Model Online / On-Site
Duration
Modules 5 modules

Course Objective & Scope

Employees already use AI tools daily to draft emails, summarise documents and write code; without a clear corporate policy, trade secrets and personal data easily leak into public consumer models. This course teaches teams how to write clear, enabling and enforceable corporate AI policies rather than blanket bans that get ignored.

It covers the Employee Acceptable Use Policy (AUP) for generative AI, data classification rules for prompts, human verification and disclosure norms, standards for AI procurement and internal development, and enforcement mechanisms. In the workshop, participants draft a customized Corporate AI Policy document ready for internal review.

Who Should Attend?

  • Legal, compliance, internal control and regulatory affairs teams
  • Information security, data privacy (KVKK/GDPR) and IT policy owners
  • HR, internal communications and organisational development managers
  • AI governance committee members and transformation leads

Key Takeaways

  • Structure a modular policy suite covering employee acceptable use, AI development and procurement
  • Map corporate data classification tiers to approved and prohibited AI tools
  • Specify mandatory human verification, citation and external disclosure rules for AI outputs
  • Define policy rules for third-party SaaS AI features, API usage and open-weight models
  • Design a rollout, attestation and role-based communication plan that drives real compliance

Curriculum

Corporate AI Policy Architecture
  • Modular policy design: Master AI Policy, Employee Acceptable Use Guide and Technical Standards
  • Aligning with existing information security, privacy, code of conduct and procurement policies
  • Scope covering employees, contractors, partners and all AI/ML systems
Employee Acceptable Use Rules and Data Handling
  • Distinguishing approved enterprise-grade AI tools from public consumer tiers
  • Traffic-light data rules: what must never enter a prompt (PII, trade secrets, client data, credentials)
  • Rules on signing up with corporate email addresses and installing AI browser extensions
Output Verification, Disclosure and Prohibited Uses
  • Mandatory human review for accuracy, bias and copyright ('you own the output')
  • Disclosure requirements for customer-facing, legal or regulatory deliverables
  • Restrictions on automated HR, credit or consequential decision-making
Procurement, Internal Development and Third-Party SaaS AI Rules
  • Approval gates for buying new AI tools or enabling embedded AI inside existing SaaS platforms
  • Mandatory no-training / zero-data-retention clauses in vendor contracts
Workshop: Policy Drafting, Rollout and Enforcement
  • Creating a one-page visual 'Do's and Don'ts' quick reference guide
  • Employee attestation, incident reporting channels and periodic policy review

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