Trust Built for Regulated Financial Institutions

Structured. Repeatable. Evidence-based. Defensible. Privacy-first. Governed.

Financial Institutions Don't Need Experimentation

They need infrastructure they can explain, defend, and audit. StandardC AI™ is built within an Enterprise Risk Management foundation designed specifically for regulated environments. Every analysis is grounded in submitted evidence, processed within governed workflows, and preserved with traceable audit controls.

TRUST ARCHITECTURE

Designed to Be Defended

Six architectural principles that make StandardC AI™ safe for regulated environments — not bolted on after the fact, but embedded from the ground up.

Configured to Your Institution

Each agent aligns with your boardapproved risk appetite statements, credit underwriting standards, BSA/AML policies, and compliance guardrails. Configuration is versioned, governed, and audit-ready

Trustworthy by Design

Every analysis is traceable to underlying evidence inputs and review context. Risk signals are tied directly to documentation, not inference. Non-conforming outputs are rejected before storage.

Privacy-First Architecture

Deterministic PII redaction and cryptographic tokenization occur before AI analysis begins.
A verification sweep confirms complete redaction. Sensitive attributes are minimized to reduce exposure and bias risk.

Human-in-the-Loop

The platform does not automate approvals, denials, or regulatory filings. Guardrails and policy constraints enforce evidencegrounded outputs. Human reviewers retain authority
at all times.

Repeatability & Defensibility

Version pinning, deterministic retrieval, and schema-constrained output ensure the same inputs under the same configuration produce consistent findings — supporting examiner readiness.

Bias Mitigation

By minimizing unnecessary identity signals and constraining outputs to evidencegrounded analysis, the platform reduces bias risk and promotes equitable review practices aligned with fair lending requirements.

Regulatory Alignment:

Designed to satisfy guidanceunder SR 11-7 (Model Risk Management), SR 23-4 (Computer-Science Based Models), and CFPB Circular 2023-03 (Adverse Action & AI).

SECURITY & ACCESS

Institutional-Grade Security Controls

Role-Based Access Controls

Only authorized financial institution users can view documentation and analysis outputs. Permissions enforced at every layer with
least-privilege principles. Separation of duties across agent creation, review, and deployment.

Single-Tenant Architecture

Each financial institution operates within a fully isolated tenant boundary. No data commingling across institutions. Multi-tenant capability available for service providers with complete isolation.

No Model Training on Customer Data

Customer data is never used to train or fine-tune models. All processing is inference-only. Data governance prevents any flow to training pipelines.

Complete Event Audit Trail

Every query, context pack, token usage, reviewer override, and human intervention is logged with user ID, timestamp, configuration version, and input/output references.

DATA HANDLING

Clear Data Commitments

No Model Training on Your Data

StandardC does not use customer data for model training. All processing is inference-only. Data governance prevents any flow to training pipelines.

On-Demand Deletion

Customer evidence and outputs are retained only per institution-defined retention policy and can be deleted on demand when permitted.

Retention Controls

Data retention controls align with institutional and regulatory requirements. Evidence preserved for audit and examination per your governance framework.