The governance problem

Organizations are adopting AI through internal tools, third-party products and increasingly autonomous workflows. Yet approval processes, risk ownership and assurance often remain designed for conventional technology. The question is no longer simply whether an AI system performs. It is who can authorize its use, what evidence supports that decision, how its behaviour is monitored and what happens when its assumptions fail.

How GRC Signal can help

Governance frameworks that fit the business

Define AI governance roles, decision rights, policies, risk appetite and escalation paths that connect business ownership with risk, compliance, technology and independent oversight.

AI use-case intake and approval

Design practical intake, classification and assessment processes so higher-risk applications receive appropriate review, testing and approval before deployment.

Controls, monitoring and assurance

Translate governance requirements into testable controls and evidence: model inventories, data and model lineage, validation, human oversight, performance monitoring, change management, incident response and periodic review.

Third-party and agentic AI oversight

Assess dependencies on external models and vendors, including access restrictions, contractual changes, concentration risk, autonomy, tool permissions, fallback arrangements and the ability to intervene when an AI-enabled process behaves unexpectedly.

Regulatory readiness and governance review

Assess current practices against relevant supervisory expectations and recognized frameworks, identify gaps, prioritize remediation and prepare decision-useful reporting for senior management and boards.

What an engagement should deliver

Not another policy that sits on a shelf. The goal is a defensible operating model: an AI inventory with meaningful risk classification, documented approval criteria, named accountable owners, control and testing requirements, escalation triggers, and management reporting that shows whether governance is actually working.

Grounded in established standards. Built for real decisions.

Our approach draws on relevant guidance and industry practices, including the NIST AI Risk Management Framework, ISO/IEC 42001, the IAPP AI governance body of knowledge, and—where applicable—Canadian financial-sector expectations such as OSFI Guideline E-23 on model risk management and OSFI’s broader technology and third-party risk guidance. Technical risk considerations can also draw on resources such as the OWASP Top 10 for Large Language Model Applications and MITRE ATLAS.

These are reference points, not interchangeable compliance checklists. The right controls depend on the AI use case, organizational responsibilities, regulatory perimeter and actual exposure.

Experience behind the perspective

GRC Signal is founded by Phalgun Kumar, whose work spans governance, risk and compliance, technology risk, financial services and professional education. His experience includes work with PwC and Scotiabank, governance and control initiatives involving ServiceNow IRM, and AI governance teaching and curriculum development. The focus is on bridging policy expectations with the decisions and evidence organizations need to operate responsibly.

Discuss an AI governance challenge

Whether you are formalizing AI oversight, reviewing a high-impact use case or strengthening an existing governance program, GRC Signal can help define the questions, accountabilities and practical next steps.

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