AI governance designed into the architecture.
Governance that lives in a policy document does not reach the system. We help organizations translate governance objectives — lineage, accountability, evaluation, review, retention — into the technical and operational requirements that engineering and delivery teams actually implement.
The gap we close
Enterprises typically know what their AI must comply with — data privacy rules, internal model-risk expectations, audit requirements, security standards — but not how those objectives become architecture: where lineage is captured, how prompts and policies are versioned, what gets logged for an auditor, who reviews a high-risk result, and who owns the system in production. That translation layer is what BIS designs.
To keep expectations precise: BIS translates governance objectives into technical and operational requirements — it does not provide legal opinions, regulatory certification, penetration testing, independent compliance attestation, financial audits, or a guarantee of regulatory approval.
What governance-in-architecture covers
Inventory and classification
AI inventory and use-case classification, with risk-level categories and escalation rules for higher-stakes uses.
Lineage and provenance
Data lineage from source systems through retrieval and prompts to each output, so any result is explainable to its inputs.
Access control and privacy
Access-control-aware retrieval and de-identification patterns, aligned with security and privacy requirements.
Evaluation and human review
Defined evaluation criteria, quality measurement, and human review and escalation points for high-risk decisions.
Versioning and retention
Versioning of models, prompts, retrieval and embedding configurations, and policies — with evidence retention scoped to audit needs.
Operations
Audit-ready logging, production monitoring, and defined ownership and accountability across engineering, risk, and compliance roles.
Governance is integrated into engineering and delivery — into pipelines, workflows, and operating models — rather than handled as a separate review gate at the end. See how engagements are structured in how engagements work.
How governance engagements start
The Regulated AI Architecture Assessment delivers the security, privacy, and governance risk register and the explainability/evaluation requirements that governance work needs. Governance controls are then designed into the target architecture and validated through a POC or pilot — the same progression described on the Explainable AI page.