Artificial Intelligence (AI) Governance Framework for SMEs

Fundamental Principles Enabling Small Organizations to Benefit from AI

AI is omnipresent. In the wake of ever-changing developments within Information Technology, the emergence and growing omnipresence of AI will impact future business operations of SMEs. This is why an AI Governance Framework (AIGF) is a helpful tool for assisting SMEs in their adaptation with AI. Consequently, what types of AI usages within SMEs need a governance framework? Our Cybersecurity Newsletter has been carefully written to answer this question. Designated at the end of this document, the Resources and References 1 to 16 have been duly accessed, circumspectly analyzed, broadly summarized and methodically adjusted for the writing of the 7 sections and subsections of this cybersecurity manuscript.

Our cardinal question: What are the types of AI usages within SMEs that need a governance framework? A surprisingly large slice of AI usage inside SMEs requires a governance framework — not because SMEs need bureaucracy but because AI introduces new operational, legal, and reputational risks that can quietly accumulate. The simplest way to think about it is this: Any AI usage that affects decisions, data, customers, employees, and compliance needs a governance framework.

SECTION I

SUMMARIZED CLARIFICATIONS OF DIFFERENT AI ARCHITECTURE MODELS USED BY ORGANIZATIONS INCLUDING SMEs

The chosen AI model architecture has a direct impact on the governance framework, risk profile, and control requirements. Governance is generally simpler when an organization uses a commercial enterprise version of ChatGPT or a similar managed AI service, as many security, infrastructure, maintenance, and compliance responsibilities remain with the vendor. In contrast, when an organization develops, fine-tunes, or operates its own AI models, it assumes significantly greater responsibility for data governance, model oversight, security controls, regulatory compliance, monitoring, and lifecycle management. Furthermore, many organizations are now embedding AI capabilities directly into their products and services. In numerous cases, these solutions rely on limited domain-specific training, retrieval-based approaches, or specialized knowledge models that have a relatively low governance impact compared with fully trained proprietary models. Nevertheless, governance requirements increase when AI systems influence customer decisions, process sensitive information, or operate with a high degree of autonomy.

1. Outsourced AI (SaaS)

Model location: Runs entirely on an external provider’s infrastructure (Microsoft, OpenAI, Google, Anthropic, etc.)

Training: Pretrained by the vendor SME data usage. Depends on vendor policy Data retention:

• Enterprise-grade offerings (e.g.: Google Gemini, ChatGPT, Microsoft Copilot, Azure OpenAI) → no customer data is used to train the model
• Consumer-grade offerings → may use data for model improvement unless opted out

Characteristics

• No infrastructure burden
• Fastest adoption
• Strong compliance if using enterprise-tier services
• Risk depends on vendor’s data-handling guarantees

Best for: SMEs without ML teams; rapid deployment; low operational overhead.

2. Internal Use of External Models (Private Cloud / VPC Deployment)

Model location: Vendor model deployed inside the SME’s private cloud or virtual private environment

Training: Usually not retrained; sometimes fine-tuned SME data usage:

• Data stays inside SME’s controlled environment
• Vendor does not retain or learn from SME data

Characteristics

• Higher security and compliance
• Allows integration with internal systems
• More expensive than public cloud usage

Best for: Regulated industries (finance, healthcare), sensitive data environments.

3. In‑House Training (Fully Internal Models)

Model location: Entirely on SME infrastructure

Training: SME trains the model from scratch or using open-source architectures SME data usage:

• All training data is controlled internally
• No external retention
• Full governance responsibility lies with the SME

Characteristics

• Maximum control
• Highest cost and complexity
• Requires ML engineers, GPU clusters, MLOps pipelines

Best for: Large enterprises, defense, advanced tech companies.

4. External Training Using SME Data (Vendor‑Trained or Co‑Trained Models)

This is the most misunderstood category — and the one with the highest governance risk.

Model location: Vendor infrastructure

Training: Vendor trains or fine-tunes a model using SME-provided data SME data usage: two (2) sub‑models exist:

4A. SME Data Retained for Vendor Training

• Vendor keeps the data
• Data may be used to improve the vendor’s general models
• Risk: loss of competitive advantage, IP leakage, compliance exposure

4B. SME Data Excluded from Vendor Training

• Vendor uses SME data only for the SME’s private fine‑tuned model
• Data is not used to train global models
• Data is deleted or isolated after training
• Common in enterprise contracts with strict DPAs

Characteristics

• Highly customized performance
• Requires strong contractual controls
• Must include: retention clauses, deletion guarantees, model-isolation guarantees

Best for: SMEs needing domain-specific AI (legal, medical, engineering, proprietary workflows).

FIGURE 1: Summary Table (Architecture vs. Training vs. Data Retention)

Non‑Obvious Insight

The AI Architecture Model does practically determine your AI risk posture more than the model type (Transformer, LLM, etc.). For the daily realities of SMEs, the biggest governance mistake is assuming “fine‑tuning” and “training” are the same.

In reality “fine‑tuning” and “training” are not the same:

• Fine‑tuning → AI architecture model learns patterns but does not absorb raw data.
• Training → AI architecture model weights change based on your data, potentially embedding proprietary information.

This distinction is critical in procurement and DPAs.

SECTION II

TYPES OF AI USAGES WITHIN SMEs NEEDING AN AI GOVERNANCE FRAMEWORK

1. AI that touches customer data

This is the highest‑risk category for SMEs.

• Customer support chatbots using CRM data
• AI‑assisted sales tools that analyze customer profiles
• Automated marketing personalization
• AI‑driven credit, eligibility, or risk scoring

Why is an AI Governance Framework needed? Privacy laws (PIPEDA, Quebec Law 25, GDPR), consent, data minimization, auditability, and preventing biased or opaque decisions.

2. AI that processes internal or sensitive business data

This includes anything that ingests proprietary information.

• Document summarization of contracts, proposals, financials
• AI‑assisted coding tools with access to internal repos
• AI‑powered analytics on operational or financial datasets
• AI used in HR processes (screening, performance analysis)

Why is an AI Governance Framework needed? Data leakage prevention, confidentiality controls, model‑output validation, and clear boundaries on what can be uploaded to external systems.

3. AI that automates or influences decision‑making

Whenever AI outputs drive actions, you need oversight.

• Automated invoice processing and approvals
• AI‑based procurement recommendations
• Inventory forecasting and supply‑chain optimization
• AI‑assisted hiring or promotion recommendations

Why is an AI Governance Framework needed? Human‑in‑the‑loop requirements, accountability, audit trails, and bias monitoring.

4. AI used for external communication

Because it affects brand, reputation, and legal exposure.

• AI‑generated marketing content
• AI‑drafted emails, proposals, or legal‑adjacent documents
• AI‑generated product descriptions or website content

Why is an AI Governance Framework needed? Accuracy checks, copyright compliance, tone/brand consistency, and preventing hallucinated claims.

5. AI used in cybersecurity or IT operations

These tools are powerful but can cause cascading failures if misconfigured.

• AI‑driven threat detection
• Automated patching or remediation tools
• AI‑based identity or access‑risk scoring

Why is an AI Governance Framework needed? Change‑control processes, monitoring, and validation of automated actions.

6. AI used by employees for productivity

This is the most underestimated risk area.

• General‑purpose AI (office operations, coding, design)
• AI note‑takers in meetings
• AI transcription and translation tools

Why is an AI Governance Framework needed? Clear acceptable‑use rules, data‑handling boundaries, and training on what not to feed into AI systems.

7. AI used in products or services delivered to customers

If your SME embeds AI into what it sells, governance becomes essential.

• AI‑enhanced SaaS features
• AI‑powered analytics offered to clients
• AI‑based recommendations inside customer‑facing apps

Why is an AI Governance Framework needed? Transparency, reliability, service‑level expectations, and liability management.

8. AI used for compliance‑relevant tasks

If AI touches regulated processes, governance is mandatory.

• Financial reporting assistance
• AI‑supported legal research
• AI used in safety‑critical workflows

Why is an AI Governance Framework needed? Validation, documentation, and ensuring AI does not replace required human judgment.

9. AI used for code generation by many organizations including SMEs

AI used for code generation is built on large language models (LLMs) that have been trained on massive corpora of source code, documentation, and natural language. The core idea is simple: the model learns statistical patterns in how code is written, structured, and explained — then uses those patterns to generate new code, fix bugs, or suggest improvements.

What Are Actually AI Code Generators?

They are transformer-based AI models (such as Google Gemini, GPT-style models, Microsoft Copilot, Code Llama, StarCoder) specialized for programming tasks. They operate by predicting the next token in a sequence — but those tokens can be:

• Keywords (if, class, return)
• Variable names
• Entire functions
• Comments and documentation
• Configuration files
• Test cases

This makes those AI Code Generators capable of producing everything from small snippets to full modules.

How Do AI Code Generators Function Operationally?

1. You provide context
   • A comment
   • A partially written function
   • A bug
   • A natural-language request
2. The model analyzes the context It uses attention mechanisms to understand dependencies, variable scope, libraries, and patterns.
3. It predicts the most likely continuation This could be a line of code, a block, or a full implementation.
4. It refines output using natural-language reasoning Modern code models combine code understanding with general reasoning, enabling:
   • refactoring
   • optimization
   • security suggestions
   • documentation generation

What Are AI Generators Trained On?

• Public open-source repositories
• Programming textbooks and documentation
• Issue trackers, and Q&A forums
• Synthetic code generated during training

Enterprise-grade systems do not train on private customer code unless explicitly contracted.

Why Are AI Code Generators Useful?

• Accelerate development
• Reduce boilerplate
• Improve consistency
• Catch bugs early
• Help junior developers learn patterns
• Assist senior developers with repetitive tasks

AI Code Generators do not replace Computer Engineers — they complement them.

The Non‑Obvious Insight

The biggest AI Governance failures in SMEs do not come from advanced AI systems — they come from employees casually pasting sensitive data into general‑purpose AI tools. That is why an AI Governance Framework (AIGF) must cover both enterprise‑level AI systems and everyday employee usages.

FIGURE 2 – Summary Table: What Type of AI Usage Needs an AI Governance Framework?

SECTION III

SAMPLE OF A POLICY-DRIVEN & PRESCRIPTIVE AI GOVERNANCE FRAMEWORK FOR SMEs

Below is a sample of an AI Governance Framework (AIGF) tailored for SMEs. This is a policy-driven and prescriptive AIGF for the benefits of SMEs.

AI Governance Framework (AIGF): Sample AIGT for a Small and Medium‑Sized Enterprise (SME)
Policy-Driven & Prescriptive

Summarized on next page is a flowchart showing AI approval and risk classification steps.

SECTION IV

SAMPLE OF A LIGHTWEIGHT AI GOVERNANCE FRAMEWORK FOR STARTUPS (NIST-ALIGNED)

SECTION V

AI Governance Framework – Employee Acknowledgment & Compliance Template
SMEs Version

SECTION VI

TRAINING, AWARENESS & CULTURE OF AI GOVERNANCE FRAMEWORK FOR SMEs

Training, Awareness & Culture for an SME AI Governance Framework Takeaway

For SMEs, the real differentiator in safe and effective AI adoption is not technology—it is people. A strong AI governance culture ensures employees understand how to use AI responsibly, avoid risks, and recognize when to escalate issues. Training and awareness turn governance from a policy document into daily behavior.

Executive Summary

SMEs need a culture where:

• Employees understand AI risks
• Leaders model responsible AI behavior
• Teams follow consistent procedures
• Everyone knows what is allowed, what is prohibited, and why

This requires three pillars:

1. Training – skills and knowledge
2. Awareness – communication and reinforcement
3. Culture – leadership, norms, and accountability

Together, they create a workforce that uses AI confidently, safely, and ethically.

1. Training Programs (Role‑Based & Lightweight)

SMEs do not need enterprise‑scale training programs. They need targeted, role‑specific modules that can be delivered in short, high‑impact sessions.

1.1 Core Training for All Employees

This covers:

• What AI is and how it works (non‑technical)
• Acceptable Use Policy (AUP)
• Data classification (what can/cannot be shared with AI)
• Privacy basics (PII, sensitive data, retention)
• Recognizing AI hallucinations
• How to report issues or incidents

Outcome: Every employee becomes a safe AI user.

1.2 Specialized Training for Key Roles

Process Owners

• Human‑in‑the‑loop responsibilities
• Output verification
• Escalation procedures

Technical Staff (Engineers, Analysts)

• Model testing, bias evaluation
• Data governance
• Monitoring and drift detection

Leadership

• AI risk appetite
• Strategic alignment
• Accountability and oversight

Outcome: Each role understands its governance responsibilities.

1.3 Annual Refresher Training

• Updated regulations (EU AI Act, privacy laws)
• New risks (prompt injection, vendor drift)
• Lessons learned from incidents

Outcome: Continuous improvement and regulatory alignment.

2. Awareness Mechanisms (Reinforcement & Communication)

Training is not enough—SMEs need ongoing awareness to keep AI governance top‑of‑mind.

2.1 Monthly AI Governance Bulletins

Short updates covering:

• New AI tools approved
• Policy changes
• Incident summaries (sanitized)
• Tips for safe AI usage

2.2 Visual Reminders

• Posters near workstations
• “Do not paste sensitive data into AI tools” reminders
• Quick‑reference cards for data classification

2.3 AI Governance Portal (Lightweight)

A simple SharePoint/Confluence page containing:

• Policies
• Intake forms
• Training materials
• AI Inventory
• Incident reporting form

Outcome: Employees always know where to find authoritative guidance.

3. Culture of Responsible AI (Leadership & Norms)

Culture is the hardest part—and the most important. SMEs need leadership that models responsible AI behavior and reinforces norms.

3.1 Leadership Modeling

Leaders must:

• Use AI responsibly
• Follow the same AUP as staff
• Avoid “shadow AI”
• Encourage transparency and reporting

3.2 Psychological Safety for Reporting

Employees must feel safe to report:

• AI errors
• Bias concerns
• Data misuse
• Unexpected model behavior

No blame—only learning.

3.3 Embedding AI Governance into Daily Workflows

• Intake forms integrated into procurement
• Data classification integrated into onboarding
• AI risk checks integrated into project planning

Governance becomes a habit, not a hurdle.

FIGURE 5: Summary Table — Training, Awareness & Culture

SECTION VII

EXTERNAL ALIGNMENT & FUTURE-PROOFING OF AI GOVERNANCE FOR SMEs

External Alignment & Future‑Proofing for an SME AI Governance Framework Takeaway

External alignment ensures your AI governance framework stays compliant with evolving laws, interoperable with global standards, and credible to customers and partners. Future‑proofing ensures your framework adapts as AI capabilities, risks, and regulations evolve. For SMEs, this is the difference between a governance system that becomes obsolete in 12 months and one that remains strategically resilient.

Executive Summary

SMEs must align their AI governance with external expectations (regulators, customers, partners, industry standards) and build future‑proofing mechanisms that keep the framework current. This requires four pillars:

1. Regulatory Alignment – mapping to global AI and privacy laws
2. Standards Alignment – interoperability with ISO, NIST, OECD, G7
3. Ecosystem Alignment – vendors, partners, supply chain
4. Future‑Proofing Mechanisms – horizon scanning, update cycles, change triggers

Together, these create an AI governance framework that is credible, compliant, and resilient.

1. Regulatory Alignment (Global, National, Sectoral)

SMEs must track and align with the minimum viable set of AI‑related regulations. This avoids legal exposure and builds trust with customers.

1.1 Core Regulatory Anchors

• EU AI Act – global reference model for risk‑tiered governance
• GDPR / PIPEDA / provincial privacy laws – data protection obligations
• Employment & human rights laws – AI in hiring, performance, and HR
• Consumer protection laws – transparency, fairness, misleading outputs

1.2 Alignment Controls

• Maintain a regulatory obligations register
• Conduct annual compliance reviews
• Document legal bases for data used in AI
• Ensure transparency notices for customer‑facing AI

Outcome: Your AI Governance Framework remains legally defensible.

2. Standards Alignment (ISO, NIST, OECD, G7)

Standards provide structure, credibility, and interoperability.

2.1 ISO Alignment

• ISO/IEC 42001 – AI Management System (AIMS)
• ISO/IEC 23894 – AI risk management
• ISO/IEC 27001 – information security
• ISO/IEC 27701 – privacy information management

2.2 NIST AI RMF Alignment

Map your controls to the NIST lifecycle:

• Map → Measure → Manage → Govern

2.3 OECD & G7 Alignment

• Trustworthy AI principles
• Human‑centric design
• Transparency and accountability

Outcome: Your AI Governance Framework is compatible with global best practices.

3. Ecosystem Alignment (Vendors, Partners, Supply Chain)

SMEs rely heavily on external AI tools. External alignment ensures vendor risk does not become your risk.

3.1 Vendor Governance Controls

• Vendor due‑diligence checklist (security, privacy, model behavior)
• Contractual clauses for:
  • Data retention
  • Model updates
  • Sub‑processors
  • Incident notification
• Annual vendor risk review
• Monitoring for vendor drift (silent model changes)

3.2 Partner & Customer Alignment

• Provide customers with AI transparency statements
• Align with partner requirements (e.g., enterprise clients demanding AI controls)
• Maintain audit‑ready documentation for external assessments

Outcome: Your AI Governance Framework integrates smoothly into your business ecosystem.

4. Future‑Proofing Mechanisms (Adaptability & Resilience)

This is where SMEs often fail. Future‑proofing ensures your governance framework evolves with AI.

4.1 Horizon Scanning

Quarterly review of:

• New AI regulations
• Emerging risks (deepfakes, synthetic data, emergence of Agentic AI)
• Vendor updates
• Industry best practices

4.2 Change Triggers

Your framework must update when:

• A vendor updates its model
• New data sources are added
• A new AI tool is introduced
• A major incident occurs
• Regulations change

4.3 Governance Review Cycle

• Quarterly: operational controls, AI inventory, risk assessments
• Annually: full governance framework review
• Every 2–3 years: strategic overhaul

4.4 Continuous Learning Loop

• Lessons learned from incidents
• Feedback from employees
• Updates to training and AUP
• Integration of new controls

Outcome: Your governance framework stays relevant, resilient, and credible.

FIGURE 6: Summary Table — External Alignment & Future‑Proofing

4.5 Future Trends: Emergence of Agentic AI

Unlike traditional AI systems that primarily generate content or provide recommendations, Agentic AI can plan tasks, make decisions, interact with multiple systems, and execute actions with limited human intervention. As Agentic AI becomes more widely adopted, governance frameworks should address issues such as delegated decision-making authority, human oversight, accountability, access controls, auditability, monitoring of autonomous actions, and safeguards to prevent unintended outcomes. Agentic AI is all about giving AI systems the ability to take initiative, not just respond. Instead of waiting for a prompt, an Agentic System can plan, decide, and act toward a goal using tools, memory, and reasoning.

Agentic AI in a Nutshell

Agentic AI refers to artificial intelligence systems designed to operate as autonomous agents capable of pursuing goals, generating plans, making decisions, and executing multi‑step actions with minimal human intervention. These systems integrate reasoning, memory, tool use, and adaptive feedback mechanisms to act in dynamic environments and modify their behavior based on outcomes. Agentic AI = AI that can autonomously break down goals, make decisions, use tools, and execute multi‑step actions without needing constant human instructions.

What Are the Main Properties of Agentic AI?

• Autonomy: The system initiates actions rather than waiting for explicit prompts.
• Goal‑orientation: It interprets objectives and decomposes them into actionable steps.
• Planning & reasoning: It constructs and revises plans using logical, probabilistic, or learned strategies.
• Tool use: It interacts with external systems (APIs, software, databases) to accomplish tasks.
• Memory & state management: It tracks context, progress, and prior actions to inform future decisions.
• Self‑evaluation: It assesses outcomes and adapts its strategy through iterative refinement.

What Does Make an AI “Agentic”?

• Goal‑driven behavior — You give it an objective, and it figures out the steps.
• Autonomous planning — It creates and updates its own plan as conditions change.
• Tool use — APIs, databases, software, browsers, code execution.
• Memory & context management — It keeps track of what it has done and what remains.
• Self‑correction — It can evaluate its own output and retry or adjust.

Why Does Agentic AI Matter?

It shifts AI from static assistants to dynamic operators capable of:

• Running workflows end‑to‑end
• Monitoring systems and acting on triggers
• Coordinating multiple sub‑agents
• Performing complex research or operations without supervision

Regarding future trends, Agentic AI is the direction modern AI systems are moving toward—more autonomy, more reasoning, and more ability to handle real‑world tasks.

RESOURCES & REFERENCES

1. Thang Le Dinh, Manh‑Chiên Vu, Giang T.C. Tran. Artificial Intelligence in SMEs: Enhancing Business Functions Through Technologies and Applications. MDPI Open Access Journals – Volume 16, Issue 5, 18^th May 2025. Artificial Intelligence in SMEs: Enhancing Business Functions Through Technologies and Applications
2. Government of Canada – G7 Industry, Digital and Technology Ministers’ Meeting 2025. G7 Hiroshima AI Process (HAIP) – Toolkit for SMEs Deploying AI (2025). Toolkit for small- and medium-sized enterprises (SMEs) deploying artificial intelligence (AI)
3. Ashley Marshall. AI Governance Frameworks Every SME Needs in 2026. Precise Impact AI Inc. 14^th March 2026. AI Governance Frameworks Every SME Needs in 2026
4. Government of Canada – Statistics Canada. Analysis on Artificial Intelligence Use by All Types of Businesses in Canada, Second Quarter of 2025. Analysis on artificial intelligence use by all types of businesses in Canada, second quarter of 2025
5. Government of the USA – National Institute of Standards and Technology (NIST). NIST AI Risk Management Framework (AI RMF 1.0). 7^th April 2026. AI Risk Management Framework | NIST
6. European Union – The EU AI Compliance Kit Governed by the EU AI Act. AI Governance Framework for SMEs (2026): 6 Building Blocks. AI Governance Framework for SMEs: 6 Building Blocks
7. World Economic Forum – Davos, Switzerland. Advancing Responsible AI Innovation: A 2025 Playbook for All Types of Organizations. 22^nd September 2025. Advancing Responsible AI Innovation: A 2025 Playbook for All Types of Organizations | World Economic Forum
8. International Business Machines – IBM. Think 2026 – Turn Agentic AI into Real Business Value. Implementing AI Governance: A Practical Guide. 20^th February 2026. Guide for Implementing an AI Governance Framework | IBM
9. ALCEA Consulting Inc. Demystifying AI Governance: A Comprehensive Guide for SMEs to Thrive in the Age of AI. 17^th April 2024. Demystifying AI Governance: A comprehensive guide for SMEs to thrive in the age of AI – Alcea Consulting
10. The Control Check. Step-by-Step AI Governance Framework for SMEs Amidst Google Gemini Adoption Trends. 29^th December 2025. Step-by-Step AI Governance Framework for SMEs Amidst Google Gemini Adoption Trends
11. Databricks Inc. – San Francisco, California, USA Headquarters. AI Governance Best Practices: How to Build Responsible and Effective AI Programs. 18^th November 2025. AI Governance Best Practices: Frameworks & Principles | Databricks Blog
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13. IIENSTITU – Online Advanced IT Courses & Certifications for Small Businesses. Marco dela Cruz. AI Risk Assessment: SMEs’ Path to Compliance in 2026. 16^th May 2026. AI Risk Assessment: SMEs’ Path to Compliance in 2026 – IIENSTITU
14. I LIKE AI – AI Deontology, Ethics, Professional Practices & Policy Issue. Conducting an AI Risk Audit: A Simple Framework for SMEs. 3^rd May 2025. Conducting an AI Risk Audit: A Simple Framework for SMEs – I LIKE AI
15. Digital Governance Council – CAN/DGSI 101:2025 – Ethical Design & Use of AI by Small and Medium Organizations (Canada). Future-Proofing AI, CAN/DGSI 101:2025 The Latest Standard Revolutionizes Ethical Tech for SMEs. 8 January 2025. Future-Proofing AI, CAN/DGSI 101:2025 The Latest Standard Revolutionizes Ethical Tech for SMEs – Digital Governance Council
16. Haggai Roitman. The Hitchhiker’s Guide to Agentic AI: From Foundations to Systems. Published on 22^nd June 2026 by arXiv Independent Nonprofit Organization. 603-page PDF format available for free: [2606.24937] The Hitchhiker’s Guide to Agentic AI: From Foundations to Systems

Contributions

Special thanks for the financial support of the National Research Council Canada (NRC) and its Industrial Research Assistance Program (IRAP) benefitting innovative SMEs throughout the 10 provinces and 3 territories of Canada.

Eligible Canadian innovative SMEs can address their cybersecurity requirements by obtaining financial assistance for compliance readiness and certification audits. If you would like more information about NRC IRAP, please consult: About the NRC Industrial Research Assistance Program or reach out to your NRC IRAP Industrial Technology Advisor.

Newsletter Executive Editor:

Alan Bernardi, SSCP, PMP, Lead Auditor for ISO 27001, ISO 27701 and ISO 42001
B.Sc. Computer Science & Mathematics, McGill University, Canada
Graduate Diploma in Management, McGill University, Canada

Author-Amazon USA, Computer Scientist, Certified Professional Writer & Translator:

Ravi Jay Gunnoo, C.P.W. ISO 24495-1:2023 & C.P.T. ISO 17100:2015
B.Sc. Computer Science & Cybersecurity, McGill University, Canada
B.Sc. & M.A. Professional Translation, University of Montreal, Canada

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