AI Policy Review
Develop and audit AI usage policies that protect your data, manage vendor risk, and satisfy compliance requirements.
Your Organization Is Already Using AI
Whether you've formally adopted AI tools or not, your employees almost certainly are — ChatGPT, Copilot, Claude, and dozens of AI-enhanced SaaS products are woven into modern work. The question isn't whether AI is in your organization. It's whether you have governance around how it's used.
Without clear policies, sensitive data flows into third-party AI models. Employees make consequential decisions based on AI outputs without appropriate review. Vendors with AI capabilities are adopted without security vetting. Regulatory exposure grows silently.
Helm's AI policy practice helps organizations build governance that enables responsible AI adoption — without creating blanket prohibitions that get ignored.
Frameworks We Reference
- NIST AI Risk Management Framework (AI RMF)
- EU AI Act (for applicable organizations)
- HIPAA implications for AI in healthcare
- FTC guidance on AI and consumer protection
- ISO/IEC 42001 AI Management System
- Organizational AI governance best practices
What an AI Policy Covers
Clear guidelines on which AI tools are approved, what data can be shared with them, and what types of decisions require human review before acting on AI output.
Data classification requirements for AI inputs — preventing sensitive, confidential, or regulated data from entering external AI systems without appropriate controls.
AI vendor evaluation requirements, data processing agreements, model training opt-out provisions, and due diligence standards for new AI tool adoption.
Defined ownership for AI governance, approval processes for new AI tool adoption, and escalation paths for AI incidents or policy violations.
Discovery process for unauthorized AI tool usage across the organization — the AI equivalent of shadow IT, and equally risky from a data governance perspective.
Additional governance requirements for AI used in consequential decisions — hiring, lending, healthcare, legal — where AI bias and errors have significant impact.
Policy Review vs. Policy Development
You have an existing AI policy (or section of an existing policy) and want expert review for gaps, framework alignment, and practical improvements.
Building AI governance from the ground up — assessment of your current AI tool landscape, risk profile, and development of a comprehensive AI use policy.