Course Objective & Scope
Employees pasting customer records, financial spreadsheets, contracts, or proprietary code into consumer AI tools has become a primary vector for corporate data leakage. This course establishes clear, practical boundaries for data privacy and information security in daily AI usage.
Participants learn how consumer versus enterprise AI licenses handle data retention and model training, what KVKK and GDPR require regarding personal data transfers, and how to sanitize and anonymize documents before prompting. Teams leave with a practical Role-Based Safe AI Usage Checklist.
Who Should Attend?
- All business teams handling customer data, employee records, financial reports, or confidential documents
- Information security, data privacy (KVKK/GDPR), legal, HR, and compliance professionals
- Managers responsible for enforcing safe AI usage standards across their teams
- No technical background required
Key Takeaways
- Distinguish data retention and model-training policies between consumer accounts and enterprise AI tenants
- Classify corporate data and apply strict red lines for PII, financial secrets, and confidential strategy
- Comply with KVKK and GDPR rules governing personal data processing and cross-border AI services
- Apply practical anonymization, pseudonymization, and synthetic data techniques before prompting
- Avoid Shadow AI risks posed by unapproved web tools, browser extensions, and free file converters
Curriculum
How AI Services Process Data: Consumer vs. Enterprise
- What happens when you hit send: logging, abuse monitoring, human reviewers, and model training pipelines
- Privacy guarantees of Enterprise/Workspace tenants vs. free or personal subscription tiers
- Configuring opt-out settings and understanding the limits of temporary chat modes
KVKK, GDPR, and Corporate Data Classification
- Mapping public, internal, confidential, and restricted data tiers to approved AI tools
- Personal data (PII), sensitive categories, and cross-border transfer rules under KVKK and GDPR
- Case studies of real-world corporate leaks involving source code, meeting notes, and M&A documents
Practical Data Masking and Anonymization Techniques
- Replacing names, client identifiers, and sensitive figures with structured placeholders ([CLIENT_A])
- Using synthetic dummy rows to generate spreadsheet formulas without exposing real customer records
- Abstracting confidential business problems into structural questions before prompting
Shadow AI Threats and Safe Usage Checklists
- Risks of unvetted AI wrappers, free PDF chat sites, and malicious browser extensions
- Account security, MFA, and the danger of public 'Share Conversation' links
- Workshop: Creating a department-specific Safe AI Usage & Data Red-Line Checklist
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