds of AI systems can be assessed?
The scope can include predictive models, recommendation and personalization systems, generative AI, retrieval-augmented applications, automated decision support, and AI capabilities embedded in third-party platforms. Priority is based on use and impact, not the marketing label applied to the technology.
Do we need a complete AI inventory before starting?
No. Existing inventories help, but discovery can identify and prioritize the systems relevant to the decision. A broad inventory may be recommended as a later governance action rather than a prerequisite for focused assessment.
Does the assessment provide legal advice or certify compliance?
No. Tricycle evaluates architecture, data, vendor, workflow, oversight, monitoring, and documentation controls and organizes evidence for stakeholder review. The client’s legal and compliance advisors determine which laws apply and make legal conclusions.
How are third-party AI vendors evaluated?
The assessment reviews available contracts, data and training terms, security documentation, subprocessors, deployment model, access, model-change practices, logging, audit support, retention, deletion, portability, and termination conditions against the use case’s requirements.
Is Sovereign AI always the recommended architecture?
No. Vendor-hosted, client-hosted, self-managed, hybrid, and sovereign patterns can each be appropriate. The recommendation follows the use case, data sensitivity, contractual and technical controls, operating needs, evidence, and risk tolerance. See the deeper shared-model and sovereign AI architecture comparison.
Who should participate from our organization?
Participation commonly includes the accountable business or product owner plus technology, data, security, privacy, risk, legal or compliance, procurement, and operational stakeholders. The systems and decision determine the working team.
What information is required, and how are timing and fees established?
Useful inputs commonly include system inventories, use-case descriptions, architecture and data-flow documentation, vendor agreements, policies, risk reviews, testing, approval records, logs, and incident procedures. Before work begins, both teams agree to the systems, evidence, working team, deliverables, timeline, and fee.

START WITH THE DECISION

Bring clarity to your next AI decision.

Share the system or deployment being considered, the stakeholders involved, and the unresolved concern. We will determine whether a focused AI Governance Assessment is the right next step.

Discuss Your Governance Question