A security-first checklist for enterprise AI deployments, from RAG risk controls to audit trails, redaction, and compliance readiness.
Enterprise AI adoption is speeding up, but security readiness is not. This guide focuses on the real controls teams are using to ship AI safely with RAG, agent tools, and compliance requirements.
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Retrieval means external data enters the model context. Secure it with allowlisted sources, content validation, and redaction for sensitive fields.
Agent tools should follow least-privilege rules. Separate read vs write access, require approval for high-risk actions, and log every tool invocation.
Treat inputs as untrusted. Use sanitization, instruction hierarchy, and tool-output validation to prevent malicious prompts from hijacking workflows.
Define which data is safe for model context. Mask or tokenize PII, PHI, and financial data before it ever touches the model.
For payments, deletions, or contract changes, require explicit approval steps. Automation should speed decisions, not bypass them.
Compliance teams need evidence. Capture prompts, tool calls, outputs, and final actions so you can prove accountability later.
Assume failures. Build rollback steps, rate limiting, and anomaly alerts so AI actions can be reversed quickly.
Enterprise AI security is not a single control. It is a system of guardrails, permissions, and continuous auditability.
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Quick answers to the most common questions.
Limit data access and log every action the system takes.
Yes. Logs are required for compliance and fast incident review.
Use strict instructions, safe tool access, and content filtering.
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