Managing AI risk without stifling innovation

Why Shadow AI Security Fails

Using the NIST 2.0 standard, we evaluate security across six outcomes:

• Lack of Visibility: AI features are often embedded silently within existing platforms or browser extensions, making them easy to adopt but hard for IT to track.
• No Meaningful Control: Even if a tool is known, it often sits outside your managed identity (SSO) systems, meaning usage cannot be audited or standardised.

The Five step shadow AI audit

This audit should be treated as routine maintenance, not a crackdown. The goal is clarity and risk reduction without disrupting the team.

Step 1: Discover Usage (Without Disruption)

Basic MFA is no longer the finish line. Modern phishing can bypass SMS codes and simple prompts.

• Identity Logs: Check which users are signing into AI platforms using personal vs managed accounts.
• SaaS Settings: Audit existing platforms (e.g., CRM or HR tools) to see which AI features have been enabled by default.
• Positive Inquiry: Ask the team: "Which AI tools are helping you save time right now?" Approach this as "help us support this safely."

Step 2: Map the Workflows

Don't just list tools; understand how they touch real work.

• Identify: The workflow, the input data (e.g., client names), and the output use.

Step 3: Classify the Data

Use simple buckets your team can actually follow:

• Public | Internal | Confidential | Regulated

Step 4: Triage Risk Quickly

Focus on high-risk areas first. Score them based on:

• The sensitivity of the data involved.
• Whether the tool uses personal logins.
• Whether the AI provider uses your data to train their models (opt-out vs opt-in).

Step 5: Decide on Outcomes

Make decisions that are easy to follow:

• Approved: Permitted with managed identity and logging.
• Restricted: Allowed for low-risk tasks only; no sensitive data.
• Replaced: Transition the user to an approved corporate alternative (e.g., Microsoft Copilot).
• Blocked: Poses unacceptable risk to data privacy.

Conclusion

Shadow AI security isn't about blocking innovation; it's about ensuring data doesn't flow into tools you can't govern. By making this a quarterly discipline, you turn a potential blind spot into a repeatable, secure process. Ready to gain visibility over your AI landscape? Contact us for a structured Shadow AI audit to protect your business data today. Contact us today.