AI Security in Enterprise Quebec: ChatGPT and Copilot Governance
Protect your enterprise from GenAI risks. Shadow AI, prompt injection, data leakage, Law 25 compliance. AI policy, audit, governance.
Problem
Your employees use ChatGPT and Copilot, but you have no visibility into shared data and no control policy.
Expected outcome
A clear AI policy, technical controls, governance, and documented Law 25 compliance.
The AI Security Paradox
Adoption of generative AI tools is exploding in enterprises. ChatGPT, Copilot, Gemini are now used daily. Yet 70% of organizations have no policy controlling these uses. Result: data leaks, Law 25 violations, compliance risks, and prompt injection attacks.
- GenAI adoption: 72% of enterprises use at least one public AI tool.
- Shadow AI: 90% of usage happens without IT or security approval.
- Data leakage: Employees share confidential data out of habit.
- Law 25 risk: Personal data sent to non-compliant services.
The AI Threat Landscape
Four risk categories dominate AI security in enterprise:
- Shadow AI: Unapproved tools (personal ChatGPT, Chrome extensions, third-party APIs). No security contracts.
- Prompt Injection: Attackers manipulate AI agents via malicious prompts to bypass guardrails.
- Data Leakage: Employees copy customer data, trade secrets, source code into ChatGPT.
- Compliance Gap: Copilot, ChatGPT store data on US servers, potentially violating Law 25 and GDPR.
Detection and Monitoring of AI Usage
Before implementing controls, you must see what exists. Three approaches:
- Endpoint discovery: EDR, device inventory to identify ChatGPT, Copilot, Claude on endpoints.
- Network monitoring: DLP, proxy logs to detect exfiltration to AI tools.
- User survey: Internal survey to identify shadow AI, common usage, awareness.
AI Governance and Policy
A robust AI policy must cover:
- Permitted data: Explicit list (public, marketing content). Prohibited: customer data, secrets, code.
- Approved tools: Only ChatGPT Premium (enterprise contracts) and Copilot Pro (Microsoft license). Prohibition on personal tools.
- Use case approval: Some uses (summarization, brainstorming) are approved. Others (customer data analysis) require security approval.
- Incident response: Clear plan if sensitive data is accidentally exposed.
60-Day Journey: Operational Governance
Here's how to implement controls and governance:
- Weeks 1-2 (Discovery): Endpoint audit, interviews, shadow AI identification. Sensitive data inventory.
- Weeks 3-4 (Policy): AI policy writing, data classification, leadership approval.
- Weeks 5-6 (Deployment): Activate enterprise Copilot, DLP rules, communication. Employee training.
- Weeks 7-8 (Monitoring): Set up alerts, audit trails, incident response procedure.
Frequently asked questions
Is ChatGPT safe for enterprise data?
ChatGPT stores data by default to improve the model. Only ChatGPT Premium with enterprise contract can offer privacy. For sensitive data, total prohibition or case-by-case approval.
What can we do with ChatGPT and Copilot?
Permitted: summarizing public content, brainstorming, marketing writing. Prohibited: customer data, trade secrets, source code, financial data, Law 25 secrets.
How do we control Copilot at scale?
Deploy Copilot Pro via Microsoft, configure Azure AD, enable logging and audit trails, integrate DLP to block sensitive data exfiltration.
Does Law 25 allow AI tool usage?
Yes, if personal data does not leave Quebec/Canada. This excludes public ChatGPT/Copilot. Use compliant solutions or request explicit consent.
What is prompt injection and how do we prevent it?
Prompt injection: attackers manipulate LLM via user input. Prevention: input validation, separating instructions/data, approval gates, suspicious prompt monitoring.
What if an employee shares sensitive data with ChatGPT?
Incident response: 1) Delete ChatGPT thread. 2) Notify leadership and compliance. 3) Assess exposure scope. 4) Retraining.
Do we need specialized tools to monitor AI?
DLP, EDR, proxy logs enable basics. For advanced visibility, specialized GenAI monitoring tools (Lakera, Robust Intelligence) add detection.
How long to implement AI governance?
60-90 days for basics (policy, Copilot, DLP). Continuous optimization after.
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