AI integration risks: what to control before going to production

AI demos are low-friction: call an API, show a result. Production AI systems require the same discipline as any system that handles sensitive data or affects business outcomes.

Data leakage is the top concern. Sending PII or confidential data to a third-party LLM API can violate policy and regulation. Mitigations include using APIs that do not train on your data, filtering or redacting inputs, or running models in your own VPC. Define a clear data boundary and enforce it in code and architecture.

Prompt injection and misuse are real. Validate and constrain user input; don’t pass raw user text directly into prompts that control actions. Use structured outputs and guardrails where the model’s response triggers workflows or access. Log prompts and responses for debugging and audit.

Cost can spiral if usage is ungoverned. Set quotas, budgets, and alerts per environment or team. Prefer fixed-scope or capacity-based patterns where possible so new features don’t silently multiply API cost.

Compliance (GDPR, HIPAA, SOC2) applies to AI like any other processing. Document where data flows, how long it’s retained, and who can access it. If you use a vendor, ensure BAA or DPA and that your usage fits their compliance scope.

We help teams design production AI with these controls in place: secure integration patterns, governance, and monitoring so AI delivers value without introducing undue risk.