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. Our [AI attack surfaces guide](/blog/ai-security-attack-surfaces-network-prompt-data-model) breaks down exactly where these payloads enter your stack and how to map each entry point.

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

If you're building directly on OpenAI or Claude APIs, our [practical guide to securing OpenAI and Claude API deployments](/blog/securing-openai-claude-api-practical-guide) covers concrete configuration steps for each of these layers.

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? Our [shadow AI detection and governance guide](/blog/shadow-ai-enterprise-detection-governance-2026) covers how to find AI tools employees are using without IT approval.
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.

Frequently Asked Questions

What is prompt injection and why is it the most critical AI security risk?

Prompt injection is an attack where a malicious user crafts input that overrides an AI application's system instructions, causing the model to ignore its rules and perform unintended actions. It is considered the most critical AI-specific risk because it exploits the fundamental architecture of LLMs: they cannot reliably distinguish between trusted instructions and untrusted user input. Every AI application that accepts user input and acts on the model's output is potentially vulnerable.

How can organizations prevent employee data leakage into public AI services?

The three most effective controls are: switching to enterprise AI tiers with data processing agreements that prohibit training on your data, deploying Data Loss Prevention (DLP) policies on proxies and endpoints to detect sensitive data being sent to AI services, and running mandatory training so employees understand what types of data are prohibited. Blocking unauthorized AI services at the network level is a useful backstop but not sufficient on its own, as employees will find workarounds.

What is the difference between direct and indirect prompt injection?

Direct prompt injection happens when a user types malicious instructions into a chat interface or form that feeds an AI application. Indirect prompt injection occurs when malicious instructions are embedded in content the AI processes from external sources, such as a document the AI summarizes, a webpage it browses, or an email it reads. Indirect injection is harder to defend against because the attack arrives through what appears to be normal content rather than explicit user input.

What are the key principles for building secure AI applications?

Every secure AI application should implement four layers: input filtering to catch injection patterns before they reach the model, rate limiting to prevent abuse and cost attacks, output filtering to sanitize responses before displaying them to users (preventing XSS and data leakage), and action gates that require human approval before the AI takes any sensitive action like sending emails or modifying records. No single layer is sufficient; defense in depth is required.

How should organizations govern AI tool adoption to reduce security risk?

Effective AI governance requires four elements: an approved tools list specifying which AI services are sanctioned for what data classifications, clear data handling rules defining what categories of data may never be entered into AI systems, developer standards for building AI features securely including input validation and output handling requirements, and an incident response procedure specifically for AI-related incidents such as prompt injection exploits or data leakage through AI services.