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AI usage policies, governance frameworks, and regulatory alignment for organizations deploying generative AI.

Generative AI introduces risks that existing security and compliance programs were not designed to address: data leakage through prompts, third-party model dependencies, output accuracy liability, and regulatory requirements that are still being written. Organizations using GenAI tools — whether commercial APIs or internally deployed models — need governance structures that account for how these systems actually behave, not just how vendors describe them.
Jacobian's GenAI compliance work builds the policy and control layer for responsible AI deployment. This is distinct from model risk quantification (which focuses on ML model performance and bias). GenAI compliance addresses acceptable use, data handling, vendor assessment, audit trail requirements, and alignment with the EU AI Act, NIST AI RMF, and emerging sector-specific guidance.

Experience the advantages of working with certified compliance experts who understand your business needs
Defines what GenAI tools can be used for, under what conditions, and by whom, with controls that enforce the policy rather than relying on user discretion.

Maps current AI usage against EU AI Act obligations and NIST AI RMF practices, identifying gaps before regulators or clients ask.

Establishes assessment criteria for third-party AI services, including data handling terms, model provenance, and incident notification requirements.

From risk assessment to program management.
Document current and planned GenAI deployments, data flows, and the regulatory context applicable to each use case.
Build acceptable use policies, data handling requirements, human oversight controls, and audit trail specifications aligned to applicable frameworks.
Evaluate current state against policy requirements and framework obligations; prioritize and implement gap remediation.
Document current and planned GenAI deployments, data flows, and the regulatory context applicable to each use case.
Build acceptable use policies, data handling requirements, human oversight controls, and audit trail specifications aligned to applicable frameworks.
Evaluate current state against policy requirements and framework obligations; prioritize and implement gap remediation.
Why governance should precede widespread adoption.
| Feature | Unmanaged | Governed |
|---|---|---|
| Regulatory Risk | Unknown | Mapped |
| Shadow AI | Undetected | Policy-Controlled |

Build the data classification and retention foundation that GenAI governance policies depend on.
Explore ServiceCommon questions about generative AI governance.
Start with an AI usage inventory and gap assessment against applicable frameworks.