Start with the work and the accountable owner
Record what each system is used for, who uses it and who may be affected by its output. Identify a business owner who can accept or stop the use, with engineering, security and privacy responsibilities agreed around that decision.
A document-search assistant and a system preparing a consequential financial action need different controls. Describe the actual information and actions before choosing a policy or buying another tool.
The NIST AI Risk Management Framework is a voluntary reference for managing AI risk. It organises activities around Govern, Map, Measure and Manage. The practical structure below helps turn those responsibilities into work your team can assign and review.
Six components to make the framework usable
Inventory
Keep a record of approved AI systems, their owners, intended uses, models, data sources and connected tools. Include externally supplied systems. Record where information is processed and which parties can access it.
Risk classification
Assess each use against its possible effects on people and the business. Distinguish a draft that a person checks from an action that changes a customer record or moves money. Record uncertainties and the reason for the chosen review level.
Controls
Translate the agreed boundaries into permissions, input and output checks, data handling, resource limits and stopping conditions. Identify which controls the platform supplies and which your application or operating team must enforce.
Human oversight
Name who may review and approve consequential work. Give that person the proposed action and the evidence needed to decide. Define how staff can reject a proposal, stop work or escalate an exception.
Evidence
Keep proportionate records of configuration, tests, approvals and observable actions. Protect those records with access and retention rules. An activity log helps establish what happened; it does not expose a model’s complete internal reasoning.
Monitoring and change
Assign responsibility for reviewing errors, incidents and changes to models, data or tools. Agree when a change requires another evaluation and who can roll back or disable the affected workflow.
Within an individual system, the Ryzolv Harness Rules describe how proposals, validation, named-human approval and records fit together. The AI harness guide explains that runtime boundary in more detail.
Canadian considerations depend on the organisation
Start with the entity, activity, information and people involved. The references below have different scopes. They are starting points for an applicability review, rather than one checklist that establishes compliance for every Canadian business.
PIPEDA
PIPEDA addresses private-sector handling of personal information in commercial activities. The Office of the Privacy Commissioner explains how substantially similar provincial laws and cross-border commercial information flows affect its application. Its fair information principles include accountability, limiting collection and retention, and safeguards. See the OPC’s PIPEDA overview.
For a governance review, map the proposed AI use to the actual data flow. Record purposes, access, retention and safeguards for review by the responsible privacy owner.
Quebec Law 25 and private-sector automated decisions
Section 12.1 of Quebec’s private-sector privacy Act addresses decisions based exclusively on automated processing of personal information. It provides for notice, specified information on request and an opportunity to submit observations to a staff member able to review the decision. Read the current Act, section 12.1 and the Commission’s explanation of automated decisions.
Where that provision applies, identify the decision, the notice and the review route. A nominal approval button is not evidence that the organisation has implemented the required process.
Ontario Bill 194 and public-sector AI
Bill 194 was enacted as the Strengthening Cyber Security and Building Trust in the Public Sector Act, 2024. Its Schedule 1, the Enhancing Digital Security and Trust Act, 2024, addresses AI use by specified public-sector entities. Provisions concerning accountability, transparency, risk management and human oversight depend on prescribed entities, circumstances and regulations. See the Legislative Assembly’s enacted bill and Schedule 1 and the current Act.
Check the applicable provisions and regulations for the organisation and use case. This legislation should not be presented as a universal private-business AI requirement.
OSFI Guideline E-23
OSFI’s revised E-23 sets expectations for model risk management at federally regulated financial institutions, with proportional, risk-based application. Its definition of a model includes AI and machine-learning methods. It takes effect on 1 May 2027. The guideline addresses model inventories, risk ratings, lifecycle governance, review and monitoring. See OSFI’s Model Risk Management guideline.
For an institution within scope, connect system-level controls and test evidence to its model-risk process. A harness supplies part of that evidence; the institution’s wider governance responsibilities still need owners.
How Ryzolv helps
Ryzolv’s AI Security and Governance work covers scoped risk assessment, access boundaries, agent and tool testing, and reviewable findings. We work with your team to turn agreed requirements into controls that can be tested.
Our AI Agents and Employees work connects those controls to a defined role, approved tools and human decisions. The How We Work guide explains requirements, scope, implementation and verification.
Ryzolv does not certify regulatory compliance. Applicability depends on the organisation, sector and information involved; legal and professional decisions remain with your organisation and its advisers.
Common questions
What should an AI governance framework include?
An inventory, accountable owners, risk classification, controls, human oversight, evidence and monitoring. Each part needs an operating process and a responsible person, so the framework can guide actual use and change decisions.
Is an AI governance framework the same as an AI harness?
A governance framework sets responsibilities and policies across the organisation. A harness implements the task loop and controls around a particular AI system. The system’s evidence should support the wider governance process.
Does Ontario Bill 194 apply to every private business?
The AI provisions discussed here address specified public-sector entities, with requirements depending on prescribed circumstances and regulations. Do not treat them as a universal private-business checklist. Establish applicability for the organisation and use case.
When does OSFI’s revised Guideline E-23 take effect?
The revised guideline takes effect on 1 May 2027. It sets risk-based model-risk management expectations for federally regulated financial institutions, including models using AI and machine-learning methods.
Does using this framework establish regulatory compliance?
No. This guide explains governance responsibilities and selected Canadian considerations. The organisation and its advisers must establish applicable requirements and assess the evidence; Ryzolv does not certify regulatory compliance.