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As AI continues to create both opportunities and risks, governance and compliance teams face new challenges in third-party oversight. These five questions will help you evaluate how vendors develop, deploy, and secure AI technologies responsibly.

How do you govern your use of AI?

AI adoption without oversight can lead to shadow AI, regulatory violations, and reputational damage. Evaluate whether your vendor has an established AI governance framework that defines clear accountability, ethical guardrails, and compliance protocols.

Evaluate whether your vendors:

  • Follow recognized frameworks (e.g., NIST AI RMF, ISO/IEC 42001)
  • Have an internal AI policy or outline the use of AI in their Acceptable Use Policy
  • Show transparency around model development, training data, and decision processes
  • Have contractual agreements and confidentiality clauses with their vendors for AI development
  • Continuously monitor ethical and regulatory risks across teams?

 

Why it matters

Effective frameworks ensure innovation occurs safely within regulatory and ethical boundaries— enabling both progress and protection

Who owns and oversees AI risk?

AI risk is an enterprise-wide challenge that requires clear ownership and oversight. Leading organizations take a cross-functional approach, uniting security, legal, compliance, and business teams, often through AI Oversight Councils that set policies, review use cases, and ensure consistent, transparent governance.

Understand your vendors:

  • Who is responsible for AI oversight?
  • How do they ensure collaboration across departments?
  • Do they have an AI council or steering committee with a defined charter and review process?

 

Pro Tip

A lack of defined roles or formal oversight is a red flag. Clear ownership and governance structures drive alignment, transparency, and responsible innovation.

What due diligence is conducted on AI vendors and models?

As AI capabilities proliferate, due diligence must go deeper than a checkbox review. Leading organizations now assess AI model provenance, data lineage, and explainability during vendor onboarding.

Check with your vendors:

  • How do they continuously monitor their AI vendors?
  • How do they ensure collaboration across departments?
  • Are there documented lines of accountability for technical, ethical, and legal risks?

 

Why it matters

Contradictory answers or vague descriptions about data usage or AI frameworks signal risk. Look for documentation and demonstrable governance practices.

How do you balance innovation and compliance?

Innovation and compliance aren’t opposites, they’re partners. The best AI governance programs enable experimentation within safe, transparent boundaries.

Ask your vendors:

  • How do vendors evaluate AI use cases before deployment?
  • What mechanisms exist to test and approve new applications?
  • Is there a process for auditing and continuous improvement?

 

Why it matters

Strong governance unlocks innovation by making experimentation safe, traceable, and compliant.

How do you keep your organization safe while using AI?

AI is powerful and can be embedded within the technology stack, but is it being assessed from a security perspective?

Evaluate whether your vendors:

  • Have AI functionality and features been included within a penetration test
  • Have been tested against OWASP Top 10
  • Conducting internal audits and risk assessments

 

Why it matters

AI requires both strong oversight and ongoing technical validation to stay secure. Continuous assessment builds confidence and resilience against evolving threats.

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