How is this different from Jacobian's AI model risk management service?

AI model risk management focuses on evaluating ML model performance, accuracy, bias, and decision risk — typically for models your organization has built or fine-tuned. GenAI compliance addresses the governance and regulatory requirements around deploying commercial generative AI tools: usage policies, data protection, vendor assessment, and regulatory obligations. Most organizations using GenAI will need both, but they address different problems.

Our team uses ChatGPT and Copilot informally. Do we need a governance program?

Informal use is where most GenAI risk originates — employees entering client data, proprietary code, or regulated information into commercial AI systems without understanding how that data is handled. A governance program starts with visibility into what's being used and what data is in scope, then builds the controls around actual usage patterns.

GenAI Compliance

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

Governance for 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.

Trend analysis chart on a dark screen, representing the risk assessment and regulatory gap tracking in GenAI compliance programs

Why Choose Our GenAI Compliance

Engineering rigor, audit-ready process, and operational depth across cloud, SaaS, and software delivery

Acceptable Use Framework

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.

Regulatory Positioning

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

Vendor and API Governance

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

Our GenAI Compliance Process

From risk assessment to program management.

AI Inventory and Use Case Mapping

Document current and planned GenAI deployments, data flows, and the regulatory context applicable to each use case.

Policy and Control Development

Build acceptable use policies, data handling requirements, human oversight controls, and audit trail specifications aligned to applicable frameworks.

Assessment and Gap Closure

Evaluate current state against policy requirements and framework obligations; prioritize and implement gap remediation.

AI Inventory and Use Case Mapping

Document current and planned GenAI deployments, data flows, and the regulatory context applicable to each use case.

Policy and Control Development

Build acceptable use policies, data handling requirements, human oversight controls, and audit trail specifications aligned to applicable frameworks.

Assessment and Gap Closure

Evaluate current state against policy requirements and framework obligations; prioritize and implement gap remediation.

Unmanaged AI Adoption vs. Governed GenAI Program

Why governance should precede widespread adoption.

Feature	Unmanaged	Governed
Regulatory Risk	Unknown	Mapped
Shadow AI	Undetected	Policy-Controlled

Regulatory Risk

Unmanaged

Unknown

Jacobian Services

Mapped

Shadow AI

Unmanaged

Undetected

Jacobian Services

Policy-Controlled

Related Service

Data Governance Services

Build the data classification and retention foundation that GenAI governance policies depend on.

Explore Service

GenAI Compliance FAQs

Common questions about generative AI governance.

Get ahead of GenAI regulatory requirements.

Start with an AI usage inventory and gap assessment against applicable frameworks.