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AI Governance for Mid-Size Companies

Helping leaders establish clarity, accountability, and guardrails as technology — and expectations — evolve.

Practical AI governance that enables adoption without losing accountability

InformaStorm helps mid-size organizations establish practical AI governance that enables adoption without losing accountability. Engagements are led directly by Solomon Smith and can align to the NIST AI Risk Management Framework (AI RMF), ISO/IEC 42001, existing cybersecurity and privacy programs, and the organization's own risk appetite. The work typically starts by answering four executive questions: What AI are we using? What data and business decisions are exposed? Who is accountable? What guardrails and review process let the organization move faster with confidence?

What an AI Governance Engagement Can Deliver

A practical AI governance program should create clarity, not bureaucracy. Depending on the organization's size, risk profile, and AI maturity, an engagement may include:

  • AI use-case and tool inventory
  • Ownership and decision-rights model
  • AI acceptable-use and data-handling guardrails
  • Risk classification and intake/review process
  • Third-party and vendor AI review
  • Alignment to NIST AI RMF and ISO/IEC 42001 where useful
  • Executive and Board reporting
  • Training and operating cadence so governance stays current
  • Integration with cybersecurity, privacy, data governance, vendor management, and enterprise risk

Who This Is For

InformaStorm's AI governance work is designed for organizations that are already adopting tools such as Microsoft Copilot, ChatGPT, Gemini, embedded AI features, analytics models, or agentic workflows but do not yet have a clear operating model for accountability.

Common triggers include:

  • Employees are using AI faster than policy and oversight can keep up
  • Leaders are unsure which data may be entered into AI tools
  • The Board is asking how AI risk is being governed
  • Customers, regulators, insurers, or auditors are asking about AI controls
  • Multiple teams are buying or building AI without a common review process
  • The organization wants to move quickly without creating unnecessary compliance friction

A Business-First Approach to AI Risk

InformaStorm treats AI governance as an operating discipline, not a policy-writing exercise. The objective is to help leadership decide which AI uses are worth pursuing, which risks need controls, who owns the decision, and how oversight fits into existing business processes.

That means governance can be proportionate. A low-risk productivity use case should not require the same review as an AI system that influences customer decisions, processes sensitive information, or materially affects operations.

Frameworks and Alignment

Where appropriate, InformaStorm can align AI governance work to recognized frameworks and standards, including:

  • NIST AI Risk Management Framework (AI RMF)
  • ISO/IEC 42001 AI management system principles
  • NIST Cybersecurity Framework
  • Existing privacy, security, enterprise-risk, and vendor-governance processes

Frameworks are used to structure decisions and evidence; they are not treated as a substitute for business judgment.

How an Engagement Works

A typical engagement can begin with a focused assessment or AI governance workshop, followed by a prioritized roadmap and an operating model the organization can maintain. A practical sequence may include:

  1. Inventory current and planned AI use.
  2. Identify data, decision, regulatory, security, and third-party exposure.
  3. Define ownership, escalation paths, and risk tiers.
  4. Establish policy, review, and exception processes.
  5. Integrate AI governance with existing technology, security, privacy, and vendor processes.
  6. Create executive and Board reporting.
  7. Train accountable teams and establish a repeatable operating cadence.

Led by Solomon Smith

AI governance engagements are led directly by Solomon Smith, Founder & CEO of InformaStorm. Solomon brings executive experience across cybersecurity, technology, enterprise risk, data, privacy, and governance, and works with leadership teams to translate technical AI risks into business decisions.

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Frequently Asked Questions

What is AI governance?
AI governance is the set of decision rights, policies, review processes, risk controls, and oversight mechanisms an organization uses to manage AI responsibly. The goal is to make clear what AI can be used for, what data can be exposed, who approves higher-risk use cases, and how decisions are monitored over time.

Does a mid-size company need an AI governance program?
If employees, vendors, or business systems are using AI in ways that affect company data, customers, operations, regulated processes, or material decisions, some level of governance is appropriate. The program does not need to be large, but ownership, acceptable use, data handling, risk review, and escalation should be clear.

What is the difference between NIST AI RMF and ISO/IEC 42001?
NIST AI RMF is a risk-management framework that helps organizations identify, assess, manage, and govern AI risk. ISO/IEC 42001 is an international management-system standard for establishing and maintaining an AI management system. They can complement each other; the right approach depends on the organization's goals, regulatory environment, customers, and maturity.

Who should own AI governance?
Ownership usually crosses technology, security, privacy, legal, risk, data, and business leadership. A single executive sponsor should be accountable, while the operating model defines who reviews specific use cases, exceptions, vendors, and data risks.

How do we govern Microsoft Copilot, ChatGPT, Gemini, and other generative AI tools?
Start by identifying approved tools and use cases, clarifying what data may be entered, defining prohibited or higher-risk uses, reviewing vendor and data-handling terms, and creating an escalation path for exceptions. The controls should be proportionate to the risk of the use case.

Can AI governance support innovation instead of slowing it down?
Yes. Good governance creates a faster path for low-risk use cases and focuses deeper review on higher-risk uses. Clear guardrails reduce uncertainty, help teams know what is allowed, and make adoption easier to scale.