Enterprise AI Governance Administration

EVENT

TIME

(Central Time Zone)

DATE

(all dates are State business days)

1.

RFI Issued

June 15, 2026

1.

Written Questions and Comments Deadline

2:00 pm

June 26, 2026

1.

State Response to Written Questions and Comments

July 6, 2026

1.

RFI Response Deadline

2:00 pm

July 13, 2026

RFI #31701-03847

TECHNICAL INFORMATIONAL FORM

1. RESPONDENT LEGAL ENTITY NAME:

1. RESPONDENT CONTACT PERSON:

Name, Title:

Address:

Phone Number:

Email:

1. Company & Platform Overview

a. Provide a general overview of your company, ownership structure, leadership team, and AI governance platform(s).

b. Describe your organization’s experience supporting enterprise AI governance programs.

c. Describe your experience supporting public sector or highly regulated environments.

d. Identify primary industries and customer types currently using your platform.

e. Support model: support tiers, SLAs, and availability (hours/regions).

1. AI Governance Administration Capabilities

a. Describe your platform’s capabilities related to: AI inventory management, intake workflows, governance approvals, policy management, risk classification, centralized reporting, model registration, governance lifecycle management

b. Describe how your platform supports enterprise governance visibility and management across agencies, departments, or business units.

c. Describe how your platform supports governance policy management and policy lifecycle administration.

d. Describe how the platform supports exception, waiver, and risk acceptance workflows, including request, review, approval, denial, expiration, renewal, compensating controls, notifications, reporting, and audit history.

1. Runtime Governance & Operational Integration

a. Describe how your platform integrates with AI runtime governance, AI gateways, orchestration layers, guardrails, or operational AI monitoring systems.

b. Describe any runtime governance capabilities natively supported by your platform.

c. Describe support for: prompt inspection, runtime guardrails, runtime policy evaluation, operational monitoring, shadow AI detection, RAG governance, and autonomous AI operational controls.

d. Describe your platform’s approach to operational AI governance visibility and runtime telemetry integration.

e. Describe support for autonomous agents and agentic workflows.

f. Describe support for delegated authority models.

g. Describe support for execution-time policy validation.

h. Describe support for agent authorization boundaries.

i. Describe integrations with AI gateways and runtime control planes.

j. Describe where governance policies are enforced within the AI request lifecycle.

1. AI Usage Intelligence, Cost Governance & Financial Accountability

a. Describe your platform's capabilities for AI usage metering, token consumption tracking, API call tracking, model utilization monitoring, and operational usage analytics.

b. Describe how AI usage can be attributed to specific agencies, departments, business units, applications, projects, use cases, users, or cost centers.

c. Describe support for AI cost governance, including cost allocation, chargeback mechanisms, showback reporting, budget tracking, cost forecasting, usage-based budgeting, and financial accountability workflows.

d. Describe how your platform supports monitoring and reporting of costs across multiple AI providers, models, cloud environments, SaaS AI services, and AI platforms.

e. Describe support for configurable spending thresholds, budget controls, usage quotas, approval requirements, automated alerts, or policy-based spending restrictions.

f. Describe capabilities for identifying anomalous usage patterns, unexpected cost increases, inefficient model utilization, or unauthorized AI consumption.

g. Describe integration capabilities with enterprise financial, procurement, accounting, ERP, budgeting, and IT asset management systems.

h. Describe how governance policies can be linked to financial controls, procurement controls, budget approvals, or cost accountability requirements.

i. Describe available executive dashboards, reporting capabilities, and analytics supporting enterprise AI financial governance and portfolio oversight.

j. Describe support for forecasting AI consumption growth, budget planning, capacity planning, and long-term AI investment management.

k. Describe support for tracking AI costs and utilization across multiple agencies, departments, programs, and business units.

l. Describe capabilities for normalizing usage and cost reporting across multiple AI vendors, model providers, cloud platforms, and AI services.

1. AI Security & Risk Management

a. Describe how your platform supports or integrates with AI security capabilities related to prompt injection detection, model abuse detection, data leakage prevention, model poisoning protection, unauthorized automation prevention, and runtime threat monitoring.

b. Describe how your platform integrates with enterprise security controls, including identity providers, secure web gateways (e.g., Zscaler), DLP solutions, SIEM/SOAR platforms, endpoint protection, and network security tools.

c. Describe support for governance controls related to sensitive, confidential, restricted, regulated, and personally identifiable information (PII), including data classification, access controls, data residency, privacy governance, privacy impact assessments, and compliance requirements.

d.

e. Describe controls preventing sensitive data exposure through prompts, retrieval systems(RAG), training, fine-tuning, embeddings, logs, and agent workflows.

1. Auditability, Assurance & Observability

a. Describe your platform’s capabilities related to telemetry, operational traceability, provenance, drift monitoring, runtime monitoring, explainability, and audit readiness.

b. Describe support for: immutable auditability, evidence collection, policy traceability, output traceability, log integrity validation, signed logs, or cryptographic assurance mechanisms.

c. Describe any support for policy-as-code, deterministic policy enforcement, or verifiable runtime controls.

d. Describe how governance decisions are captured as evidence.

e. Describe how approval lineage is maintained.

f. Describe how policy decisions can be reconstructed during audits.

g. Describe support for evidentiary chains linking users, prompts, models, outputs, approvals, and operational actions.

1. Human-in-the-Loop (HITL) & Autonomous Governance

a. Describe how your platform supports human oversight and configurable autonomy for AI systems and agents, including review thresholds, approval workflows, confidence-based escalation, intervention, override capabilities, and policy-driven controls that determine when human review is required.

1. Platform Architecture & Future-State Readiness

a. Describe the architectural origins of your platform, including whether AI governance capabilities were originally designed as AI-native functionality, or added onto existing governance, workflow, GRC, ITSM, security, privacy, or data management platforms.

b. Describe: core architectural principles, modularity, extensibility, API strategy, event-driven architecture support, cloud-native architecture support, and telemetry-driven capabilities.

c. Describe how your platform architecture supports evolving AI operational governance requirements over the next 3–5 years.

d. Describe any architectural limitations, dependencies, or legacy constraints that may impact future AI governance scalability or adaptability in the various environments leveraged by the state: on-prem, private cloud, public cloud, and SAAS

e. Provide a high-level architecture diagram illustrating governance workflows, runtime integrations, telemetry flows, policy enforcement points, operational monitoring integration, and external system integrations.

f. Describe where your solution can reside. (i.e. cloud tenant, on-prem, vendor hosted cloud)

g. What percentage of engineering resources are dedicated to AI governance capabilities?

h. Describe the evolution of the platform architecture over the past three years.

i. Describe how the platform supports emerging agentic AI architectures.

j. Describe the largest AI governance deployment currently operating on the platform.

1. Integration & Interoperability

a. Describe integration capabilities with: AWS Bedrock, Azure OpenAI, hybrid environments, SaaS AI systems, SIEM platforms, identity systems, data governance platforms, AI gateways, and observability tooling.

b. Describe your platform’s interoperability approach across multi-vendor AI governance ecosystems.

c. Describe supported APIs, SDKs, event streaming, webhook support, or extensibility frameworks.

1. Product Maturity & Production Readiness

a. For all major capabilities identified in this response, indicate whether the capability is Generally Available (GA) / Production, Limited Availability, Preview / Beta, Roadmap, Partner-Provided, Custom Implementation Required

b. Identify which capabilities are currently deployed in production customer environments.

c. Identify any capabilities materially dependent upon specific cloud providers, proprietary ecosystems, model providers, or partner technologies.

d. Describe your product release cadence and approach to supporting rapidly evolving AI technologies and emerging threats.

e. Describe your approach to customer commitments, accountability, or at-risk arrangements related to capabilities and customer outcomes represented during procurement processes.

f. Provide examples of contractual accountability, service commitments, milestone validation, or production acceptance mechanisms used with customers.

g. Identify capabilities not currently available to all production customers.

h. Identify capabilities requiring separate products or third-party services.

i. Describe willingness to contractually commit to represented capabilities.

j. Describe willingness to tie capabilities to milestone-based acceptance criteria.

1. Public Sector & Regulatory Alignment

a. Describe your experience supporting FedRAMP, CJIS, HIPAA, FERPA, IRS Pub 1075, or similar regulated environments.

b. Describe support and configurability for audit readiness, public records retention, and regulatory reporting requirements.

c. Provide relevant public sector customer references where permissible.

1. Future Roadmap & Strategic Direction

a. Describe your strategic roadmap for evolving AI governance capabilities over the next 3–5 years.

b. Describe anticipated investments related to runtime governance, AI operational assurance, autonomous governance, AI security, explainability, policy-as-code, deterministic controls, and operational observability.

c. Please provide any additional information, recommendations, or considerations that may benefit the State as it evaluates future AI governance strategies.

COST INFORMATIONAL FORM

1. Pricing model — describe your high-level licensing/pricing approach (e.g., per user, per AI system, tiered), and key cost drivers.

ADDITIONAL CONSIDERATIONS

1. Please provide input on alternative approaches or additional things to consider that might benefit the State.