How IT Should Provide Governance Around AI

AI is being adopted faster than any technology in enterprise history. Unlike past waves, it's entering organizations through every door at once. Every IT leader is now dealing with the same situation: Marketing turned on a copilot last quarter. Finance is piloting an agent that touches the GL. Three different teams bought three different vendors that all claim to do "agentic AI." Someone is asking who approved all of this, and nobody has a clean answer.

For IT, this creates a governance challenge that traditional software controls don’t solve. Without a deliberate governance model, organizations face uncontrolled costs, security exposure, compliance violations, and an inability to measure whether AI is really delivering value.

IT's role is to make AI safe to scale efficiently. That requires governance that starts with the following:

1. Start with an Operating Model

Define how governance will work before investing in a governance platform. A tool can only enforce decisions that have already been made. Establish who approves new agents, what data AI can access, who owns it after launch, etc. The operating model must come first. The tool comes second.

A sound AI operating model answers four questions:

1. Who decides? Define decision rights, RACI, and approval authority for new AI use cases, vendors, and agents.
2. What's allowed? This includes approved tools, integration patterns, permitted data classes, and prohibited use cases.
3. How is risk classified? Use a tiered framework (low/medium/high/critical) tied to data sensitivity, autonomy level, and business impact. Each tier triggers different controls.
4. How is value tracked? Understand cost attribution, chargeback, and ROI measurement so AI investment is defensible.

This operating model should explicitly separate execution (lifecycle management, vendor evaluation, financial modeling) from governance (decision rights, controls, compliance). Conflating the two is how governance becomes a bottleneck.

2. Build an AI Control Tower

Once the operating model exists, IT needs a centralized control plane, or AI control tower, that does four things:

1. Discovers and inventories every AI asset across the enterprise: agents, models, embedded vendor features, shadow AI. You cannot govern what you cannot see.
2. Enforces policy at runtime through guardrails, role-based access, human-in-the-loop checkpoints, and environment promotion controls.
3. Continuously monitors usage, cost, drift, incidents, and SLA compliance and alerts when thresholds are breached.
4. Reporting through dashboards that give executives, risk officers, and engineers a shared view of the AI portfolio.

Whether built on ServiceNow, a purpose-built AI governance platform, or stitched together from existing tools, the principle is the same: governance should be operational.

3. Govern the Full Lifecycle

AI assets need the same lifecycle discipline IT applies to applications, just adapted for the unique risks of probabilistic, autonomous systems. This lifecycle includes:

• Intake – every new AI use case enters through one front door with risk classification.
• Architectural review – alignment to reference patterns, approved integrations, and data boundaries.
• Approval – proportional to risk tier (low-risk moves fast, high-risk gets more analysis).
• Deployment – with guardrails, role-based access control, override mechanisms, and audit logging baked in.
• Monitoring – performance, cost, incidents, and compliance tracked in real time.
• Retirement – decommissioning when models drift, vendors change, or value disappears.

4. Govern Vendors as Aggressively as Internal Builds

Most enterprise AI risk today comes from AI features embedded in SaaS products, not custom-built agents. IT governance must extend to vendor evaluation scorecards, third-party risk reviews, OLA tracking, and roadmap management. If a vendor flips on an AI feature in their next release, IT should know before users do.

5. Make Governance a Partnership

The fastest way to kill an AI governance program is to make it feel like the old change advisory board. Teams that want to move fast will move around it. Instead, IT should make the approved path the fastest path with clear, tiered pathways so low-risk AI moves quickly and higher-risk AI receives necessary deliberation. Time-to-approval should be one of IT's KPIs, alongside coverage and incidents prevented.

Bottom Line

IT's job is to be the organization's AI trust layer, making it possible for the business to adopt AI quickly and safely because the right controls, visibility, and accountability are in place. Plus, good governance will actually make the organization more effective and secure in the long run. A clear path that works is the easier way to get things done.

At Trenegy, we help IT leaders build practical AI governance that protects the organization without slowing it down. To chat more about this, emailinfo@trenegy.com.