What Is Shadow AI ?

Shadow AI is already happening in most companies. This blog explains what it is, why it matters for finance, and a practical way to make AI safe and useful without slowing the business.

What we mean by Shadow AI

Shadow AI is when employees use public AI tools such as ChatGPT, Claude, or Gemini for work outside your approved systems and policies. In many cases, people paste company information into tools you do not control. There is no audit trail or safeguard.

Shadow AI happens because the tools are easy to use and genuinely helpful. The risk is that sensitive data such as client details, internal financials, contract,s or payroll information can leave your governed environment.

Why finance leaders should care

• Data exposure - Confidential or regulated data may be shared with tools that store or learn from it.
• Compliance gaps - No logging, unclear retention, and no clear accountability.
• Rework - AI can draft helpful content, but without review, it can add errors or confusion.

What Shadow AI looks like in finance

• Drafting vendor or credit control emails
• Summarising month-end notes and variance commentary
• Cleaning CSV files and creating first draft reports
• Writing quick formulas or code snippets for analysis
• Drafting board or investor summaries

The value is real, but the risks are real as well if the use is unstructured.

Do not block it. Channel it.

You do not need a blanket ban. You need clear and simple guardrails and better options so people can use AI safely.

A practical playbook

1. Assess what is happening
   Run a short staff survey and review basic traffic or logs. Identify the top tasks where AI is already used.

2. Label your data in a simple way
   Use four classes. Public, Internal, Confidential, Restricted.
   Examples. Marketing copy is Internal. Internal financials are Confidential. Customer information and payroll are restricted.

3. Set an allowed use guide
   Green- Brainstorming and public copy may use public or approved tools with no sensitive data.
   Amber. Internal drafts and analysis must use approved enterprise AI with sign-on, logging, and review.
   Red- Restricted data must not go to public AI. Use a private and governed route.

4. Offer better paths rather than only blocking
   Approve an enterprise AI option with sign-on, audit logs, and retention controls. Provide a simple redaction template so people can remove names and identifiers before they prompt.

5. Keep people in the loop
   Humans remain accountable. Require quick review and attribution that records who checked the output and when.

6. Start small and measure
   Pilot one process with one owner and two metrics. Measure cycle time and the percentage of rework. Keep what works and remove what does not.

A simple 30, 60, 90-day plan

Days 0 to 30
Survey and quick log review. Publish a one-page policy and an allowed use guide. Turn on an approved AI option with sign-on and logging. Share a prompt library with redaction examples.

Days 31 to 60
Pilot two workflows, such as vendor emails and month end commentary. Track cycle time and rework. Add basic data loss prevention rules for Restricted data.

Days 61 to 90
Expand to adjacent teams. Create a simple intake for new use cases. Start a monthly review of usage, incidents and value stories.

The takeaway

Shadow AI is a question of governance and behaviour, not a yes or no technology decision. Pair clear rules with better tools and you will reduce risk and free time for higher value advisory work.