Security-firstComprehensivePractical

AI security best practices every team should follow

AI tools are powerful, but they introduce new security risks your existing controls were not designed for. This guide covers the best practices for securing AI usage across your team — from DLP scanning to audit trails to access control.

Security Practices

Six security practices for AI tool usage

Every feature designed to help your team work smarter with AI.

01

Data loss prevention

Scan every outbound prompt for sensitive data — PII, PHI, credentials, financial data, and proprietary information — before it reaches any AI model.

02

Comprehensive audit trails

Log every AI interaction with user attribution, timestamps, and tool details. Audit trails provide the visibility security teams need to detect and investigate incidents.

03

Access control

Implement role-based access so that only authorized users can manage guardrails, view audit logs, and publish prompts organization-wide.

04

Compliance policy packs

Deploy pre-built DLP rule sets for HIPAA, GDPR, PCI-DSS, CCPA, SOC 2, and general PII. Each pack is designed by compliance experts to cover framework-specific data patterns.

05

Credential scanning

Detect API keys, tokens, connection strings, and passwords before they are sent to AI tools. Developers commonly paste credentials when asking for debugging help.

06

Security awareness

Combine technical controls with team training. Help your team understand why certain data should never enter AI prompts and how to use sanitization tools effectively.

Benefits

Why AI security requires new approaches

Traditional perimeter security does not inspect what users type into web-based AI tools
AI tools process data externally, making every prompt a potential data exfiltration vector
Compliance frameworks increasingly require specific controls on AI tool usage
Without audit trails, security teams have zero visibility into what data reaches AI models
Credential leaks through AI tools can compromise infrastructure and production systems
Proactive DLP scanning is far less costly than responding to a data breach after it occurs

16

Smart detection patterns

19

Compliance frameworks

40+

Detection rules

FAQ

Frequently asked questions

What is the biggest AI security risk for teams?

Unintentional data exposure. Employees paste sensitive data into AI tools without realizing it leaves their organization's control. Automated DLP scanning catches these incidents before they become breaches.

How do we secure AI tools without blocking them?

Use guardrails instead of bans. TeamPrompt scans prompts in real-time and either warns users, auto-sanitizes sensitive data, or blocks high-risk content — all without preventing legitimate AI usage.

Do we need different security rules for different teams?

Often yes. Engineering teams may need credential scanning, while healthcare teams need PHI detection. TeamPrompt supports team-specific rule configurations alongside organization-wide policies.

How quickly can we implement AI security controls?

Basic DLP scanning and audit logging can be deployed in under an hour. Install the browser extension, enable the relevant compliance packs, and you have immediate protection.

How it works

Three steps from install to full AI security coverage.

1

Install

Add the browser extension to Chrome, Edge, or Firefox — or deploy it to your whole team via MDM. No proxy or VPN needed.

2

Configure

Enable the compliance packs for your industry, set DLP rules, and add your team's prompts to the shared library.

3

Protected

Every AI interaction is scanned in real time. Sensitive data is blocked before it leaves the browser. Your team has a full audit trail.

Ready to secure your team's AI usage?

Drop your email and we'll get you set up with TeamPrompt.

Free for up to 3 members. No credit card required.