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Guide• March 28, 2026• 10 min read

The Enterprise AI Security Checklist

Before deploying AI to your organization, here are the security controls you need in place — and the questions your security team should be asking.

Security Team
work.studio

Your CEO wants AI deployed to every employee by Q3. Your developers are already using Copilot. Marketing is experimenting with ChatGPT. The pressure is on.

But rushing AI deployment without proper security controls is how data breaches happen. Here's the checklist your security team needs before saying "yes" to enterprise AI.

What's at Stake

AI security incidents are different from traditional breaches:

  • •Data leaks are invisible — You may never know an employee shared trade secrets with ChatGPT
  • •AI remembers (sometimes) — Data may be used to train future models
  • •Compliance is unclear — Regulators are still figuring out AI rules
  • •Shadow AI is rampant — Employees use AI whether you sanction it or not

1. Access Control

Who can use AI, and what can they do with it?

SSO Integration
AI access should use your existing identity provider. No separate logins.
Role-Based Permissions
Different teams need different AI capabilities. Developers vs. marketing vs. legal.
Automatic Deprovisioning
When someone leaves, their AI access should terminate immediately via SCIM.

Question for vendors: "How does your AI platform integrate with our IdP? Do you support SCIM for automated provisioning?"

2. Data Protection

What data can flow to AI systems, and how is it protected?

PII Detection
Automatically identify and handle personal data before it reaches AI models.
Data Classification
Know what's confidential, internal, or public — and enforce different rules for each.
Data Residency
Where is data processed? EU customers may require EU-only processing.
No Training on Your Data
Ensure the AI vendor won't use your data to improve their models.

Question for vendors: "Where is our data processed? Is it used for model training? Can we get a DPA that explicitly prohibits training?"

3. Audit & Visibility

Can you see what's happening and investigate when needed?

Complete Audit Logs
Every AI interaction logged with user, timestamp, input, and output.
SIEM Integration
AI logs should flow to your existing security monitoring tools.
Usage Dashboards
Who's using AI? How much? For what? Real-time visibility is essential.
Incident Investigation
Can you search logs to investigate "what did this user ask about Project X?"

Question for vendors: "What's in your audit logs? Can we export them? How long are they retained? Can we send them to Splunk/Datadog?"

4. Cost Control

AI can get expensive fast. How do you prevent runaway costs?

Per-User Budgets
Set spending limits per user, team, or department.
Usage Attribution
Know exactly who's driving costs for chargeback and budgeting.
Rate Limiting
Prevent abuse and runaway automation with sensible limits.

5. Content Safety

AI can generate problematic content. How do you handle it?

Input Filtering
Block or flag problematic requests before they reach the AI.
Output Moderation
Review AI outputs for harmful, biased, or inappropriate content.
Prompt Injection Protection
Defend against adversarial prompts designed to bypass controls.

The Complete Checklist

Before approving any enterprise AI deployment, verify:

SSO integration
Role-based access control
SCIM provisioning
PII detection
Data residency controls
No-training guarantee
Complete audit logs
SIEM integration
Usage dashboards
Per-user budgets
Content moderation
Prompt injection protection

Need Help Evaluating AI Security?

Our team can walk you through how work.studio addresses each item on this checklist and help you build a secure AI deployment strategy.

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