Review the data-security approach
See how BaristaLabs scopes source data, least-privilege access, vendor/model assumptions, retention, and rollback before sensitive workflow work moves toward production.
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AI workflow security review worksheet
Use this worksheet to map what an AI workflow may read, which vendors or models see the data, what it may draft or change, who approves risky steps, what gets logged, and how your team can roll back a mistake.
Best fit for support triage, appointment intake, CRM notes, document extraction, website updates, finance/admin review, and other workflows where AI gets close to customer records or business systems.
Review packet preview
Reviewer sees
Source evidence, proposed action, policy rule, risk reason, before/after preview, and approve or escalate decision.
An AI workflow security review should start with one named workflow, not a general argument about whether AI is safe.
The useful question is smaller: for this workflow, what data boundary is needed, what data is excluded, which systems are touched, which actions are allowed, which actions require approval in a review queue, what evidence appears in the queue, and what record after the run will exist?
Write those answers down before choosing broader permissions. The worksheet can become the implementation brief for the builder, the review packet for security or compliance, and the operating reference for the workflow owner. It also connects the data-security questions to the broader responsible AI operating model the team uses for approvals, receipts, rollback, and expansion.
Copy-paste artifact
Copy this into a working document. Fill it out for one workflow before the workflow receives production credentials, broad source access, or permission to write into a system of record.
Need approval rules too?
The security worksheet maps what the workflow touches. The approval policy decides what the workflow may do with that access, when a person must approve, and what stays manual.
Use the approval policy worksheetThis example stays approval-gated. The workflow reads a new support ticket, checks limited customer context, drafts a response, suggests a category, and prepares a CRM note. A person reviews before anything is sent or written to the system of record.
Once the boundary, vendor exposure, approval triggers, receipt fields, and rollback path are ready for stakeholders, carry them into the AI workflow launch review packet to make the production-permission decision.
Before production access
If the workflow can send, update, publish, or touch sensitive records, the approval queue should show the evidence a reviewer needs before execution.
Read the approval queue guideA workflow that drafts an internal summary does not need the same review as one that changes customer records, sends external messages, handles credentials, or touches regulated data.
Use the lightest review that still lets a reasonable owner explain what happened and recover from a mistake. When the workflow touches customers, records, money, access, public claims, or regulated work, keep review and receipts visible until the failure modes are understood.
The first production version should usually be narrower than the demo. Keep the stop line visible. If the workflow cannot show its source evidence, explain its proposed action, route to the right reviewer, leave a useful receipt, or offer a rollback path, it is not ready for more autonomy.
Common v1 manual-only actions include refunds, contract language, medical or legal advice, account access, credential handling, deleting records, final hiring or HR decisions, unsupported public claims, and anything a reviewer cannot unwind cleanly. A shadow week can reveal which actions deserve more review before launch.
Related resources
See how BaristaLabs scopes source data, least-privilege access, vendor/model assumptions, retention, and rollback before sensitive workflow work moves toward production.
Read the data security pageTurn the worksheet into operating rules for what the workflow may read, draft, change, send, escalate, log, and roll back.
Use the approval policy worksheetGive reviewers the proposed action, source evidence, risk reason, policy rule, and final execution record before risky work leaves the queue.
Read the approval queue guidePair sensitive-system boundaries with the owner, receipt, correction path, and stop rule for each AI-proposed change.
Map the rollback pathLog what started the run, what the workflow saw, what it proposed, who reviewed it, what happened, and how the team can correct it later.
Copy the agent receipt templateUse this when vendor/model exposure, retention settings, quota, SDK behavior, or fallback assumptions need an owner before production access expands.
Copy the model facts registerUse this before analytics agents answer recurring business questions from sensitive datasets, dashboards, warehouses, or semantic-layer paths.
Copy the source-of-truth registerUse the worksheet as an input to a process-automation or AI-consulting conversation before broad permissions are granted.
No. This worksheet is an implementation-planning artifact, not legal advice, a compliance guarantee, or a certification claim. Regulated or sensitive workflows should involve the client's legal, privacy, compliance, security, or operational stakeholders before production use.
No. The worksheet makes the boundary visible so the right people can review it. The actual security posture depends on the implementation, vendors, permissions, hosting stack, data handling, monitoring, and operating process.
Yes, if the workflow touches sensitive systems. The worksheet exposes platform requirements that a demo may hide: permission scopes, vendor data handling, evidence display, approval routing, audit logs, retention controls, and rollback support.
That should be documented for the specific project and vendors involved. For client deployments, BaristaLabs favors vendor and model settings that keep client data out of public model training, and the worksheet should name the expected setting before production use.
No. Low-risk, internal, reversible actions may become safe to automate after testing. Customer-facing, financial, access-related, regulated, public, irreversible, or ambiguous actions should stay under review until the workflow has evidence that more autonomy is safe.
Treat unresolved security, legal, privacy, compliance, vendor, retention, or rollback questions as launch blockers for production access. The workflow can often continue as a prototype or shadow-week test while those answers are collected.
The worksheet defines what should be logged. The agent receipt is the run record left behind after the workflow drafts, routes, updates, sends, publishes, or proposes action.
Security review for one workflow
BaristaLabs helps teams define source data, vendor/model assumptions, approval gates, receipt fields, retention expectations, and rollback paths before AI workflows touch real customer or regulated work.