Shadow AI in the Enterprise: What CISOs Can Measure in 30 Days

Most organizations already have “AI policy.”

It usually reads like this:Do not paste sensitive data into AI tools. Use approved tools only. Follow internal guidelines.

Then reality happens.

People still use ChatGPT in the browser. They still use AI inside Slack. They still use personal accounts. Teams still build quick internal copilots. Agents still cache context because it makes answers better. And none of it is visible enough to govern properly.

That is shadow AI.

The mistake is assuming you can solve shadow AI with a memo. You cannot. You solve it the same way you solved shadow IT: visibility first, then controls, then workflow adoption.

Here is a practical 30-day measurement plan that gives you signal without boiling the ocean.

What shadow AI really is (and why it is different from shadow IT)

Shadow AI is not just “unsanctioned tools.”

It is also sanctioned tools used unsafely.

The risk comes from three places:

1. Untracked egress: Employees share internal content with external model providers through browsers, plugins, and ad hoc workflows.

2. Unmanaged context: Modern AI use is not just a prompt. It often includes context that is pulled in automatically. Conversation history, documents, retrieved snippets, and “memory” become a new data surface.

3. No audit trail: When something goes wrong, you cannot answer: what left, who sent it, what context was attached, and what policy was applied.

That is the governance gap.

The 30-day goal: get real numbers, not vibes

In 30 days, you should be able to say:

• Where AI is being used in the organization

• Which teams and workflows are driving usage

• What categories of sensitive content are likely being exposed

• Whether AI use is trending up or down

• Whether you can produce an evidence-based audit record for AI interactions

If you cannot say those things, you are not governing. You are hoping.

Week 1: map AI usage surfaces

Start with a simple inventory. You are not trying to catch every single usage. You are trying to identify the major channels.

The most common AI usage surfaces

• Browser-based chat tools (personal or work accounts)

• Collaboration environments (Slack, Teams)

• Internal copilots (knowledge base bots, developer assistants, support drafting tools)

• Developer workflows (extensions, CLI tools, IDE assistants)

• Agents and automations (anything that runs on a schedule or reacts to triggers)

Output of Week 1: a one-page list of the top AI surfaces and who owns them.

Week 2: measure exposure risk with a lightweight classification

You do not need perfect data classification in 30 days. You need a risk map that can guide controls.

Create three buckets:

• High sensitivity: regulated data, client confidentiality, legal matters, deal room content, proprietary R&D, credentials

• Medium sensitivity: internal strategy, customer issues, pricing, internal process docs

• Low sensitivity: generic writing assistance, public info summarization, brainstorming

Then ask each team lead one question:

“What are the top three AI use cases in your team, and which bucket does the input data belong to?”

This is fast, and it produces a usable risk heatmap.

Output of Week 2: a table of teams, top use cases, and input sensitivity level.

Week 3: define the metrics that matter

Most AI governance efforts fail because they measure the wrong things. They measure “number of policy documents” or “number of approved tools.” That is not governance.

Governance is measurable.

Here are the metrics you can track in 30 days:

Adoption metrics

• Number of active AI users by team

• Number of AI interactions per week

• Top workflows using AI (drafting, summarization, coding, analysis)

Risk metrics

• Percentage of AI interactions involving medium or high sensitivity inputs (estimated at first)

• Number of workflows where client or project boundaries could be violated

• Number of tools where conversational history or agent memory is not governed

Control metrics

• Percentage of AI interactions that are policy-governed

• Percentage of AI interactions with an audit artifact (a receipt or log)

• Number of blocked or transformed interactions (if you have controls in place)

Incident readiness metric

• Can you reconstruct what was shared in a given AI interaction within 24 hours?

Output of Week 3: a simple dashboard, even if it is just a spreadsheet.

Week 4: run one controlled pilot workflow

Do not try to govern everything at once. Choose one workflow where the risk is real and the value is obvious.

Good pilot candidates:

• Legal client drafting

• Finance or private equity deal analysis

• Consulting work across multiple clients

• Biotech or R&D summarization and synthesis

• Internal support or incident response drafting

Define:

• Allowed AI usage surfaces

• Which sensitivity bucket is allowed

• Who can access the workflow context

• What audit record is required for each interaction

Then run it for two weeks, measure adoption, measure friction, measure auditability.

Output of Week 4: a real-world case study inside your organization.

What most teams learn after 30 days

If you do this honestly, you will learn three things:

1. Shadow AI is larger than expected.

2. The highest risk is not the prompt. It is the context that gets attached to it.

3. Bans do not work. People route around them.

The only scalable path is governed adoption: policy-controlled context, compartment boundaries, and auditability that holds up under scrutiny.

That is the gap Vivendur is built to close.

Next step

If you want, we can share a pilot structure designed around these metrics: visibility, compartment boundaries, policy enforcement, and audit receipts. Start small, prove governance, then scale.