Inventory Every AI Extension
Before scaling any AI extension to production, a security checklist must start with full visibility. Every browser add-on, IDE plugin, or agentic tool in use needs to be logged with its owner, purpose, and exact permissions. Many of these extensions request far more access than needed, from reading entire repositories to running terminal commands. For autonomous agents, the real danger isn't just data exposure, but the ability to take actions without human approval. Without strict permission reviews, even a well-meaning pilot can open the door to serious data leaks.
Also worth reading: How Can an AI Smart Contract Audit Checklist Strengthen Blockchain Security? · What Should an Agent Governance Implementation Checklist Include for Enterprise AI Tutorials? · What Makes an AI Tutorial Verification Checklist Trustworthy?
Beyond access, the checklist must focus on data flow and long-term control. Teams need to know where prompts, code, or credentials are sent, how long they're stored, and if third parties can reuse them. Vendor trust, update history, and a clear way to revoke access should all be verified. Most of all, any tool used outside of official approval must be treated as shadow AI. With just a small group often driving the majority of usage, these unchecked extensions pose an outsized risk that no growing AI rollout can afford to ignore.
Review Permissions And Data Flows
An AI extension security checklist must begin by strictly mapping permissions and data flows. Every extension, especially agentic ones used in developer tools, should only be granted the bare minimum access needed to do its job. This includes file system rights, shell commands, and the ability to create or push pull requests. Just as important is knowing which third-party model provider each request goes to, as sending proprietary code or API keys to an unapproved endpoint can create serious compliance risks. Without a clear view of these flows, even a small pilot can leak sensitive data long before it reaches production.
Beyond access control, the checklist must focus on visibility and governance. With shadow AI on the rise, teams need to know exactly who installed each extension, when it was approved, and how often it is used. Update reviews are often ignored, yet a trusted extension can become dangerous after a single release. Audit logs for prompts, tool calls, and outbound requests should be mandatory, along with a fast way to revoke access. Most of all, AI extensions must be treated as active agents, not passive plugins. Holding them to this level of scrutiny is the only way to scale safely without inviting avoidable breaches.
Test Agent Actions Safely
An AI extension security checklist should begin by mapping every permission, data flow, and external call the extension can make, then require least-privilege access, explicit user consent for sensitive actions, and clear boundaries around code execution, file access, and network requests. It should also verify dependency integrity, secret handling, update channels, and audit logging so reviewers can reconstruct what the extension did, when it did it, and why each action was authorized.
Beyond technical controls, the checklist should cover governance and operational readiness: threat modeling for prompt injection, unsafe tool use, and data leakage; sandboxing or isolation for agent actions; rollback and kill-switch procedures; and monitoring for anomalous behavior after deployment. It should define who approves changes, how incidents are reported, and how quickly risky capabilities can be disabled without breaking legitimate workflows, while also documenting user-facing disclosures and support expectations.
Monitor Shadow AI Usage
An AI extension security checklist should begin with identity, permissions, and data boundaries. It should verify who can install or approve the extension, what scopes it requests, whether it can read source code, prompts, secrets, or customer data, and how tokens are stored. It should also require a clear data flow map, retention rules, and vendor review so teams know where prompts and outputs travel. Security teams should test for prompt injection, unsafe tool calls, and overbroad API access before rollout, while documenting rollback steps and owner accountability.
The checklist should also cover runtime monitoring and lifecycle governance. Teams need logging for extension actions, alerts for unusual data exfiltration, and periodic access reviews as features change. It should define approved use cases, prohibited data types, and escalation paths for incidents. Finally, it should require updates for dependency risks, model changes, and new permissions, plus a retirement plan when an extension no longer meets policy. Together, these controls help organizations reduce shadow AI risk while keeping developer productivity and innovation within safe, auditable boundaries.
Document Incident Response Steps
An AI extension security checklist should begin with permissions and data boundaries, because extensions often run inside editors, browsers, or IDEs where they can read files, prompts, tokens, and repository contents. It should require least privilege, clear disclosure of what data leaves the device, encryption in transit and at rest, and explicit user consent before sending code or documents to a model provider. It should also verify that secrets are not logged, cached, or embedded in prompts, and that sensitive paths can be excluded from indexing or context retrieval.
Beyond data controls, the checklist should cover model and supply chain integrity, including approved providers, version pinning, dependency scanning, signed releases, and update channels that cannot be silently hijacked. Runtime safeguards matter too: sandboxing, rate limits, tool allowlists, human approval for destructive actions, and audit logs that capture prompts, outputs, and tool calls without exposing private content. Finally, it should define incident response steps, rollback procedures, and ownership so teams can revoke access, disable the extension, investigate misuse, and restore trust quickly.
AI Extension Risk Comparison
| Checklist Item | Potential Risk | Mitigation Action |
|---|---|---|
| Least Privilege Permissions | Excessive access to files, terminals, or Git repos | Enforce strict scopes and revoke unused permissions on every update |
| Secrets & Credential Handling | API keys, tokens, or PATs leaked through prompts or logs | Use a secure vault, block hardcoded secrets, and run automated secret scans |
| Third-Party Model & Data Flow | Proprietary code sent to unapproved LLM endpoints | Restrict to vetted models, use allowlists, and inspect outbound payloads |
| Supply Chain & Extension Updates | Malicious VSIX packages or compromised dependencies | Verify publishers, pin versions, run SCA checks, and block auto-updates from unknown sources |