What should an AI governance framework include?
Answer
A working AI governance framework should include a written AI use policy, an inventory of where AI makes or materially influences consequential decisions, documentation and record-keeping requirements, defined human-review points, incident-response procedures, and regular audits. Because Colorado's AI Act (SB 26-189) replaced the old impact-assessment and high-risk-classification model with disclosure, consumer-notice, and human-review duties, the framework should map to those obligations rather than the repealed assessment regime.
An AI governance framework should include an AI use policy, an inventory of where AI makes or substantially influences consequential decisions, documentation requirements, incident response procedures, and regular audit mechanisms. Note that Colorado's AI Act (SB 26-189, which repealed and reenacted SB 24-205) dropped the old impact-assessment and high-risk-classification model in favor of disclosure, consumer-notice, and human-review duties — so a framework should map to those obligations rather than the repealed assessment regime.
Sources checked
Scope
General governance guidance, not legal advice. The right components depend on your jurisdictions, industries, which decisions AI touches, how much autonomy tools have, and how your contracts and insurance allocate AI risk. Regulatory duties such as Colorado's change over time and vary by state, so confirm current obligations with qualified counsel.
Operational implication
A framework only reduces risk once it is operated, not just written. Gridex turns governance components into running workflow controls — the AI inventory, the human-review checkpoints, the audit trail, and insurance-ready documentation — so the policy maps to what actually happens when AI makes a consequential decision.
Applicable Regulations
Colorado AI Act — Automated Decision-Making Technology (SB 26-189, repeal & reenactment of SB 24-205)
On 2026-05-14 Governor Polis signed SB 26-189, which repeals and reenacts the Colorado AI Act (originally SB 24-205). The new law abandons the risk-management / annual-impact-assessment model and replaces it with a disclosure-and-notice framework governing "automated decision-making technology" (ADMT) that makes or substantially influences "consequential decisions" (education, employment, housing, financial services, insurance, healthcare, government services). The statute formally takes effect 2026-08-12 (no safety clause), but all substantive compliance obligations — for both deployers and developers — begin 2027-01-01, which is the operative date for regulated businesses; the Attorney General's implementing rules are also due by 2027-01-01. The AG has stated he will not enforce until the mandatory rulemaking process concludes.
Key Requirements
Full State Analysis
Where this lands operationally
Gridex turns the compliance or coverage question into operated workflow controls: intake, review points, audit trails, and the places a person stays in the decision.
Build Your AI Governance Framework
Turn the framework checklist into operated controls — build the AI decision inventory, set human-review points, and stand up the audit trail. Book a Gridex AI governance review to map it to your Colorado and multi-state obligations.
Build Your AI Governance Framework →Related Questions
- What is an AI impact assessment? An AI impact assessment is a documented evaluation of an AI system's potential risks, including bias, privacy, and safety impacts. Connecticut SB-1103 requires impact assessments before deploying high-risk AI systems. Note: Colorado originally required impact assessments under SB 24-205, but SB 26-189 (signed 2026-05-14) repealed that requirement — Colorado no longer mandates impact assessments and instead requires an ADMT disclosure-and-notice framework.
- What should an AI risk register include? An AI risk register should catalog each AI system, its risk classification, applicable regulations, data inputs, decision scope, last assessment date, responsible owner, and insurance coverage status — critical for both compliance and claims documentation.
- Who is liable when an AI agent causes harm? Liability for an AI agent's actions tends to resolve in layers. Default — deployer or operator: the business that puts the agent into operation is generally answerable for the harm it causes, much as it would be for an employee or a tool it chose to use, under established agency, vicarious-liability, and negligence principles. Vendor or developer: responsibility can extend upstream through product-liability, professional-liability (E&O), or misrepresentation theories where the harm traces to a defect or an overstated capability rather than the deployer's own setup. Contract and indemnity: master service agreements, warranties, limitation-of-liability clauses, and indemnities reallocate that risk between the parties and often decide who actually bears a loss. Insurance and exclusions: a policy may respond, but AI-specific exclusions such as Verisk's CG 40 47 can strip coverage a deployer assumed it had — changing who pays without changing who is legally liable. Human review and audit trail: where a person reviews the agent's decisions and every action is logged, that record shapes whether the deployer is found negligent and whether coverage responds. Outcomes vary by jurisdiction and the agent's degree of autonomy, and newer rules such as Colorado's AI Act (SB 26-189, deployer and developer duties effective January 1, 2027) can add obligations whose breach supports a claim. This is general business and insurance-risk analysis, not legal advice.
- What is the difference between an AI governance policy and procedure? An AI governance policy defines the organization's principles and risk tolerance for AI use. Procedures are the specific steps employees follow to comply — approval workflows, documentation templates, and review cadences required by state regulations.