AI Security Checklist for Product Development

Security review should not begin after an AI pilot has already been sold internally. By then, the sponsor is defending a solution while security and IT are still trying to define the system.

What should an AI security checklist for product development include? It should define the use case and data boundary, map the complete data flow, test identity and authorization, examine model-specific risks, verify evidence provenance and auditability, assess vendor operations, and define integrations, export, and exit.

This is especially important in product development, where the data may include unreleased specifications, customer requirements, test results, research, intellectual property, quality records, cost assumptions, supplier information, and expert know-how.

The fastest security review is not the one with the fewest questions. It is the one that defines the system early enough for the answers to shape the pilot.

1. What is the approved use case and data boundary?

Before reviewing the vendor, document:

• The task the system will perform
• The products, programs, functions, and lifecycle stages in scope
• The named users and administrators
• The person accountable for consequential outputs
• The data classifications permitted and prohibited
• The internal and external source classes the workflow may use
• The outputs that can be exported, shared, or written elsewhere

“AI for engineering” is difficult to approve. “Compare incoming customer specifications with the approved product record for one line, used by six named engineers, with no production write-back” is reviewable.

2. Can the vendor show the complete data flow?

Ask where data enters, where it is stored, which services process it, where model inference occurs, which logs are created, and how information leaves the system.

Require current vendor documentation for:

• Hosting locations and tenant architecture
• Encryption in transit and at rest
• Backups, resilience, recovery, and deletion behavior
• Subprocessors and model providers
• Retention for source files, prompts, outputs, logs, and backups
• Whether customer data is used for model training or product improvement
• Cross-border transfers and contractual mechanisms
• Export paths and downstream copies

These details can change and belong in security, privacy, and contractual materials—not in assumptions inferred from marketing copy.

3. Are permissions enforced in retrieval and output?

Review:

• SSO and identity-provider support
• Role design and least-privilege administration
• Organization-, hub-, member-, and feature-level permissions
• Joiner, mover, and leaver processes
• Guest and external-collaborator controls
• Service accounts, MCP clients, APIs, and connector authentication
• Permission behavior in chat, search, generation, cross-hub queries, agents, and exports

Then test with two users who have different access. The critical question is not whether a restricted file is hidden in the interface. It is whether its contents can influence an answer, comparison, generated document, agent output, or external-client response for an unauthorized user.

4. Which generative-AI controls apply?

Traditional SaaS controls do not answer every AI question. Ask:

• Which models and providers may process customer data?
• Can model selection change, and how is that governed?
• What prompts, outputs, or customer data may providers retain?
• How are malicious documents, unsafe external content, and prompt injection addressed?
• Can the system distinguish internal evidence, approved external sources, and general model knowledge?
• How does it behave when evidence is missing, contradictory, or outdated?
• How are model, retrieval, agent, and workflow changes tested?
• Which tasks require human review or approval?

NIST’s AI Risk Management Framework and Generative AI Profile provide a useful structure: Govern, Map, Measure, and Manage risk in the actual context of use. Explore the NIST AI RMF.

5. Are sources, versions, approvals, and changes auditable?

For product-development work, security, quality, and technical governance overlap. Determine whether the system preserves:

• The sources behind an answer, analysis, or generated document
• Source dates, versions, instructions, and approval state
• Document versions and change history
• Reviewer identity, approval, rejection, and override
• The workflow stage and decision context
• Who uploaded, queried, generated, edited, exported, or shared content
• Enough logging to investigate an incident without exposing unnecessary content

A citation is not a complete audit trail. A sound review connects the evidence used, output produced, people involved, and decision that followed.

6. What operational assurance should the vendor provide?

Request current evidence for the controls that matter to the organization, including:

• Independent certifications or assurance reports
• Penetration testing and vulnerability management
• Secure development and change-management practices
• Incident response and notification commitments
• Business continuity and disaster recovery
• Employee and privileged-access controls
• Privacy documentation, DPA terms, and subprocessor governance
• Roadmap items the proposed use case depends on

Narratize-specific certification, hosting, encryption, retention, model-training, and subprocessor claims should be taken from the current security packet and contract during review. This article does not substitute for those materials.

7. How should enterprise connectors be assessed?

For every connection, document:

• The authoritative source system
• Whether the pattern is upload, point-in-time copy, live retrieval, synchronization, or write-back
• Credential type and permission scope
• Data classes and objects accessible
• Refresh, failure, retry, revocation, and ownership behavior
• Logs and alerts generated by the integration
• What happens to imported data after the connection is removed

Narratize supports point-in-time source selection from OneDrive, SharePoint, and Google Drive; ingestion from Jira, Confluence, and Aha!; direct uploads and URLs; and authenticated MCP access to hub retrieval and selected agents. These are live patterns, but point-in-time cloud files do not refresh automatically when the source changes.

The Integration Layer is expanding through Power Automate and deeper connector orchestration. Direct PLM, ERP, LIMS, and other specialized connections, two-way synchronization, and no-code integration building are broader roadmap layers. Security review should assess the connection actually proposed and the delivery state on which the contract depends.

8. How should upcoming alerts and portfolio analytics be governed?

Expiration and approval alerts support current governance. Narratize is expanding high-priority workflow, stage-gate, compliance, integration-failure, and live regulatory alerts, along with in-app and email delivery options.

Portfolio Intelligence is in build to provide cross-hub health, stage progression, evaluation summaries, Knowledge Readiness scoring, reliability signatures, and natural-language portfolio questions. As those views come online, security teams should test whether every aggregate, chart, export, and drill-down respects the underlying hub permissions.

9. Is the exit plan defined?

• How can the organization export source content, generated work, metadata, versions, and audit history?
• What is deleted at termination, on what schedule, and how is deletion confirmed?
• Which integrations, tokens, service accounts, and downstream copies must be revoked?
• Can the business continue operating if the service is unavailable?
• Which records must remain in a controlled system of record?

Exit is part of architecture, not an end-of-contract administrative detail.

Which Narratize product controls are live?

Narratize supports organization and hub administration, granular member permissions, and SSO including Okta and Google. Product Knowledge Hubs separate product- and program-specific contexts. Per-hub settings can exclude sensitive hubs from cross-hub queries.

Drafts remain visible to authorized hub members but are not retrieved by AI until saved as knowledge. Source citations, document instructions, version history, lineage, configured approvals, and workflow override records support traceability. The platform’s named Red Team Agent and other purpose-built evaluations remain grounded in permitted hub knowledge and their approved external-source behavior.

The decision-grade AI procurement checklist provides six benchmark tests for source support, missing evidence, conflicts, superseded documents, permissions, and change.

What belongs in a minimum viable pilot approval packet?

1. A one-page use-case and data-boundary statement
2. A vendor data-flow and subprocessor diagram
3. An access-control matrix for pilot users
4. A model, retention, and training-terms summary
5. A benchmark covering missing evidence, source traceability, and permission boundaries
6. A connector inventory and authoritative-system map
7. An incident, export, and exit plan
8. A capability-state list separating live, contracted implementation, in-build, and roadmap dependencies

That packet gives security, IT, legal, quality, product, and the business a shared definition of what has actually been approved.

Bring the security questionnaire and proposed pilot boundary. Narratize can map current controls, required evidence, open questions, connector architecture, and delivery dependencies before implementation begins. Request a security and architecture review.