Combined Developer + Deployer Obligations
Organizations that both develop AI internally and deploy those systems for consequential decisions must comply with both sides of the developer/deployer split that runs through most US AI laws.
Governance routing
Use the AI governance guide to assign decision rights, committees, lifecycle gates, and escalation paths before mapping role-specific duties into the AI compliance framework register.
Obligations under US laws
- data handlingFlorida AI Legislation (Deepfake and AI Disclosure Laws)Fla. Stat. § 836.13 (HB 757 / Brooke's Law)
Do not willfully create, possess with intent to promote, solicit, or produce an altered sexual depiction of an identifiable person without consent, including AI-generated deepfakes. Covered platforms must remove altered sexual depictions within 48 hours of a valid takedown request. Per-image third-degree felony exposure.
Deadline: 48_hour_takedown
Framework controls
Maintain documentation throughout the AI system lifecycle including data management, system development, verification and validation, and deployment per Annex A.6 controls.
Conduct AI system impact assessments and risk assessments addressing intended uses, deployment context, affected stakeholders, and mitigation of identified risks per Annex A.5 controls.
Establish, implement, maintain, and continually improve an AI management system (AIMS) covering policies, leadership commitment, roles, and integration with other management systems.
MANAGE function: prioritize and treat identified risks, allocate resources, and implement risk response strategies including mitigation, transfer, acceptance, or avoidance.
MEASURE function: assess, analyze, and monitor AI risks using both quantitative and qualitative methods, including bias evaluation, robustness testing, and explainability assessments.
MAP function: identify the context, intended uses, stakeholders, and risks of each AI system, including categorization of impacts on individuals, communities, and the organization.
GOVERN function: establish policies, processes, structures, and accountability for AI risk management across the organization, including senior leadership oversight and a risk-based culture.
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