This checklist consolidates the secure AI development and testing practices discussed across this cluster into a single working reference, organized by lifecycle stage so a team can verify readiness systematically before an AI feature reaches production.
Requirements and design
Confirm security requirements name specific data reach, access authorization, action limits, content trust handling, output validation, and failure behavior, not general statements. Confirm design review has included the assume-compromise exercise at each point the model's decision leads to a real action. Confirm data minimization is justified with specific field or scope detail, not general assurance.
Implementation
Confirm authorization for every tool call is checked independently of the model's request, using the requesting identity's actual permissions. Confirm every tool has an explicit schema with strict type checking, bounded values, and rejection of unexpected fields. Confirm secrets are stored in a proper secrets management system and never embedded in prompt templates or documents feeding retrieval.
Testing
Confirm functional testing covers a range of phrasings, not only expected inputs. Confirm conventional security testing has not been reduced in scope for this being an AI feature. Confirm adversarial testing covers direct and indirect injection, jailbreak resistance where relevant, and multi-turn scenarios. Confirm downstream enforcement, not just model response text, was verified for every successful manipulation test case.
Failure and degraded behavior
Confirm every security-relevant dependency has explicit, tested deny-by-default behavior when unavailable. Confirm error handling does not leak internal detail to the model or end user, and does not reveal specific security thresholds. Confirm retry logic re-executes security checks rather than bypassing them.
Logging and monitoring readiness
Confirm every significant interaction produces a metadata-focused audit record including identity, policy decisions, tool calls, and outcomes. Confirm specific abuse and anomaly signals relevant to this feature are being tracked, not only theoretically loggable. Confirm the logging design was reviewed alongside functional design, not added as an afterthought.
Release readiness
Confirm security acceptance criteria are specific, testable, and integrated into the feature's actual definition of done. Confirm a staged rollout plan exists with explicit stage-advancement criteria and a tested rollback path. Confirm a fast, granular, pre-tested kill switch exists and the right people have pre-authorized access to trigger it.
Ongoing readiness beyond initial launch
Confirm CI/CD includes automated security testing triggered on relevant changes, not only manual testing before release. Confirm a process exists for retesting after model, prompt, or tool integration changes. Confirm this feature is included in the organization's AI inventory with an assigned risk classification and owner.
Scale checklist depth to the feature's risk classification
Following the risk classification guidance discussed in governance content, a low-risk internal tool can move through this checklist quickly, focusing on the items most relevant to its actual exposure. A high-risk, customer-facing feature with real action capability deserves a thorough pass through every item, since the consequence of a gap scales with the feature's actual worst-case impact.
Using this checklist as a gate, not a suggestion
The value of this checklist depends on it functioning as an actual release gate, integrated into the team's real release process, rather than a reference document consulted informally. Connecting each relevant item to a specific, verified acceptance criterion, discussed in acceptance criteria guidance, is what turns this checklist from a good intention into something that actually shapes what reaches production.
Common gaps this checklist catches early
Running this checklist before a release candidate is finalized, rather than only during a final sign-off meeting, tends to surface a small, recurring set of gaps: a tool that was scoped narrowly on paper but whose actual implementation still exposes a broader operation than intended, a fail-safe requirement that was written but never actually tested under a simulated dependency outage, and logging that captures activity but not the specific policy decision context an investigation would actually need. None of these gaps are exotic; they are the ordinary byproduct of building quickly, and catching them here is considerably cheaper than catching them after a real incident forces the question.
Where TELEON fits
Several items on this checklist, tool authorization, fail-safe behavior, audit logging, and kill switch implementation at the policy layer, map directly to what TELEON's runtime policy enforcement and audit trail provide when deployed through the gateway or supported middleware. The remaining items, requirements, design review, testing execution, and release process integration, are development work that a runtime tool supports with evidence but does not perform on its own.
The checklist in short form
- Specific requirements and design review including the assume-compromise
exercise.
- Independent authorization, strict schema validation, proper secrets
management.
- Functional, conventional security, and AI-specific adversarial testing.
- Deny-by-default failure behavior and safe error handling.
- Metadata-focused logging and relevant abuse monitoring signals.
- Specific, integrated acceptance criteria and a staged rollout plan.
- A fast, tested kill switch with pre-authorized access.
- Automated CI/CD security testing and inclusion in the AI inventory.
Working through these items systematically, scaled to each feature's actual risk, is what turns pre-production AI security review from a general aspiration into a concrete, verifiable gate that catches the specific gaps most likely to cause a real incident after launch.