Move fast on AI without creating an audit problem.
We build the guardrails that let mid-market companies adopt AI aggressively and defensibly: usage policy, identity and access controls, data protection, and governance that stands up to auditors, insurers, and boards.
In one sentence: AI governance here means a written, enforced answer to four questions: which AI tools are approved, who can use them with what data, how access is controlled through identity, and who is accountable when something goes wrong.
Book a call about ai governance, securityThe problem: shadow AI is already inside
Your employees are already using AI — the only question is whether anyone governs it. Client data pasted into consumer chatbots, contracts summarized by free tools, credentials shared across accounts: in most mid-market companies this is happening today, unmonitored, because there's no approved alternative and no policy.
Regulators, cyber insurers, and enterprise customers are starting to ask pointed questions about AI usage. 'We don't really know' is becoming an expensive answer — in premiums, in lost deals, and in liability.
The approach: policy enforced through identity, not memos
AI usage policy that people can follow
A short, enforceable policy: approved tools, prohibited data flows, review paths for new tools. Written for how your teams actually work, not copied from a template.
Identity and access controls
The policy is enforced where it can't be ignored — SSO, conditional access, data loss prevention, and role-based permissions on AI tools. This is IAM discipline applied to AI, built on decades of doing it at enterprise scale.
Data protection and classification
Which data may touch which AI systems, enforced technically. Client-confidential, regulated (PHI, financial), and public data get different rules and different plumbing.
Audit-ready governance
Ownership, review cadence, model and vendor inventory, and documentation formatted so that when an insurer, auditor, or enterprise customer asks, the answer is a folder — not a scramble.
Governance & security capabilities
Eight capabilities, from discovery through standing governance — scoped to what your risk profile actually requires.
Governance Frameworks & Steering Committees
Ownership, review cadence, and decision paths for AI — including standing up and facilitating an executive steering committee — so governance keeps running after we leave.
AI Usage & Responsible AI Policies
A short, enforceable policy: approved tools, prohibited data flows, review paths for new tools, and standards for acceptable use, human review, and transparency.
Identity & Access Management
Policy enforced through SSO, conditional access, DLP, and role-based permissions — where it can't be ignored. Decades of enterprise IAM discipline, applied to AI.
Data Privacy & Classification
Which data may touch which AI systems — client-confidential, regulated, and public each get their own rules and plumbing.
Shadow AI Discovery
Finding the AI already in use — tools, extensions, and data flows nobody sanctioned — before an auditor or insurer does.
AI Security Assessments
A working assessment of your AI attack surface: data exposure, prompt-injection paths, and access gaps.
Vendor Risk Reviews
Security and data-handling reviews of AI vendors before they touch your environment — and again at renewal.
Compliance Readiness
Documentation formatted for insurers, auditors, and enterprise customers, so the answer to a questionnaire is a folder, not a scramble.
How the engagement runs
Governance work typically runs six to ten weeks, with your IT team or MSP doing hands-on enforcement.
- Weeks 1–2
Exposure review
Discover AI in use, data flows, and gaps in current identity controls.
- Weeks 3–5
Policy & controls
Author usage policy and design identity and data-protection enforcement.
- Weeks 6–8
Enforcement
Stand up controls, integrated with your existing security stack.
- Weeks 8–10
Governance
Ownership, review cadence, and audit documentation in place.
What you walk away with
Every engagement ends in artifacts you own — not a verbal debrief.
- AI exposure review — what tools and data flows are actually in use
- Enforceable AI usage policy tailored to how your teams work
- Identity and access control design (SSO, conditional access, DLP, RBAC)
- Data classification scheme mapping data types to AI systems
- Audit-ready governance pack: model/vendor inventory, review cadence, ownership
- Response kit for insurer and customer security questionnaires
The outcome, measured
AI adoption that accelerates instead of stalling: employees get sanctioned, capable tools; leadership gets visibility and a defensible posture; and security reviews stop being the place where AI initiatives go to die.
- Employees get sanctioned, capable tools instead of ungoverned shadow AI
- A defensible posture for auditors, insurers, and enterprise customers
- Security reviews stop being where AI initiatives go to die
- Documented ownership and cadence — answers live in a folder, not a scramble
Questions buyers actually ask
Won't governance slow our AI adoption down?
Done right, it speeds adoption up. The alternative to governance isn't freedom — it's shadow usage, followed by an incident, followed by a blanket ban. Sanctioned tools with clear rules get used more, not less.
We have an MSP handling security. Why isn't that enough?
MSPs run controls; they rarely set AI policy, classify data for AI use, or answer to your board for AI risk. We design the governance layer and then work with your MSP to enforce it — the two roles are complementary, not competing.
Do we need this if we're not in a regulated industry?
If you handle client data, sign enterprise contracts, or carry cyber insurance, you have AI governance obligations in practice even without a regulator. Security questionnaires and insurance renewals now routinely ask about AI usage.
Related services
AI Strategy & Executive Advisory
Executive AI strategy, readiness assessment, investment prioritization, and a fractional Chief AI Advisor — the decisions that come before the tools.
Fractional CIO & Technology Leadership
Enterprise-grade technology leadership, sized for the mid-market: vendor management, IT spend, board reporting, and roadmap ownership.
Talk to an operator, not a salesperson.
Engagements typically begin with a 30-minute call and, where it fits, an AI readiness assessment. No retainer required — the first conversation is free.