Enterprise AI Security Pre-Flight Checklist
Key takeaway
An enterprise AI security pre-flight checklist is a go-live gate for connecting large language models and AI agents to organizational data. It verifies controls across eight areas: identity and access, data classification, agent tooling, prompt injection and retrieval safety, auditability, observability, incident response, and trust gradients that grant AI agents autonomy gradually.
Security pre-flight checklist
If you're connecting LLMs to real organizational data, this is the "don't embarrass yourself in production" list.
1) Identity and Access
- SSO + MFA enforced for all users (no exceptions, no "temporary" accounts)
- Separate identities for humans, services, and agents (no shared "data_bot")
- Least privilege by default: start read-only, scoped datasets, scoped tools
- RBAC/ABAC defined (roles/attributes mapped to reality, not fantasy)
- Row/column-level security for sensitive domains (HR, finance, customer PII)
- Time-bound access for elevated permissions (auto-expire; approvals logged)
- Secrets management + rotation (no long-lived keys; no creds in prompts/logs)
2) Data Classification and Handling
- Classification tags applied (PII/PHI/PCI/Confidential/Public)
- Approved-use policies per classification (e.g., "PII can't leave VPC")
- Masking/tokenization defined and enforced
- Retention rules for data and AI artifacts (prompts, transcripts, embeddings)
3) Agent Tooling Controls (the "sharp objects" section)
- Tool allowlist (explicit connectors/actions; everything else blocked)
- Read vs write separation (writes require stronger auth + gating)
- Action approvals for high-risk actions (exports, perms, deletions, sends)
- Sandboxed execution (no unrestricted network, no arbitrary file writes)
- Query validation to prevent exfil patterns; enforce limits and safe joins
- Egress controls (network policies, domain allowlists, "no internet" mode)
4) Prompt Injection and Retrieval Safety
- Treat retrieved content as untrusted input
- Grounding required: answers cite catalog/contracts/lineage where possible
- Permission-filtered retrieval (per-user isolation, per-dataset constraints)
- Output filtering: redact sensitive values; block disallowed classes
- No training on customer data unless explicitly governed/contracted
5) Auditability and Evidence
- End-to-end audit logs: who asked, what was retrieved/queried/output
- Immutable log storage with compliance-aligned retention
- Lineage + run logs connected to AI actions ("this answer came from...")
- Change control for prompts/tools/policies (versioning + approvals + rollbacks)
6) Observability and Detection
- Data observability: freshness, volume, distribution shifts, schema changes
- Security observability: query anomalies, export spikes, new principals, drift
- Cost observability: token usage + query spend per team/agent
- Alerts routed to owners (not
#general) with playbooks attached
7) Incident Response (because it's not "if")
- Playbooks for: leaks, injection, tool misuse, credential exposure
- Kill switch: disable an agent/connector instantly
- Key rotation runbook tested (not theoretical)
- Postmortems include blast radius + datasets + outputs + remediations
8) Trust Gradients (adoption hinge)
- Graduated autonomy levels (Manual -> Suggested -> Assisted -> Autonomous)
- Show sources + planned actions + impact before execution
- Risk scoring for actions (higher risk -> more friction/approval)
- Easy rollback for automated changes
Frequently asked questions
How do you secure an LLM connected to enterprise data?
Secure an LLM connected to enterprise data by enforcing SSO and MFA, giving agents their own identities with least-privilege, read-only access by default, and filtering retrieval by user permissions. Add classification tags, masking, tool allowlists, egress controls, and end-to-end audit logs so every query and output can be traced.
How do you protect AI agents against prompt injection?
Protect AI agents against prompt injection by treating all retrieved content as untrusted input. Require answers to be grounded in cited sources such as the catalog, contracts, or lineage, filter retrieval by user and dataset permissions, and filter outputs to redact sensitive values. Sandboxing and tool allowlists limit damage if injection succeeds.
What controls should AI agents have before they can take actions?
AI agents should have an explicit tool allowlist, separation of read and write permissions with stronger authentication for writes, and approval gates for high-risk actions such as exports, permission changes, deletions, and sends. Run agents in sandboxed execution with egress controls, and provide a kill switch to disable any agent instantly.
What are trust gradients for AI agents?
Trust gradients are graduated autonomy levels for AI agents, moving from manual to suggested to assisted to autonomous. Agents show sources, planned actions, and impact before execution, higher-risk actions carry more friction or approval, and automated changes are easy to roll back. This builds the user trust that adoption depends on.
