You deployed AI. Now you need to prove it's under control.
Organizations across the GTA and Canada are moving fast on AI – and most haven't built the governance and documentation layer that makes adoption defensible. We build that layer: structured frameworks, written controls, and evidence packages that hold up when auditors ask.
What's Included
Our AI GRC service covers the full documentation and controls lifecycle for AI adoption – from initial risk identification through to ongoing governance and GRC platform entry.
AI Inventory & Risk Identification
We map every AI model and tool in use across your organization – including third-party tools, embedded models, and AI features within existing software – and create a structured model inventory capturing purpose, data inputs and outputs, deployment context, and preliminary risk classification.
AI Risk Assessment & Classification
Not all AI tools carry the same level of risk. We conduct a structured risk assessment for each identified model – evaluating complexity, data sensitivity, regulatory exposure, and third-party dependency – and assign a risk rating that determines what controls, governance requirements, and documentation intensity apply.
Controls Documentation
For each identified risk, we document the controls your organization has in place – or needs to put in place – to manage it. Every control is documented with clear language: what it does, who owns it, how it is tested, and how evidence of its operation is captured. This is the documentation that stands up in an audit.
Human-in-the-Loop Process Documentation
We create the workflow documentation, SOPs, and accountability assignments that formalize human oversight requirements for your AI systems – ensuring your organization can demonstrate, on paper, that human review is real and not theoretical. For SOX-relevant processes, this includes evidence capture procedures that produce audit-ready proof of review.
GRC Platform Entry – AuditBoard / ServiceNow
Documentation that lives in a Word document doesn't function as a book of record. We enter all documented controls, risk ratings, and governance artifacts directly into your GRC platform of record – whether AuditBoard, ServiceNow GRC, or another system – configured to your control framework and testing cycles.
Policy & Procedure Development
A compliant AI GRC program requires a set of governing policies alongside controls documentation. We develop or update your AI Acceptable Use Policy, AI Model Risk Management Policy, Data Governance Policy for AI inputs and outputs, Third-Party AI Vendor Risk Policy, AI Incident Response Procedure, and model lifecycle documentation.
The Regulatory Reality for Canadian Organizations
AI governance is moving from best practice to regulatory requirement – particularly for organizations in federally regulated industries.
OSFI's final Guideline E-23 takes effect May 1, 2027, addressing AI and machine learning model use by federally regulated financial institutions – banks, insurers, trust companies, and more. Organizations must demonstrate ongoing, documented governance across the entire model lifecycle.
Ontario's Act sets out accountability requirements for AI use, with the Information and Privacy Commissioner's six principles – including accountability, transparency, fairness, and keeping humans in the loop – increasingly shaping private sector expectations.
The NIST AI RMF provides guidance through four core functions: Govern, Map, Measure, and Manage. Alignment is fast becoming the de facto standard for demonstrating AI governance maturity to clients, partners, and regulators.
The first international standard for AI management systems, specifying requirements for establishing, implementing, and improving AI governance within organizations. We map your controls and documentation to ISO 42001 requirements as part of the engagement.
How It Works
Our process moves from inventory to active governance – transforming a documentation gap into an auditable program.
Inventory
We map every AI tool and model in use, identify the data and decisions they touch, and determine which regulatory frameworks apply.
Risk Assessment
Each model is assessed and rated – quantitative factors like operational impact alongside qualitative ones like model complexity and customer exposure.
Documentation
We write the controls, policies, SOPs, and human-in-the-loop workflows – in your organization's voice, specific to your actual AI use cases.
Platform Entry & Handoff
Governance artifacts are entered into your GRC platform and handed off with maintenance documentation so the program operates after we leave.
The AdventuReliable Difference
The challenge most organizations face isn't understanding AI risk in the abstract. It's translating that understanding into documented controls, written policies, GRC platform entries, and governance processes that hold up under scrutiny.
We are change management and documentation specialists. We work alongside your technical, legal, and compliance teams – writing what they know, structuring what they've built, and ensuring the paper trail is as strong as the technology behind it. We don't touch the AI itself. We build the governance layer around it.
Who We Serve
This service is designed for organizations in the GTA and across Ontario that are federally regulated financial institutions preparing for OSFI Guideline E-23 compliance by May 2027, have adopted AI tools without a formal governance framework in place, are approaching an audit where AI risk and controls will be reviewed, or are subject to SOX compliance and need to document human-in-the-loop processes for AI-generated financial data.
We also work with organizations that want to demonstrate AI governance maturity to clients, partners, or regulators before it becomes a mandatory requirement – including those at earlier stages of AI adoption who want to build governance in from the start rather than retrofit it later.
Don't wait for the audit to ask about your AI governance.
Organizations that act now have the runway to build governance properly. Those who wait will be remediating under pressure. Let's build your AI GRC framework before the regulator asks for it.