AI Governance Consulting Services

AI governance consulting from PS3G gives you the policy, inventory, risk assessments and controls to use AI with confidence, whether you are a founder adding an assistant to a first product, an operations lead whose staff already paste customer data into public AI tools, or a compliance team inside a larger organization that needs documented answers for auditors. We build the program at the size you need, align it to the NIST AI Risk Management Framework and ISO/IEC 42001, and keep it running after the first six weeks. The outcome: you know what AI you use, who is accountable for it, what could go wrong, and what you have done about it.

AI governance sized to how much AI you actually use

Most organizations use AI in three ways at once, and each needs a different amount of governance. Staff use general-purpose assistants and the AI features inside the office suite. The software you already buy, from CRM and HR systems to clinical platforms, ships with AI features switched on. And some organizations build their own: a support assistant on top of company documents, a scoring model, an agent that files tickets.

Our AI governance services match that reality. A ten-person firm whose only AI is a writing assistant needs a one-page policy, a short inventory and a vendor check, not a committee. A company with an AI feature in its product needs impact assessments, testing evidence, monitoring and a documented lifecycle. A division of a larger group usually needs an enterprise AI governance program that plugs into existing risk, privacy and security functions. The controls come from the same frameworks; the scope is set by how much AI you run and how much harm a mistake could cause.

Why now

Employees already use AI tools

In almost every assessment we run, people are already using AI at work, with or without permission. The productivity is real, so a ban does not hold. What holds is a short list of approved tools, clear rules about which data never goes into a prompt, and a place to ask for new tools. Without those, client records, source code and personal data leak into services nobody reviewed. Our guide to shadow AI covers how to find that use and keep what is useful.

Customers and regulators are asking

Security questionnaires now carry an AI section. Cyber-insurance applications ask whether you have an AI policy. Procurement teams ask how vendor AI handles their data. SOC 2 auditors ask how AI tools fit into change and vendor management. A written AI governance program, with evidence behind it, turns those questions from a scramble into a lookup.

State laws such as Colorado’s are arriving

Colorado’s AI law sets duties for developers and deployers of high-risk AI systems used in consequential decisions such as employment, lending, housing, insurance, healthcare and education, including risk management programs, impact assessments and notices. Other states and cities regulate automated employment decision tools, insurance underwriting and chatbot disclosure, and the EU AI Act reaches some US companies serving EU customers. Check the current official text for effective dates and amendments. This is general information, not legal advice. We are not a law firm; we work with your counsel and build the operational side: the inventory, assessments, notices and records counsel will need.

Vendors embed AI

Your CRM summarizes calls, your HR platform screens resumes, your help desk drafts replies, and each feature arrived in a release note rather than a decision, often under terms written before AI existed. AI governance brings those features back into view so you can decide which to keep, configure or turn off.

What we deliver

Every engagement produces working documents and controls, not a slide deck. Depending on scope, that includes:

• AI policy and acceptable-use rules. A short policy your staff will read: approved tools, prohibited data, review rules, disclosure, vendor use and consequences. We start from our AI acceptable use policy template and tailor it to your industry and tools.
• AI inventory and risk tiering. A register of every AI system in use, whether bought, built or embedded, with an owner, a purpose, the data it touches and a risk tier. The tier decides how much of the rest applies.
• Risk and impact assessments. An AI risk assessment for each system that matters, and an AI impact assessment for systems that affect people’s access to jobs, credit, care, housing or services. Both follow a repeatable template.
• Controls for data, privacy and security. Data classification tied to AI use, access rules, retention, logging, prompt and output handling, and the application-security controls from the OWASP Top 10 for LLM applications where you build your own.
• Vendor AI due diligence. A questionnaire and review process for AI vendors and for AI features inside existing vendors: training on your data, retention, subprocessors, security attestations and a business associate agreement where protected health information is involved.
• Monitoring and incident process. What you watch (usage, quality, complaints, drift), who reviews it and how often, and what happens when an AI system produces a harmful or wrong result, inside your existing incident process.
• Training. A short session for all staff on the policy and a deeper session for the people who own AI systems, approve tools or review outputs.
• Documentation for audits and questionnaires. A governance pack for customers, auditors, insurers or counsel: policy, inventory, assessments, control mapping and meeting records.

Frameworks we align to

We do not invent a framework. We map your program to the published ones customers and auditors recognize, so your evidence is reusable.

Framework	What it is	How we use it
NIST AI RMF 1.0	A voluntary US framework with four functions (Govern, Map, Measure, Manage), seven trustworthiness characteristics, a Playbook of suggested actions and a Generative AI Profile.	The backbone of our risk assessment and governance structure. Every control we recommend traces to a Govern, Map, Measure or Manage outcome.
ISO/IEC 42001	The certifiable AI management system standard: clauses 4 to 10 for the management system and Annex A controls for AI-specific risk, lifecycle, data and third parties.	Our target structure for organizations that want certification or already hold ISO 27001. ISO 42001 consulting is a defined track within this service.
OWASP Top 10 for LLM applications	The application-security risk list for systems built on large language models: prompt injection, sensitive information disclosure, supply chain, data and model poisoning, improper output handling, excessive agency and more.	Our technical checklist for AI you build. Our testing team turns it into security test cases.
HIPAA and SOC 2 overlaps	HIPAA requires a risk analysis that must now cover AI tools touching protected health information. SOC 2 asks how AI fits into vendor management, change management and logical access.	We reuse evidence across programs so you maintain one control set. See IT compliance consulting.

Our 6-week starter program and what comes after

The starter program is the fastest way to go from no program to a defensible one. It runs in about six weeks for a typical organization; the calendar stretches when there are many AI systems and shrinks for a small team with two tools.

What comes after depends on what you run. Most clients keep a quarterly cycle: new use cases enter through an intake form, assessments are refreshed when a system changes, the inventory is reconciled against actual spend, and the policy is updated once a year. Organizations building AI products add testing and release gates; those under customer or regulatory pressure move toward ISO 42001 readiness. We can run that cycle under a managed-services agreement or hand it to your team.

Ask for a fixed-scope quote for the six-week starter program.

Governance for AI you build vs AI you buy

The two paths share a policy and an inventory and then diverge. For AI you buy, including AI features inside tools you already own, governance is mostly contractual and configurational: what the vendor may do with your data, how long it is retained, which subprocessors see it, whether the feature can be disabled per user or data type, and how staff are trained to review its output. Our vendor due diligence process handles that and ties into the vendor-management controls you may already keep for SOC 2.

For AI you build, governance runs through the development lifecycle: data governance for training and retrieval data, documented model and prompt choices, testing for accuracy, bias and prompt injection, human oversight designed into the workflow, logging of inputs and outputs, change control for model and prompt updates, and monitoring for drift once live. When our AI development and AI agents and chatbots teams build a system, those controls are part of the delivery rather than a review at the end. Our AI consulting work starts the same way, with governance inside the readiness assessment.

AI governance for healthcare and other regulated SMBs

AI governance in healthcare has one extra rule: if an AI tool can see protected health information, it is inside your HIPAA risk analysis, and the vendor needs a business associate agreement. Ambient scribes, patient-facing chatbots and summarization features all qualify. Clinical uses also need a clinician in the loop, a clear statement of what the tool does not do, and a way to report wrong outputs. We build those controls alongside our healthcare software development and HIPAA compliance consulting work; our guide to AI governance in healthcare goes deeper.

Other regulated organizations face parallel questions. Lenders and insurers inherit model risk management expectations and fairness obligations. Law and accounting firms must keep privileged client material out of AI tools that retain prompts. Defense suppliers need AI use to fit their CMMC boundary. In each case we start from the regulation you already live with and add the AI-specific pieces, with information security controls where they are missing.

Engagement models

• Fixed-scope project. The six-week starter program, an AI governance assessment of an existing program, an ISO 42001 gap analysis or a set of impact assessments, each with a defined deliverable and price.
• Dedicated team or staff augmentation. An AI governance consultant, analyst or engineer inside your team on a monthly basis, scaled up or down as the workload changes.
• Managed services. We run the governance cycle under defined SLAs: intake, assessments, vendor reviews, monitoring reviews and the annual policy refresh.
• Advisory. A fractional AI governance lead for leadership meetings, board questions, customer questionnaires and counsel coordination.

How we start: a first conversation, a short assessment, then a written proposal with scope and price. No minimum budget; scope is set by the work.

Why PS3G

We govern AI because we ship it. PS3G builds AI features into its own software, including our HIPAA-compliant EHR and telehealth platform, DocVilla, used by medical practices across multiple states. Those features go through the same inventory, impact assessment, vendor review, testing and monitoring we set up for clients, inside a HIPAA boundary that regulators can audit. That is the difference between a consultant who has read the frameworks and one who has had to run them.

The rest of the firm backs that up: more than ten years in business, a US headquarters in Wilmington, Delaware with an India delivery center, a full lifecycle under one roof (consult, build, test, run, govern), Microsoft and open-source certified engineers, and a testing center of excellence that turns governance requirements into test cases. See all our services.

Related services

• Data governance: the quality, classification and lineage work AI depends on.
• AI consulting: readiness, use-case selection and roadmap with governance built in.
• AI development: custom models, RAG assistants and integrations delivered with the controls above.
• AI agents and chatbots: builds with guardrails, logging and human oversight.
• IT compliance consulting: SOC 2, HIPAA and CMMC programs that share evidence with your AI program.
• Information security: the security controls AI governance relies on.

Guides on this topic

• What Is AI Governance? A Practical Guide for Any Size of Organization
• AI Governance Framework: How to Build One (Structure + Examples)
• AI Acceptable Use Policy Template for Employees (Free, Editable)
• NIST AI RMF Explained: Govern, Map, Measure, Manage
• ISO 42001 Explained: The AI Management System Standard
• AI Risk Assessment: How to Run One (Template and Examples)
• AI Governance Maturity Model and Checklist: Where Are You Today?
• AI Governance Tools and Platforms: Do You Need One?
• AI Governance vs Data Governance: Differences and Overlaps
• Data Governance for AI: The Prerequisite Nobody Wants to Fund
• AI Governance in Healthcare: HIPAA, Clinical Risk, Controls
• Shadow AI: How to Find It, Govern It and Keep the Productivity
• Colorado AI Act and US State AI Laws: What Businesses Must Actually Do
• Generative AI Governance: Policies, Controls and Guardrails

Frequently asked questions

What does an AI governance consultant actually deliver?

A working program, not a report: an AI policy and acceptable-use rules, an AI inventory with risk tiers, risk and impact assessments for the systems that matter, assigned controls for data, privacy and security, a vendor due diligence process, monitoring and incident handling, staff training, and a documentation pack you can show to customers, auditors and counsel, plus the review cadence that keeps it current.

How much AI governance does a small business need?

Less than you fear, but more than nothing. AI governance for small business usually means a one-page policy, an inventory of the handful of tools in use, a vendor check on each, a rule about which data never goes into a prompt, and a named owner. That takes days, not months. The program grows only when you build AI into a product or use it for decisions about people.

Do we need ISO 42001 certification?

Only if a customer, regulator or market requires it, or if you sell AI products to organizations that will ask. Most organizations are better served by aligning to NIST AI RMF and keeping ISO 42001 as a later option. If you already hold ISO 27001, adding 42001 is a smaller step because the management system clauses share one structure. We run a gap analysis before anyone commits to an audit.

How does AI governance relate to HIPAA and SOC 2?

It extends them. HIPAA requires a risk analysis, and any AI tool that touches protected health information belongs in it, with a business associate agreement from the vendor. SOC 2 auditors look at vendor management, change management and access, and AI tools fall under all three. We map AI controls onto the control set you already maintain so one piece of evidence serves every program.

Can you help with the Colorado AI Act and other state laws?

Yes, on the operational side. We identify which of your systems could count as high-risk, run the impact assessments and risk management program the laws describe, draft notices and build the records. Legal interpretation, including whether a law applies to you and when, stays with your counsel, and we work alongside them. Check the current official text for effective dates. This is general information, not legal advice.

What is the difference between an AI risk assessment and an AI impact assessment?

A risk assessment looks at what could go wrong with a system for the organization: data exposure, wrong outputs, security, vendor failure, cost. An impact assessment looks at the effect on the people the system touches: fairness, access to services, transparency, the ability to contest a decision. High-risk systems need both. Our risk assessment guide explains the templates and when each applies.