Securing AI features starts with two realities: user text is hostile input, and model output is untrusted. PII rules and prompt injection basics belong in the architecture, not in a late checklist.
1. PII handling
- Decide what may be sent to model providers
- Scrub or tokenize sensitive fields when possible
- Minimize retention of prompts and completions
- Encrypt secrets and rotate API keys
2. Prompt injection basics
Attackers will try "ignore previous instructions." Separate system instructions from user content. Do not concatenate untrusted text into tool allow-lists. Prefer structured outputs and server-side authorization over trusting the model to obey.
3. Tools are the real risk
Read tools need tenant checks. Write tools need authz plus often human approval. Never let the model invent SQL or shell commands that execute without a strict allow-list.
4. Logging and abuse
Log enough to debug, not enough to create a second breach. Rate limit. Moderate inputs when needed. Block repeated injection patterns.
Summary
AI security is application security with a probabilistic component. Treat prompts as input, tools as privileged APIs, and PII as a product policy.
Related: AI Integration Services, AI Agent Development, Hire AI Developer.
Prakash Tank
Full-Stack Architect & Tech Enthusiast. Passionate about building scalable applications and sharing knowledge with the community.
