AI Risk Management Policy Template: What Colorado SB 24-205 Actually Requires

Section 6-1-1002 of Colorado's AI Act requires every deployer of high-risk AI systems to implement and maintain a "risk management policy and program." This isn't a suggested best practice — it's a statutory obligation with enforcement teeth.

The policy must be:

• Documented — Written and accessible, not informal tribal knowledge
• Public-facing — Available to consumers, not locked in an internal SharePoint
• Operational — Describing actual practices, not aspirational platitudes
• Framework-aligned — Mapped to NIST AI RMF or ISO 42001 for the affirmative defense

Generic privacy policies won't satisfy this requirement. A risk management policy specific to AI systems, describing how you govern, monitor, and respond to AI risks, is a distinct and new legal obligation for most organizations.

Related: SB 24-205 compliance guide · NIST AI RMF mapping · 90-day buildout plan

Based on the statute's requirements and NIST AI RMF alignment, your policy must address these eight areas:

Section 1: Scope and Applicability
Define which AI systems are covered by the policy. Explain how you identify high-risk systems and distinguish them from lower-risk tools. Reference SB 24-205's definition of "consequential decisions" and your classification criteria.

Section 2: Governance Structure
Who owns AI compliance in your organization? Define roles, responsibilities, and escalation procedures. Name the person or team accountable for policy implementation, bias audit oversight, incident response, and AG notification. (NIST AI RMF: GOVERN function)

Section 3: AI System Inventory Process
Describe how you discover, register, and classify AI systems. Include your process for evaluating new AI tools before deployment and re-evaluating existing tools when they change. (NIST AI RMF: MAP function)

Section 4: Risk Assessment Methodology
Detail how you assess AI risks, including algorithmic discrimination. Describe the impact assessment process — what triggers an assessment, what it covers, how often it's updated. Reference specific statistical methodologies for bias detection. (NIST AI RMF: MEASURE function)

Section 5: Bias Monitoring and Auditing
Specify your bias audit schedule, methodologies (disparate impact ratio, statistical significance, demographic parity), thresholds, and alerting criteria. Describe how audit results feed into remediation processes.

Section 6: Consumer Disclosure Practices
Explain how and when consumers are notified that AI is involved in consequential decisions. Describe your disclosure format, timing, content, and appeal/human review process.

Section 7: Incident Response and AG Notification
Define what constitutes an "incident" under your policy. Describe the response workflow: detection, investigation, remediation, and the 90-day Attorney General notification procedure. (NIST AI RMF: MANAGE function)

Section 8: Record Retention and Evidence Management
Describe your three-year retention policy for all AI compliance records. Specify what's retained, where it's stored, who has access, and how evidence bundles are generated for AG or court production.

Aligning your policy to NIST AI RMF isn't just good practice — it's the foundation of your affirmative defense. Here's how each policy section maps to the framework:

When documenting your policy, explicitly reference the NIST subcategories each section addresses. This creates a clear, auditable link between your operations and the framework — exactly what the AG looks for when evaluating an affirmative defense claim.

After reviewing dozens of AI risk management policies, these are the mistakes that consistently weaken compliance posture:

• Generic language without operational specifics. "We are committed to responsible AI" means nothing. "We conduct monthly disparate impact analysis using the 4/5ths rule with a p < 0.05 significance threshold" is defensible.
• Missing governance ownership. Every policy needs a named responsible party. "The organization will ensure compliance" doesn't tell the AG who dropped the ball when something goes wrong.
• No version control. Policies evolve. Without version history (v1.0 dated January 2026, v1.1 dated April 2026), you can't demonstrate continuous improvement. The AG wants to see a living document, not a static artifact.
• Copying another company's policy. Your policy must describe your AI systems, your risk landscape, and your operational procedures. A template provides structure — the content must be yours.
• Hiding the policy. SB 24-205 requires public accessibility. Linking from your footer or privacy page is the minimum. "Available upon request" may not satisfy the disclosure requirement.
• No review cadence. A policy without a scheduled review process will become stale. Annual review at minimum, with additional reviews when you add new AI systems or experience an incident.

Writing an AI risk management policy from scratch takes weeks — researching requirements, mapping to NIST, drafting language, getting legal review, and iterating. CO-AIMS compresses this to days:

• Pre-built policy framework — Section-by-section structure aligned to SB 24-205 and NIST AI RMF, pre-populated with your organization's context
• Auto-populated system inventory references — Your policy automatically references the AI systems in your registry, their risk classifications, and their audit schedules
• Methodology documentation — Bias audit methodologies, thresholds, and monitoring schedules are already defined in your CO-AIMS configuration and can be directly cited
• Living document management — Version control, revision history, and automatic update prompts when your AI landscape changes
• Public hosting — Your policy is published at a stable URL you can link from your website, satisfying the public accessibility requirement

The goal is to eliminate the blank-page problem. Your policy should be a reflection of your actual governance practices — which CO-AIMS is already managing. The policy document formalizes what you're already doing.