AI Governance and Policy Services for Government and Enterprise

We design and put into operation the framework an organization uses to approve, inventory, assess, and monitor its AI systems. The policies, intake process, risk register, monitoring, and training are built inside the tools you already run, and your staff are trained to operate them.

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Most Organizations Adopt AI Before They Write the Rules for It

Agencies and regulated companies already have AI in production systems, in pilots, and in everyday use by individual staff. Few have a single record of those systems, one way to approve a new one, a method for rating risk, or evidence that each system still performs as intended.

Oversight bodies, auditors, and boards now ask for all four. A policy document on its own does not satisfy them, so Sprinklenet builds the framework as a working program: each policy is tied to a control that runs in a tool your staff already use, and each control leaves a record an auditor can inspect.

What the Practice Delivers

Seven components, delivered together as a complete framework or separately where part of the work is already done.

Responsible AI FrameworkPrinciples, decision rights, and review stages for the full life of an AI system, from proposal through retirement, written for your mission and legal authorities.
AI Policy DevelopmentPolicies and standards for staff use of generative AI, data handling, transparency notices, human review, third-party AI services, and AI output used in decisions or as evidence.
Use Case Intake and AI InventoryA standard intake form and approval path for every proposed use, feeding one inventory that records the owner, purpose, data classification, and status of each system.
Risk Assessment and Risk RegisterA tiering method that sets review depth by impact, and a register that links each risk to a system, a control, an owner, and a review date.
Performance and Drift MonitoringMeasures and thresholds for accuracy, fairness, and model drift, with scheduled checks that open a ticket when a threshold is crossed.
Workforce Training and Train-the-TrainerRole-based training for executives, product owners, developers, and end users, and a train-the-trainer program that prepares your own staff to deliver it.
Governance Operating ModelThe council charter, roles, meeting cadence, release checks, and annual recertification cycle that keep the framework current after the engagement ends.

One Governance Cycle for Every AI Use Case

Each proposed use of AI moves through the same five stages. Monitoring results revise policy, and the use cases a change affects are reviewed again.

IntakeUse case, data sources, owner, and intended users recorded.
InventoryEntered in the AI use case inventory with its status.
Risk AssessmentImpact level set. Required controls identified.
Controls and MonitoringControls applied. Audit records and evaluation runs reviewed.
Accuracy, fairness, and drift measuresRelease checks and recertification
Policy UpdateFindings revise policy and guidance.

Affected use cases return to intake

How an Engagement Runs

The work moves through four stages, and each stage ends with something your organization can use.

Review What ExistsWe read your current AI policies, decisions, and system lists, interview the people who own and use AI, and record each required element as complete, partial, or missing.
Design the FrameworkWe define decision rights, risk tiers, the system record, and measures of success with your leadership, and publish them as a blueprint for approval.
Build the ControlsWe write each control, configure it in your tools with your administrators, and prove it on one or two real systems before extending it to the rest.
Train and TransferWe train your staff by role, prepare your own trainers, and hand over a framework your team runs without us.

Built Inside the Tools You Already Own

The framework does not require a new platform. Intake forms, the inventory, the risk register, release checks, and dashboards are configured in your existing data catalog, ticketing system, code repositories, analytics environment, and collaboration suite. The design is vendor-neutral, so it continues to work when you change models, vendors, or monitoring tools.

Alignment With Federal Standards and Guidance

Each framework is mapped to the NIST AI Risk Management Framework (AI RMF 1.0) and, for generative AI, the NIST Generative AI Profile (NIST AI 600-1). For federal agencies, the mapping extends to OMB AI guidance, including Memorandum M-25-21 on agency use of AI and Memorandum M-25-22 on AI acquisition, and to GAO's AI Accountability Framework.

The mapping is delivered with the framework. An auditor can trace each requirement to the control that satisfies it and to the evidence that the control ran. When guidance changes, the mapping shows which controls need to be revised.

Referenced Frameworks

NIST AI RMF 1.0: AI Risk Management Framework
NIST AI 600-1: Generative AI Profile
OMB M-25-21: Agency use of AI
OMB M-25-22: AI acquisition
GAO AI Accountability Framework: Oversight practices for federal AI

What Your Organization Receives

A framework document and policy set, published in your format.
An AI inventory covering known systems, each with an owner and a risk tier.
A risk register and assessment procedure, with completed assessments for the highest-impact systems.
An intake form and approval workflow in production use.
Monitoring dashboards and drift alerts for systems in production.
Training materials, role playbooks, and staff prepared to deliver the training.
An evidence package for each system that records approvals, tests, and sign-offs.

The framework, policies, configurations, and training materials produced for your program belong to you.

Who the Practice Serves

Federal AgenciesChief AI Officers, CIO offices, and oversight and audit organizations that need a complete inventory, risk tiers for high-impact AI, and documented practices consistent with OMB AI guidance.
Regulated EnterprisesRisk, compliance, and legal leaders in financial services, healthcare, and other regulated sectors who need oversight of AI across the business that a board and regulators will accept.
Prime Contractors and Program TeamsTeams that need a responsible AI lead and governance specialists on a federal program, under a subcontract or teaming arrangement.

Knowledge Spaces as an Optional Platform

The practice does not depend on any Sprinklenet product. Where an organization wants a governed environment for AI assistants and knowledge retrieval, Knowledge Spaces provides one as a hosted service or under a commercial license, with role-based access, configurable guardrails, scored evaluation runs, and an audit log. Knowledge Spaces is Sprinklenet's proprietary platform and is licensed separately from the consulting deliverables. Sprinklenet operates it every day for its own clients, and that experience informs the controls we design for yours.

Evaluation on Synthetic Data

Many AI evaluations stall while approval to share real data is negotiated. Sprinklenet builds synthetic datasets that mirror the structure, volume, and difficulty of an organization's records, using fictional parties and a recorded correct answer for every test question. The data is loaded into an isolated Knowledge Spaces environment, where assistants, models, and retrieval settings are tested under the same access rules, guardrails, and audit logging that apply in production.

Known AnswersEach synthetic collection is generated with a ground-truth record, so accuracy, citation, and refusal behavior are scored against facts.
Realistic VolumeCollections run to thousands of documents, which shows the retrieval, response time, and cost behavior that a small sample hides.
Model ComparisonThe same test set runs against several models, so the choice of model rests on measured results.

No sensitive record leaves the organization's control during the evaluation, and the test sets remain in place for regression testing after launch.

See the Platform

Senior Practitioners Who Operate AI Governance Daily

Controls Tested in OperationSprinklenet builds and runs a governed AI platform in production. The access rules, guardrails, evaluation runs, and audit records we design for clients are ones we operate ourselves.
Senior-Led DeliveryA senior practitioner leads every engagement and works directly with your leadership, counsel, and technical staff.
Available on GSA MASFederal buyers can order through Sprinklenet's GSA Multiple Award Schedule for management consulting and IT professional services, or through a prime contractor's vehicle.

Sprinklenet contributes to federal AI standards. Read our public comment to NIST on AI agent identity and authorization. Terms used on this page are defined in the AI Governance Glossary.

AI Governance Questions

What is a responsible AI framework?

A responsible AI framework is the set of policies, roles, and controls an organization uses to decide which AI systems it will run and to keep them safe, fair, and effective while they are in use. A complete framework covers ethics and integrity standards, use case intake, an inventory of AI systems, risk assessment, performance and drift monitoring, staff training, and a governance body with defined decision rights.

How does Sprinklenet align AI governance with the NIST AI RMF and OMB guidance?

Sprinklenet maps each control to the NIST AI Risk Management Framework (AI RMF 1.0) and, for generative AI, the NIST Generative AI Profile (NIST AI 600-1). For federal agencies the mapping also covers OMB AI guidance, including Memoranda M-25-21 and M-25-22, and GAO's AI Accountability Framework. The mapping is delivered with the framework, so an auditor can trace each requirement to a control and its evidence.

Do we need to buy a new platform to govern AI?

No. Sprinklenet builds the framework inside the tools an organization already owns, such as its data catalog, ticketing system, code repositories, and collaboration suite. Knowledge Spaces, Sprinklenet's governed AI platform, is available as a separate hosted service for organizations that want one, and the framework does not depend on it.

Can an AI system be tested before it has access to real data?

Yes. Sprinklenet generates synthetic datasets that match the structure and volume of an organization's records and loads them into an isolated Knowledge Spaces environment. Assistants and models are tested there against recorded correct answers, under the same access rules and audit logging used in production, so results are available before any sensitive data is shared.

What are an AI inventory and an AI risk register?

An AI inventory is a maintained record of every AI system an organization uses or is developing, with its owner, purpose, data, and status. A risk register lists the risks attached to those systems, each with a rating, a control, an owner, and a review date. Sprinklenet builds both, completes the first assessments with your staff, and sets the recertification cycle that keeps them current.

How are AI systems monitored for performance and drift?

Each system has measures and thresholds set by its risk tier, including accuracy, fairness across groups, and drift in inputs and outputs. Scheduled checks run in the organization's own analytics environment, and a breach opens a ticket with a named owner. For generative AI, monitoring adds tests for grounded answers, citation accuracy, and prompt injection, along with periodic human review of samples.

How long does a governance engagement take, and can our staff run the framework afterward?

The length depends on how much is already in place. A governance review comes first and produces a gap assessment and a sequenced plan. A full framework is delivered in phases, with the inventory and intake process in use early in the program. Training and a train-the-trainer program are part of the work, so your staff deliver the training and run the framework after handover.

Start With a Governance Review

For organizations that need to know where they stand, we review existing AI policies, the system inventory, and oversight practices against the NIST AI Risk Management Framework and applicable guidance, then deliver a gap assessment and a sequenced plan.

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