What is Shadow AI?
Shadow AI refers to the unauthorized or unmonitored use of artificial intelligence tools in workplace settings. Unlike officially sanctioned platforms, these tools often enter workflows without input from IT, compliance, or legal teams. According to findings from IBM’s Cost of a Data Breach Report 2024, 35% of data breaches between between March 2023 and February 2024 involved shadow IT.
Why it’s growing — and why that’s a problem
Employees turn to AI to:
– Draft documents and summarise meetings
– Analyse spreadsheets and financials
– Generate interview questions
– Power customer auto-replies
The catch? These tools may store data outside of Canada or reuse it to improve their models, without your knowledge. And under PIPEDA, your organisation is still responsible.
Three major risks of Shadow AI for SMBs
1. Data privacy violations
Uploading personal or sensitive data into uncontrolled AI tools may breach Canada’s PIPEDA law, even if the user had no ill intent.
2. Security vulnerabilities
Phishing emails now mimic internal tone and context. AI-generated malware adapts faster than traditional security can block it.
3. Regulatory exposure
Data that crosses borders without explicit consent could put your organisation at risk—legally and reputationally.
Paid Copilot vs “free” AI: Why the distinction matters
Licensed Microsoft 365 Copilot:
– Keeps data inside your Microsoft environment
– Respects your organisation’s retention policies
– Provides audit logging and admin control
Free or consumer-grade AI tools:
– Often store or reuse prompts
– Rarely guarantee where data resides
– Lack of visibility and compliance assurance
In short, the wrong AI tool could turn your business data into someone else’s training material.
A 4-phase roadmap for safe, strategic AI adoption
Phase #1 – Contain
Strategy: Stop uncontrolled AI use.
- Block or throttle suspicious AI URLs using firewall rules.
- Run a short employee survey to identify tools already in use.
Phase #2 – Envision
Strategy: Align AI with business priorities.
- Meet with senior leadership to define AI goals.
- Identify high-impact use cases (e.g., Customer experience, efficiency, insights).
Phase #3 – Pilot
Strategy: Test value under IT supervision.
- Grant Copilot access to a limited group (e.g., execs, IT).
- Define access permissions, data classes, and monitoring requirements.
Phase #4 – Scale
Strategy: Democratise AI—safely.
- Offer security and compliance training to teams.
- Gradually enable approved tools, monitor usage, and refine quarterly.
Quick-check: Is your AI usage secure?
- Do we have a clear, plain-language AI policy?
- Are approved and restricted tools documented?
- Is AI traffic being logged like other SaaS tools?
- Does each use case meet PIPEDA requirements?
If you said “no” to any of these, it’s time to take action. Review your policies, then schedule a consultation with us to review your findings and action plan. We’re here to offer insights and guidance.
Case in point: How one Canadian non-profit regained control
A growing nonprofit organization discovered that its staff were testing donor data in publicly available AI tools. Rather than issuing blanket bans, they partnered with Third Octet to:
1. Lock down external AI endpoints
2. Map responsible AI use cases with each team
3. Deploy Copilot under strict governanc
4. Deliver 1-hour workshops on AI privacy and risk
Result: Report-writing time decreased by 30%, and they became audit-ready.
Next steps: Ready when you are
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