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BaristaLabs builds AI agents, automation, and internal workflow tools for small businesses. That work can sit close to customer records, operational data, documents, credentials, and business processes that should not be handed to a model casually.
We treat data security as a scoping decision before it becomes an implementation detail. For a sensitive workflow, the first useful artifact is not a broad AI policy. It is a named boundary: what the workflow may read, what it may draft, what needs review, what gets logged, and how the team can remove or correct the result.
This page explains the safeguards we use during discovery, prototyping, and production implementation. Some practices are default working habits. Others depend on the system, data, vendors, hosting stack, and client requirements involved.
Not every prototype needs the same controls as a production workflow that touches protected, regulated, or customer-facing data. When risk rises, the project plan should make the controls visible enough for a client security, privacy, legal, compliance, or operations stakeholder to review.
Healthcare, certification, finance, HR, legal, and customer-support workflows can carry obligations that go beyond normal software delivery. Before an AI agent or automation is allowed to touch those processes, we look for the workflow boundary first: what data is needed, what sources are blocked, who approves actions, what gets logged, what must never be automated, and which vendors are acceptable for the data involved.
Our responsible AI workflow guides include an approval queue guide for turning boundaries into human review and escalation rules. The AI agent receipt template shows the field set a workflow can leave behind after an agent drafts, routes, updates, sends, or publishes work. The AI dependency exception register records which packages, SDKs, connectors, or tool dependencies are allowed, blocked, excepted, or under review near sensitive systems.
If you need a copyable artifact before the workflow gets access, use the AI workflow security review worksheet to map source data, excluded fields, vendor/model exposure, approval triggers, receipt fields, retention, and rollback for one sensitive workflow. If the workflow also needs approval, receipts, evals, and rollback planning, use the AI workflow controls guide before expanding permission.
For an example of risk review around sensitive data, read our article on HIPAA-sensitive healthcare agents. For a production modernization example, see the National Certification Board AKS upgrade case study.
Security decisions are part of the work, not a separate policy page. They show up when we scope process automation, plan AI consulting, choose the first safe pilot, and decide which integrations should be allowed into production.
You do not need a finished platform choice before the first security conversation. Bring one workflow and enough real examples to test the boundary.
If your project involves customer records, protected data, regulated workflows, or an AI agent that can take action, ask us to walk through the boundary before implementation. We can map source data, approval gates, receipt fields, vendor/model assumptions, retention needs, and rollback expectations for one workflow.
Request a security review conversationBaristaLabs scopes each project around the minimum data and access needed for one workflow. We define the approved source list, least-privilege access, reviewer checkpoint, vendor/model assumptions, receipt fields, and project-specific retention or removal expectations before sensitive work moves toward production.
We do not claim public SOC 2 Type II certification or ISO 27001 certification on this page. We can align project work with relevant controls, support client compliance review, and provide security notes for the specific workflow under discussion.
Yes, with appropriate scoping. Healthcare, certification, finance, HR, legal, and customer-data workflows require extra review before automation: data classification, allowed and blocked sources, approval gates, auditability, vendor/model review, receipt fields, and clear boundaries for what an AI agent may and may not do.
Project architecture should make this explicit. For client deployments, BaristaLabs favors vendor and model settings that keep client data out of public model training, and we document project-specific data handling choices before production use.
Use the security-review contact path to ask for security notes for your workflow. Bring the workflow name, systems touched, sample records, action boundary, reviewer role, retention needs, and any legal or compliance review requirements.
We encourage responsible disclosure. If you discover a security issue involving BaristaLabs or a system we maintain, please contact security@baristalabs.io with enough detail for us to investigate.