AI Security: Risks You Need to Know and How to Mitigate Them

The AI Security Problem Nobody's Talking About

Every company is rushing to adopt AI. ChatGPT, Copilot, custom LLMs - they're everywhere. But security teams are struggling to keep up, and the risks are real.

I've seen employees paste customer data into ChatGPT, companies deploy AI assistants without input validation, and "AI-powered" applications that trust everything the model outputs.

The Big Risks

1. Data Leakage

This is the most common and easiest to prevent, yet companies still mess it up.

What happens: Employee pastes confidential data into a public AI service. That data might be used for training, stored in logs, or accessed by the provider.

Real example: Samsung engineers pasted proprietary source code into ChatGPT. The company later banned AI tools entirely.

Prevention:

• Use enterprise versions with data privacy agreements
• Implement DLP policies that detect sensitive data going to AI services
• Train employees on what's acceptable to share

2. Prompt Injection

This is the SQL injection of the AI world, and most AI applications are vulnerable.

What happens: Attackers craft inputs that make the AI ignore its instructions and do something else.

Prevention:

• Never trust user input directly
• Separate system prompts from user input clearly
• Use output filtering to catch unexpected responses
• Implement rate limiting and monitoring

3. Indirect Prompt Injection

Even sneakier. Malicious instructions hidden in documents, emails, or web pages the AI processes.

Prevention:

• Sanitize all external content before AI processing
• Use separate AI instances for different trust levels
• Don't let AI directly execute actions based on external content

4. Insecure Output Handling

When AI outputs are used without validation, bad things happen. AI-generated code that gets executed, or content displayed without escaping - instant XSS vulnerability.

Building Secure AI Applications

Architecture Principles

Every AI application should have:

• Input filtering to block injection attempts
• Rate limiting to prevent abuse
• Output filtering to validate and sanitize
• Action gates requiring human approval for sensitive actions

Enterprise AI Governance

Create policies covering:

• Approved tools list
• Data handling rules
• Development standards
• Incident response procedures

Quick Wins for Today

1. Audit current AI usage - What tools are employees using? What data are they sharing?
2. Block unauthorized AI tools - Use your proxy/firewall to control access
3. Enable enterprise features - Switch from consumer to business AI tiers
4. Add basic monitoring - Log who's using what
5. Train your team - 30-minute session on AI security basics

AI tools are powerful. Used carelessly, they're powerful liabilities. Take security seriously from the start.