AI Governance
Your organization is already using AI. The question is whether you're governing it.
AI is already inside your organization. It's in the email tool drafting your replies, the productivity suite writing your meeting summaries, the marketing platform generating your campaigns, the developer tools writing your code. Half of it your IT team approved. Half of it your staff brought in on their own. None of it came with a policy.
Now the questions are starting. Your board wants to know your AI risk position. Your regulator is publishing guidance. Your auditor wants evidence of governance. Your insurer is asking on the renewal form. And the staff member who put a sensitive document into a public LLM last quarter still doesn't realize what happened.
Governance isn't optional anymore. It's just a question of whether yours is intentional or accidental.
What an AI governance engagement looks like
We help leadership teams put practical AI governance in place — fast enough to keep up with adoption, structured enough to satisfy boards and regulators, and grounded enough not to slow your teams down.
The goal isn't to lock AI out of the organization. The goal is to let your people use it well — knowing what's allowed, what isn't, what to escalate, and what protections are in place — while giving your leadership team something defensible to put in front of every stakeholder who asks.
Our methodology
STEP 01
AI Inventory & Risk Assessment
What AI is in use across your organization, by whom, for what purpose, and against what data? Most organizations don't actually know. We map the current state — sanctioned tools, embedded AI in existing SaaS, shadow AI use by staff — and assess the risk exposure: data leakage, regulatory exposure, accuracy risk, reputational risk, and bias or fairness risk.
Policy Development
Acceptable use policies that staff can actually follow. Data handling rules that protect what needs protecting without making the tools useless. Vendor due-diligence frameworks for evaluating new AI-enabled SaaS. Manager guidance for handling staff questions in real time.
STEP 02
STEP 03
Risk Framework
A structured process for evaluating new AI deployments before they go live: risk classification, control requirements, sign-off authority, and monitoring obligations. Aligned to NIST AI RMF and ISO/IEC 42001 where appropriate and built to evolve with the regulatory landscape.
Training & Awareness
Staff training that explains what the policies mean in their day-to-day work. Manager-level training that prepares them to handle questions and incidents. Executive sessions that prepare your leadership team to make AI risk decisions and report to boards.
Ongoing Governance
Board reporting on AI risk and adoption. Control monitoring. Periodic policy refresh as the technology and regulatory environment evolve. Incident response readiness for AI-specific events (deepfake attacks, prompt injection, model output errors with material consequence).
Topics we cover
Acceptable use policies (AUPs) for generative AI and AI-enabled SaaS
Data handling and confidentiality in AI workflows
Vendor due diligence for AI-enabled tools
Bias, fairness, and discrimination risk
Regulatory alignment (NIST AI RMF, ISO/IEC 42001, EU AI Act, emerging Canadian guidance)
AI-specific incident response (deepfake attacks, prompt injection, training-data poisoning, model output errors)
AI-enabled cybersecurity threats (deepfake social engineering, AI-generated phishing at scale)
Board and executive AI risk reporting
Ethical use frameworks and responsible AI principles
Who this is for
Public sector bodies deploying or evaluating AI in service delivery — under heightened public, political, and regulatory scrutiny.
Healthcare and regulated industries managing patient, client, or customer data through AI-enabled workflows.
Growing companies, whose AI adoption is outpacing their policies — and whose customers and investors are starting to ask.
Non-profits using AI to stretch limited resources without stretching their risk exposure with funders, donors, or beneficiaries.
Any organization whose board has started asking AI questions the executive team can't yet answer.
What you walk away with
A defensible AI governance program, documented and ready for audit or regulatory review
Clear policies your staff can follow
A risk framework for evaluating every new AI deployment going forward
Training calibrated to your workforce — staff, managers, and executives
Board-ready reporting on AI risk and adoption
An incident response plan that covers AI-specific events
A foundation that scales as the technology and the regulatory environment evolve
Why Magenta for AI governance
We approach AI governance from two directions: as a cyber problem (the data exposure, the threat landscape, the attack vectors) and as a governance problem (the policies, the structures, the reporting, the board-level risk conversation). Few firms bring both.
Our practice lead holds a Generative AI certificate from the Rotman School of Management at the University of Toronto, and we work alongside partners in AI safety research and regulatory advisory — meaning we bring you a perspective grounded in both what's technically emerging and what's about to be required.
We're not here to tell you AI is dangerous and you should slow down. We're here to help you use it well, govern it intentionally, and stay ahead of the conversations your stakeholders are about to start having.
AI policy only works if people are trained on it. Pair this with Education & Training for staff and executive sessions.
STEP 04
STEP 05
