Generative AI Security Controls for Enterprise Deployment
Generative AI security is a systems problem. Protect the identities, data, retrieval layer, tools, outputs, infrastructure, and operating process around the model.
Control data and identity first
Classify which information may reach each model and retrieval index. Enforce access at the source and query layers so the model cannot retrieve content the user is not entitled to see. Use enterprise identity, least privilege, encryption, retention limits, secret management, and isolated environments for code or tool execution.
Treat retrieved documents, web content, emails, and tool results as untrusted input. Prompt injection can arrive through data the user never typed. Separate instructions from content and restrict what downstream actions untrusted context can influence.
Constrain tools and outputs
Authorize tool calls with technical policies, not natural-language instructions alone. Validate arguments, destinations, file paths, and transaction limits. Require confirmation for external communications, purchases, production changes, sensitive disclosures, or destructive actions. Sanitize generated code and content before execution or rendering.
Apply output handling based on use: citations for grounded answers, structured validation for machine-consumed data, content safety where relevant, and human review for consequential decisions. Preserve a safe fallback when models or dependencies fail.
Test continuously and prepare to respond
Build evaluations for leakage, unauthorized retrieval, jailbreaks, indirect prompt injection, insecure tool use, hallucinated actions, denial-of-wallet loops, and model or prompt changes. Red-team realistic workflows, then monitor production signals such as unusual tool sequences, data volumes, destinations, costs, and policy blocks.
Incident plans should cover model providers and connected systems. Teams need to revoke credentials, disable tools, isolate sessions, preserve traces, notify owners, assess affected data, and restore a known-safe configuration.
Leadership checklist
- Enforce user entitlements before retrieval and tool execution.
- Treat external and retrieved content as untrusted.
- Validate tool arguments and require approval for high-impact actions.
- Continuously evaluate, monitor, and rehearse AI incident response.