What Compliance Teams Should Do Before Colorado AI Act Enforcement Ramps Up

The Colorado AI Act was enacted in 2024 and then amended by SB25B-004 so that the SB 24-205 requirements become effective June 30, 2026. The window to get into compliant shape before the operative date is now.

What should be in place before June 30, 2026

1. AI system inventory with high-risk classification

For every AI system in use:

Description of purpose and intended use
Classification: high-risk (yes/no) per the high-risk definition
Role: developer, deployer, or both
Industries / decisions affected

If a system is not high-risk under Colorado law, document why — exclusions in C.R.S. § 6-1-1701(7)(b) apply to anti-fraud, cybersecurity, narrow technical functions, etc.

2. Impact assessment for every high-risk deployer-side system

Under C.R.S. § 6-1-1703(3), the impact assessment must address: purpose, intended outputs, performance, transparency, monitoring, and risks of algorithmic discrimination.

Use the Impact Assessment Generator for the baseline template.

3. Consumer disclosure mechanism

When a high-risk AI system is used in a consequential decision, the consumer must be notified. Build the disclosure surface into customer-facing flows before launch or before the June 30 operative date, whichever is later.

4. Right-to-correct and right-to-appeal

Build operational paths for affected consumers to: (a) correct inaccurate personal data, and (b) appeal adverse decisions to a human reviewer where technically feasible.

5. AG-notification process

If a team identifies algorithmic discrimination in a high-risk system, the law requires notification to the Colorado AG within prescribed timeframes. Build the internal escalation process now so the first occurrence does not generate a missed deadline.

6. Developer-deployer documentation flow

Developers providing systems to deployers should prepare documentation per C.R.S. § 6-1-1702: intended uses, harmful uses, training data summary, performance evaluations, and mitigation measures.

Deployers using vendor AI should request this documentation from vendors and add it to procurement checklists for new vendors.

Common pitfalls to avoid

Generic impact assessments: the assessment must be specific to the system. Boilerplate language is weak evidence of compliance.
No bias testing: writing about bias without testing for it. Run quantitative tests with documented methodology.
One-time assessment: the obligation is annual. Set a refresh cadence.
No consumer-disclosure path: documenting compliance internally without a consumer-facing surface is incomplete.

Adopt a federal framework

Adopting NIST AI RMF or ISO/IEC 42001 substantially supports Colorado AI Act compliance through their MAP / Annex A.5 controls. Treat the framework as the control baseline, with the Colorado-specific obligations layered on top.