AI Governance Briefing — October 9, 2025
Zeph Tech is piloting Colorado AI Act impact assessments with deployer partners to meet Section 6-1-1706 requirements ahead of the February 2026 effective date.
Executive briefing: Colorado’s SB24-205 requires deployers of high-risk AI systems to complete annual impact assessments that evaluate algorithmic discrimination risks, document mitigation steps, and inventory data sources. Zeph Tech is co-developing assessment pilots with Colorado customers, filling templates, testing correction workflows, and aligning notice-and-appeal language with Section 6-1-1706 ahead of the February 1, 2026 effective date. The work ties directly to the AI pillar hub, the Colorado AI Act compliance guide, and adjacent AI governance briefs such as developer deliverables and readiness sprint to provide end-to-end visibility.
Methodology and context
Our pilots follow the statute and Attorney General implementation guidance. Section 6-1-1706 directs deployers to capture system purpose, data inputs, evaluation metrics, mitigation measures, and governance approvals. Assessments must refresh at least annually and after substantial modifications, with records retained for three years. The Attorney General’s implementation hub stresses that impact assessments, notice templates, and appeals processes will inform enforcement, so we are building evidence bundles that keep these artifacts synchronized.
Zeph Tech’s methodology uses four stages:
- Scoping. Identify consequential decision paths (employment, lending, housing, healthcare, education, insurance, essential government services) and map which models qualify as high-risk under SB24-205.
- Template population. Populate assessment fields with system purpose, data lineage, evaluation metrics, fairness mitigations, and human-review checkpoints. Map each field to statutory language to avoid gaps.
- Workflow integration. Embed assessment completion into CI/CD gates, ensuring no high-risk release proceeds without approved documentation. Link to notice and appeal triggers so transparency is consistent with assessed risks.
- Validation. Conduct red-team exercises and mock Attorney General inquiries to confirm evidence retrieval, correction handling, and appeal routing are audit-ready.
Stakeholder impacts
- Data science and engineering. Own the accuracy of system descriptions, datasets, model versions, evaluation metrics, and mitigation steps entered into assessments. They must align monitoring outputs with assessment updates.
- Product and UX. Translate assessment findings into consumer-facing notices and appeals content that accurately reflects model purpose and limitations.
- Legal and compliance. Validate that assessments cite Section 6-1-1706 elements, maintain records for three years, and coordinate responses to Attorney General requests.
- Trust & safety and support. Use assessment outputs to refine correction and appeal workflows, ensuring human review is meaningful and well-documented.
Control implementation mapping
| Assessment field | Operational control | Evidence |
|---|---|---|
| System purpose and scope | Architecture diagrams that show model boundaries and integrations with decision systems. | System design docs, dependency maps, decision trees. |
| Data inputs and limitations | Data inventories with lineage, quality checks, and documented exclusions to avoid bias. | Dataset catalogs, sampling reports, data quality dashboards. |
| Evaluation metrics and mitigation | Fairness tests (e.g., disparate impact analysis) and mitigation playbooks applied before release. | Test results, threshold rationale, mitigation commits, reviewer approvals. |
| Governance approvals | Multi-function sign-off (legal, risk, product) before deployment or major changes. | Approval records, meeting minutes, versioned assessment archives. |
| Notice and appeal alignment | Templates that mirror assessed risks and explain correction routes with human oversight. | Published notice text, appeal workflows, training completions. |
These controls dovetail with the AI model evaluation guide for testing rigor and the incident response guide for discrimination escalation, ensuring assessments are not static documents but living operational artefacts.
Detection and response priorities
- Monitoring integration. Stream model monitoring dashboards into the assessment repository so signals of drift or disparate impact automatically flag assessment updates and possible notification obligations.
- Consumer transparency drills. Validate notice templates and appeal workflows through usability tests, ensuring instructions for correction and human review are understood across languages and channels.
- Attorney General readiness. Stage mock inquiries that require producing the past three years of assessments, notices, appeals, and mitigation evidence to confirm retrieval speed and completeness.
- Lifecycle management. Track when systems undergo substantial modification and ensure assessments are refreshed with new data inputs, metrics, and mitigation narratives.
Data Science -> Populate purpose, data, metrics -> Submit assessment
Engineering -> Gate releases -> Link notices/appeals -> Ship with evidence
Legal/Compliance -> Review & archive -> Prepare AG response kits
Support/T&S -> Train on risks -> Handle corrections -> Escalate discrimination
Metrics to prove effectiveness
- Assessment completion rate. Percentage of high-risk systems with current assessments; target 100% before go-live.
- Fairness mitigation velocity. Time from detection of a disparity to deployment of mitigation and documentation of results.
- Notice/appeal coherence. Audit samples comparing assessment narratives to notice language to ensure consumers receive accurate descriptions of purpose, data inputs, and limitations.
- Evidence retrieval speed. Time to assemble a three-year assessment archive and corresponding notices/appeals when prompted by mock Attorney General requests.
Actionable checklist
- Finalize the Colorado-specific assessment template and roll it into CI/CD gates for all high-risk AI systems.
- Complete at least two pilot assessments with Colorado customers, capturing purpose, data lineage, metrics, mitigations, and sign-offs.
- Link assessment outputs to consumer notices and appeal scripts so transparency reflects documented risks.
- Test monitoring-to-assessment automation by triggering a mock disparity alert and observing update workflows.
- Archive all assessment artefacts with three-year retention and rehearse production for Attorney General requests.
By operationalising assessments as living documents tied to monitoring, notices, and appeals, Zeph Tech customers can show Colorado regulators that high-risk AI systems are evaluated, updated, and governed with verifiable evidence.
Scenario walkthroughs
Two illustrative walkthroughs are guiding the pilots:
- Loan pre-qualification. The assessment documents credit file inputs, income verification checks, geographic risk factors, and mitigation steps such as caps on model influence when data quality is low. Notices highlight these inputs, and appeals route to underwriters with authority to override the model.
- Hiring resume screen. The assessment catalogues features derived from resumes, interview transcripts, and skills tests; tests for adverse impact by gender and race proxies; and records mitigation such as balanced training sets and calibration. Notices explain that AI assists ranking, and appeals allow applicants to correct records or request human review.
Each walkthrough links assessment evidence to consumer-facing language so deployers can prove that disclosures match the documented model purpose and limitations.
Crosswalk to related controls
Colorado assessments sit alongside other governance artefacts. Zeph Tech is maintaining a crosswalk that references:
- Security controls. Access controls for assessment archives, audit logging for edits, and change-management records for model updates.
- Privacy controls. Data minimisation notes and correction workflows that dovetail with GDPR/CCPA rights-handling, reducing duplicate processes.
- Vendor management. Supplier attestations that third-party models or datasets meet Colorado assessment expectations and support downstream notices and appeals.
This crosswalk keeps assessors aligned with enterprise governance and reduces rework when regulators request evidence across domains.
Retention and audit preparation
SB24-205 requires deployers to retain assessment records for three years. Zeph Tech is standardising retention with immutable storage buckets, indexable metadata (system name, decision category, model version, assessment version), and export jobs that can bundle notices, appeals, and monitoring logs. Quarterly drills will rehearse producing these packets within strict deadlines to mirror Attorney General expectations.
Resource planning
Assessment quality depends on staffing. Zeph Tech recommends defining reviewer roles (data science, legal, risk) with explicit time allocations per assessment and backups for peak periods leading into February 2026. Tracking hours spent per assessment will help defend budgets and demonstrate “reasonable” efforts under SB24-205.
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