Free Template

AI Agent Deployment Checklist:
40+ Items for a Safe Enterprise Rollout

AI agents can take autonomous actions across your systems — sending emails, calling APIs, reading databases, and executing code. Without a structured deployment checklist, you are flying blind. Use this checklist to ensure every AI agent your organisation deploys is secure, governed, and auditable before it touches production.

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40+
Checklist items
8
Deployment phases
Enterprise-grade
Coverage depth
Free
No sign-up required

Work through each section before go-live. Items marked with ✓ should be completed and evidenced — not just acknowledged.

01

Pre-deployment Security Assessment

Before any agent touches production data, your security team must sign off on the threat surface. This is the foundation everything else rests on.

✓
Threat Model Completed
Document attack vectors specific to the agent's role — data exfiltration, prompt injection, privilege escalation, and lateral movement.
✓
Data Classification Review
Identify every data source the agent will access and classify it: public, internal, confidential, or regulated (PII, PHI, PCI).
✓
Vendor Security Posture Verified
Confirm the underlying LLM provider holds SOC 2 Type II, ISO 27001, or equivalent. Review their subprocessor list and data retention policies.
✓
Authentication Model Defined
Decide how the agent authenticates to systems: service account, OAuth token, API key, or certificate. No shared credentials with human users.
✓
Network Exposure Scoped
Determine which network segments the agent can reach. Apply zero-trust micro-segmentation — the agent should only reach what it needs.
✓
Regulatory Compliance Review
Confirm the deployment complies with applicable regulations: APRA CPS 234, GDPR, HIPAA, EU AI Act, or NIST AI RMF as relevant.
02

Data Access & Permissions Scoping

AI agents are remarkably effective at accessing data they should not have. Lock permissions down before deployment, not after an incident.

✓
Least-Privilege Applied
Grant only the minimum permissions the agent needs to perform its defined tasks. Review and remove any inherited or default permissions.
✓
PII & Sensitive Data Boundaries Set
Explicitly block the agent from accessing fields containing PII, financial data, or health records unless strictly required by the use case.
✓
External API Access Whitelisted
Enumerate every external API the agent can call. Block all others at the network or policy layer. Review the list quarterly.
✓
Read vs Write Permissions Separated
Default to read-only for all data sources. Write permissions require explicit justification, a secondary approval, and audit logging.
✓
Data Retention Policy Defined
Specify how long agent session data, tool call logs, and outputs are retained, where they are stored, and how they are deleted.
03

API & Integration Security

Most AI agent breaches come through the integrations, not the model itself. Every API connection is a potential attack surface.

✓
API Keys in Secrets Manager
All credentials stored in a secrets manager (AWS Secrets Manager, HashiCorp Vault, Azure Key Vault). No hardcoded keys in code or config files.
✓
Rate Limiting Configured
Apply rate limits on all API calls made by the agent. Abnormal call volume is often the first signal of a compromised or runaway agent.
✓
Webhook Signatures Validated
If the agent receives webhooks, validate HMAC signatures on every inbound request. Reject unauthenticated payloads silently.
✓
Dependency Vulnerability Scan
Run SAST/SCA on agent code and its dependencies before deployment. Block deployment if critical CVEs are unpatched.
✓
Secrets Rotation Schedule
Set a maximum credential lifetime (90 days recommended). Automate rotation and test that the agent handles rotation gracefully without downtime.
04

Agent Behaviour Boundaries & Guardrails

Guardrails are what separate a useful AI agent from a liability. Define exactly what the agent can and cannot do — and enforce it technically, not just via policy.

✓
Output Filtering Implemented
Scan every agent output for PII, profanity, and off-topic content before it reaches users or downstream systems. Use a dedicated filtering layer, not just prompting.
✓
Prompt Injection Controls Active
Implement input sanitisation for all user-supplied data fed to the agent. Test with adversarial inputs before go-live. Never trust user input.
✓
Hallucination Mitigation Strategy
Define how the agent signals uncertainty. For high-stakes outputs (financial, legal, medical), require a human review step before action is taken.
✓
Action Scope Limits Enforced
Use tool-calling restrictions to prevent the agent from taking actions outside its defined scope. Prefer explicit allow-lists over block-lists.
✓
Human-in-the-Loop Triggers Defined
Specify conditions that pause the agent and escalate to a human: high-value transactions, ambiguous instructions, confidence below threshold, or novel situations.
✓
Refusal Logic Tested
Verify the agent correctly refuses out-of-scope requests, harmful prompts, and role-play attempts. Document tested edge cases and expected refusal responses.
05

Monitoring & Audit Logging

You cannot govern what you cannot see. Full audit trails are non-negotiable for AI agents — both for security and for regulatory compliance.

✓
Session Logging Enabled
Log every agent session: inputs, tool calls, outputs, latency, token counts, and user identifiers. Store logs in a tamper-evident system.
✓
Anomaly Detection Configured
Set up automated alerts for unusual behaviour: excessive API calls, access to unexpected data sources, output volume spikes, or error rate increases.
✓
Alert Thresholds Defined
Agree on alert thresholds with your security team before go-live. Avoid alert fatigue by calibrating sensitivity against baseline behaviour.
✓
Log Retention Policy Enforced
Retain agent logs for a minimum of 12 months (or as required by applicable regulations). Ensure logs are accessible for forensic investigation.
✓
SIEM Integration Tested
Forward agent logs to your SIEM. Confirm that critical events (auth failures, scope violations, data exfiltration signals) trigger real-time alerts.
06

Incident Response Planning

When (not if) an AI agent behaves unexpectedly, you need a rehearsed response. Improvising during an incident is expensive and often makes it worse.

✓
Kill Switch Tested
Confirm you can disable the agent in under 60 seconds. Test the kill switch monthly. The agent should fail closed — doing nothing — when disabled.
✓
Escalation Path Documented
Who is notified when an incident is detected? Define a chain: agent owner → security team → CISO → board (for material incidents). Publish the path.
✓
Containment Playbook Written
Document step-by-step containment actions for the most likely incident types: data leak, runaway actions, prompt injection, and service abuse.
✓
User Notification Process Defined
Know in advance how and when to notify affected users. Check regulatory notification timeframes (GDPR: 72 hours, Australian Privacy Act: as soon as practicable).
✓
Post-incident Review Scheduled
Commit to a blameless post-incident review within 5 business days of any significant event. Review findings feed back into this checklist.
07

User Communication & Training

The humans who interact with AI agents need to understand what they are interacting with, how to use it responsibly, and what to do if something goes wrong.

✓
Acceptable Use Guidelines Published
Publish clear guidelines on what users can and cannot ask the agent to do. Include examples of appropriate and inappropriate use cases.
✓
Privacy Notice Updated
Update your privacy notice to disclose AI agent usage, data processing, and any third-party model providers. Obtain consent where required.
✓
Feedback & Reporting Channel Live
Give users a simple way to report concerning agent behaviour. Review reports weekly. Close the loop with users who report valid issues.
✓
Awareness Training Delivered
Train all users who interact with the agent before go-live. Cover: what it can do, what it cannot do, how to spot errors, and how to escalate concerns.
08

Post-deployment Review Schedule

AI agents are not set-and-forget deployments. The model, the threat landscape, and your organisation's requirements all change. Build review cycles in from day one.

✓
30-Day Review Completed
At 30 days post-deployment, review logs, user feedback, incident reports, and performance metrics. Adjust guardrails and permissions based on real usage patterns.
✓
Quarterly Security Audit Scheduled
Schedule a quarterly security audit covering: permission review, dependency updates, threat model refresh, and penetration test of agent interfaces.
✓
Model Update Policy Defined
Define who approves model version updates, what testing is required before updates, and how you will validate behaviour hasn't changed unacceptably.
✓
Governance Board Sign-off Process
For high-risk agent deployments, require governance board review annually. Include AI risk register updates and attestation by the accountable executive.

Related Resources

Aona AI Agents →AI Agent Security →Platform Overview →All Templates →

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