CipherNorth’s Foundational Framework for Responsible GenAI Adoption

Why a Foundational Framework?

Not every organization is ready to adopt a full AI governance framework like ISO/IEC 42001, NIST AI RMF, or OWASP Top 10 for LLMs. But waiting to establish guardrails can expose you to real risks such as data leakage, misuse, compliance gaps, and reputational harm.

At CipherNorth, we recommend a practical foundational framework: the bare minimum set of policies, processes, and safeguards you should have in place if you plan to use Generative AI (GenAI). This framework draws from trusted sources like NIST Cybersecurity Framework (CSF), NIST AI 600-1, and SP 800-53 but is simplified for organizations that need to get started now.

Core Components of the Framework

1. Policies: Define When, Where, Why, and How GenAI Can Be Used

• Establish clear acceptable use policies for GenAI.

• Distinguish between:

  • Buying vs. Building: purchasing a third-party GenAI product vs. developing your own.

  • Using within a tool vs. building with it: leveraging embedded features (e.g., Microsoft Copilot) vs. developing applications with services like AWS Bedrock or OpenAI APIs.

2. Onboarding GenAI Like Any Other Technology, With Added Safeguards

• Create a GenAI onboarding process that mirrors your standard technology adoption review.

• Add unique checkpoints:

  • Model risk review: Assess bias, hallucination risks, and misuse potential and calculate a risk ranking for each model.

  • Inventory: Track models in use, where they are deployed, and who owns them.

  • Risk documentation: Note risks that cannot yet be mitigated and who accepted them.

3. Training: Build GenAI Awareness Across Teams

• At a minimum, provide foundational GenAI training for all employees.

• Require security and technology teams to complete technical training (e.g., AWS AI Foundational Certification).

• For defenders and red teams, invest in advanced training tailored to prompt injection, model poisoning, and adversarial testing.

4. Legal and Contractual Safeguards

• Ensure contracts explicitly address:

  • Data protections (ownership, residency, encryption).

  • Data sharing rules (e.g., your inputs not being used for retraining).

  • Incident handling and notification obligations.

  • Intellectual property and liability boundaries.

5. Vendor and Architecture Review

• Require transparency from vendors on:

  • Whether your data is used for training or fine-tuning.

  • Whether other customers can access models influenced by your data.

  • Security controls protecting your inputs and outputs.

• Conduct architecture reviews of third-party GenAI tools before approval.

Conclusion

This foundational framework is not as comprehensive as ISO 42001 certification or full adoption of NIST AI RMF, but it provides the bare minimum guardrails organizations need to safely begin leveraging GenAI. Think of it as building a secure foundation.