1. AI-Specific Security Risks
AI features introduce risks that traditional security doesn't cover:
Prompt Injection
Users craft inputs that override your system prompt, making the AI do unintended things.
Data Leakage
AI accidentally reveals sensitive data from training or context. Users extract information they shouldn't have access to.
Harmful Output
AI generates offensive, dangerous, or legally problematic content.
Model Abuse
Users exploit your AI for their own purposes: free API access, content generation, etc.
2. Prompt Injection Protection
Prompt injection is the most common AI security issue. Mitigations:
- Input validation: Reject inputs containing obvious injection attempts (e.g., "ignore previous instructions").
- Delimiter separation: Use clear delimiters between system instructions and user input. Some providers support this natively.
- Output validation: Check AI responses for signs of compromised behavior before returning to users.
- Minimal permissions: Don't give AI access to capabilities it doesn't need. Limit tool access.
- Human-in-the-loop: For high-stakes actions, require human confirmation before execution.
Reality check
Prompt injection can't be fully prevented. Design your system assuming some attacks will succeed, and limit the damage they can cause.
3. Data Handling and Privacy
What you send to LLM providers matters:
- Check data processing terms: OpenAI and Anthropic have different policies on data retention and training. Know what you're agreeing to.
- Minimize data in prompts: Only include data that's necessary. Don't send full database records when you need one field.
- Anonymize when possible: Replace names, emails, and IDs with placeholders. Reconstruct after receiving response.
- Log carefully: Don't log sensitive data in prompts. Redact before logging if needed.
- Consider on-premise: For highly sensitive data, self-hosted models may be required.
4. Content Filtering
Filter both inputs and outputs:
- Input filtering: Block obviously harmful requests before they reach the model.
- Output filtering: Screen AI responses for harmful content before showing to users.
- Provider filters: Enable built-in content moderation from OpenAI/Anthropic. Not sufficient alone.
- Custom classifiers: For domain-specific concerns, train classifiers on your data.
- Human review queue: Flag edge cases for human review rather than auto-blocking.
5. Compliance (SOC 2, HIPAA)
AI features have compliance implications:
SOC 2
- • Document AI data flows in your architecture diagrams
- • Include LLM providers in your vendor assessment
- • Log AI interactions for auditability
- • Implement access controls on AI features
HIPAA
- • Never send PHI to standard LLM APIs
- • Require BAA from any AI provider handling PHI
- • Consider on-premise models for PHI processing
- • Document AI usage in your privacy practices
GDPR/CCPA
- • Disclose AI usage in privacy policy
- • Honor data deletion requests for AI-processed data
- • Ensure EU data stays in EU-compliant infrastructure
- • Obtain consent for AI processing where required
6. AI Security Checklist
Use this checklist before launching AI features:
- ☐Input validation for obvious injection attempts
- ☐Output filtering for harmful content
- ☐Rate limiting per user and globally
- ☐Logging with appropriate data redaction
- ☐Data minimization in prompts
- ☐LLM provider terms reviewed
- ☐Compliance requirements documented
- ☐Incident response plan for AI issues
- ☐User disclosure about AI usage
- ☐Human escalation path for edge cases