Trust & Security
Confidential legal workflows need more than a security page.
Trust in legal AI is not only about encryption or hosting. It is also about bounded retrieval, firm-scoped records, review states, provider posture, and making sure work does not leave draft state without attorney judgment.
Core principle
Controlled intelligence, not autonomous risk.
Consumer chat interfaces and legal workflow systems are not the same category of tool. The difference is not branding. It is part of the confidentiality, supervision, and litigation-risk posture of the work.
The posture
What legal buyers should expect from the confidentiality posture.
Workflow boundary
Attorney review remains the control point.
Specialists can prepare work. Attorneys decide what moves forward, what is approved, and what must stay in draft.
Tenant scope
Workspace context stays workspace-scoped.
Records, retrieval paths, and operational context should stay attached to the firm or organization that owns the work.
Auditability
Runs, edits, and approvals should remain visible.
The system should preserve review history and state transitions so supervision is part of the workflow rather than an afterthought.
Controls
AI access is bounded by surface and workflow.
Legal teams should be able to decide where AI is allowed to operate and where human review is always required.
Provider posture
Model access is not the whole security story.
What matters is where data flows, what is retained, what is logged, and how provider boundaries are enforced.
Storage and transit
Encryption is baseline, not the full answer.
Legal workflows also require bounded retrieval, clear storage posture, and disciplined handling of context before it reaches a model.
Why this matters
Security answers one set of questions. Legal confidentiality answers another.
A security page should explain infrastructure controls. A trust and confidentiality posture also has to explain how legal work is scoped, how review is enforced, how provider boundaries are handled, and why the product does not behave like a generic consumer chat thread.
Legal AI risk is not only a security issue. It is also a workflow issue. The system has to make it clear what belongs in draft, what stays attached to the record, what can be reviewed, and what can move into external effect.
Buyer checklist
Where is data stored, and is the workspace tenant-bounded?
Are customer inputs used for model training?
What is retained, what is logged, and what can be deleted?
What reaches the model, and what stays outside the run?
What review boundary exists before work leaves the system?
Can supervising attorneys see what happened in the middle of the workflow?
Operating details
The questions serious buyers will ask next.
Provider architecture
AWS Bedrock and Google Vertex AI
FlowCounsel uses managed inference layers through AWS Bedrock and Google Vertex AI rather than consumer chat surfaces.
Training boundary
Customer inputs are not for model training
Customer inputs are routed through provider settings intended for non-training use rather than contributed back into foundation-model training.
Hosting region
US cloud infrastructure
Application and workflow infrastructure are hosted in US cloud regions, with model requests routed through FlowCounsel services rather than directly from the browser.
Retention and export
Matter data should be portable
Firms will ask how data is exported, how retention is handled at the end of an engagement, and how records remain accessible without lock-in. That posture should be explicit during procurement.
Subprocessors
Operational providers should be disclosed
Subprocessors, hosting providers, and AI infrastructure providers should be visible on the security surface or supplied during procurement.
Compliance stance
Only claim what is actually in place
If a compliance program or audit is not complete, the right move is to say so directly and describe the current operating posture without inflating it.
The standard
Legal AI should be bounded, reviewable, and easy to supervise.
The point is not to outsource trust to a vendor label. The point is to build legal workflows where context is scoped, records stay attached to the right workspace, and attorney supervision remains visible before anything consequential moves forward.