Building Your AI Risk Management Program: Zero to Compliant in 90 Days

Building an AI risk management program sounds daunting, but it doesn't require an 18-month enterprise transformation. Colorado SB 24-205 is prescriptive enough that the requirements are clear, and NIST AI RMF provides the structural framework. With focused execution, 90 days is sufficient for most organizations.

The key is to work in phases: foundation first, then systematic expansion, then ongoing operations. Don't try to boil the ocean in week one.

Week 1: Executive Alignment & Ownership

• Assign an AI compliance owner (can be part-time for smaller organizations)
• Brief leadership on SB 24-205 requirements and penalties
• Secure budget for compliance tooling ($199-$999/month for CO-AIMS)
• Create a shared compliance workspace

Week 2: AI System Discovery

• Audit every software tool in your tech stack
• Interview department heads about AI-powered features they use
• Review vendor documentation for machine learning and AI components
• Register each system in your CO-AIMS inventory

Week 3: Risk Classification

• Classify each AI system as high-risk or lower-risk under SB 24-205
• Document the decision rationale for each classification
• Prioritize high-risk systems for immediate assessment
• Create a timeline for addressing each system

Weeks 4-5: Risk Management Policy

• Draft your public-facing AI risk management policy
• Map policy sections to NIST AI RMF functions (Govern, Map, Measure, Manage)
• Define governance roles and escalation procedures
• Review with legal counsel and publish

Weeks 6-7: Impact Assessments

• Complete impact assessments for each high-risk AI system
• Document data sources, known risks, human oversight, and safeguards
• CO-AIMS auto-populates assessments from your registry — review and supplement
• Schedule annual reassessments

Week 8: Bias Audit Baseline

• Run initial bias audits for all high-risk systems
• Establish baseline metrics for disparate impact across protected classes
• Configure ongoing monitoring schedule (monthly recommended)
• Document methodology and thresholds

Week 9: Consumer Disclosures

• Implement disclosure notices for each high-risk AI system
• Choose disclosure patterns (banner, gate, contextual) per system
• Set up disclosure tracking and timestamping
• Configure appeal/human review workflows

Week 10: Incident Response

• Define what triggers an incident investigation
• Document the response workflow: detection → investigation → remediation → notification
• Set up 90-day AG notification deadline tracking
• Run a tabletop exercise with your team

Week 11: Record Retention & Evidence

• Verify all documentation is being stored with retention policies
• Test evidence bundle generation — can you produce a court-ready package?
• Set up automated backup and retention schedules

Week 12: Staff Training & Go-Live

• Train all staff involved in AI governance on their responsibilities
• Document training completion for compliance records
• Final review of all systems, policies, and processes
• You're compliant. Now maintain it.

Compliance isn't a destination — it's an operating rhythm. After initial implementation:

• Monthly — Automated bias audits run, results reviewed, anomalies investigated
• Quarterly — Governance review meeting, policy updates, new system classification
• Annually — Impact assessments updated, staff re-trained, evidence bundles generated for board review
• As-needed — Incident response when issues are detected, AG notifications within 90 days

CO-AIMS handles the cadence automatically. Audits run on schedule, deadlines are tracked, assessments are flagged for renewal, and evidence accumulates continuously. Your job is to review, decide, and lead — not to manually manage compliance logistics.