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ai-governance-architecture

System architecture for Salesforce AI governance: MLOps pipeline design, AI Audit Trail architecture, Einstein Trust Layer security design, Policy-as-Code engine, and regulatory compliance design for EU AI Act and similar frameworks. NOT for authoring AI ethics policy and human-oversight requirements - use admin/ai-ethics-and-governance-requirements. NOT for model tier selection or Supervisor/Specialist topology - use architect/ai-platform-architecture.

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AI Governance Architecture

This skill activates when an architect needs to design a comprehensive AI governance system for a Salesforce org — covering the 4-layer governance framework (AI/ML Lifecycle, Security/Guardrails, Audit/Observability, Responsible AI Controls), Audit Trail architecture, Policy-as-Code engine, and regulatory compliance design. It does NOT cover individual Agentforce agent configuration, general security architecture, or AI ethics policy documentation.

Before Starting

Gather this context before working on anything in this domain:

Core Concepts

The 4-Layer AI Governance Framework

Comprehensive Salesforce AI governance spans four layers:

Layer 1 — AI/ML Lifecycle Governance: Model registry, training data documentation, model versioning, approval gates before production deployment. Requires MLOps pipeline integration (Weights & Biases, MLflow, or Salesforce Model Builder's built-in versioning).

Layer 2 — Security and Guardrails: Einstein Trust Layer (zero-data-retention, prompt injection detection, toxicity filtering), topic guardrails on Agentforce agents, data masking for PII in prompts, BYOLLM routing policies.

Layer 3 — Audit and Observability: Salesforce Generative AI Audit Trail (requires Data Cloud), prompt/response logging, model performance monitoring, usage analytics. 30-day retention requires external export pipeline.

Layer 4 — Responsible AI Controls: Fairness monitoring (bias detection), human-in-the-loop override design, transparency documentation, explainability requirements for regulated use cases.

AI Audit Trail Architecture

The Salesforce Generative AI Audit Trail captures:

Architectural constraints:

Recommended architecture for long-term compliance:

  1. Configure Data Cloud Audit Trail export to S3 or SIEM (Splunk, Datadog)
  2. Set export frequency to daily (within 30-day window)
  3. SIEM provides real-time alerting on anomalous model behavior
  4. Data Cloud provides the Salesforce-native audit surface for near-real-time review

Policy-as-Code Engine

Policy-as-Code for Salesforce AI governance enforces rules declaratively:

Policy changes require code review and deployment — they are not ad-hoc UI configuration. This provides auditability of governance policy changes over time.

Common Patterns

4-Layer Governance Architecture Design

When to use: Any organization deploying Agentforce, Einstein Prediction Builder, or BYOLLM models in a regulated industry or for high-risk use cases.

How it works:

  1. Layer 1: Integrate model registry (Salesforce Model Builder or external MLflow) with approval workflow before any model is promoted to production.
  2. Layer 2: Enable Einstein Trust Layer. Configure topic guardrails on all Agentforce agents. Configure data masking for PII fields. Define BYOLLM routing policies.
  3. Layer 3: Enable Generative AI Audit Trail (requires Data Cloud). Configure daily export to S3/SIEM. Set up SIEM alerts for anomaly patterns (unusual prompt volumes, blocked topic attempts).
  4. Layer 4: Define fairness monitoring thresholds. Document human override points in agent workflows. Create transparency documentation for each deployed model.

Why not just Trust Layer: Trust Layer provides prompt-level safety controls. It does not govern the model lifecycle, provide long-term audit evidence, or satisfy EU AI Act documentation requirements. Governance requires all four layers.

Audit Trail Export Pipeline

When to use: Regulated industries (financial services, healthcare) requiring AI audit evidence beyond 30 days, or when real-time anomaly detection is required.

How it works:

  1. Configure Data Cloud data stream export to S3 (daily batch).
  2. S3 triggers Lambda or MuleSoft flow to ingest into SIEM (Splunk/Datadog).
  3. SIEM applies detection rules: high-frequency unusual topics, blocked content patterns, after-hours model invocations.
  4. SIEM alerts fire to security team on anomaly detection.
  5. Audit records retained in S3 for compliance evidence period (typically 7 years).

Decision Guidance

SituationRecommended ApproachReason
Basic AI safety for AgentforceEinstein Trust Layer + topic guardrailsMinimum viable safety layer
Regulated industry AI deploymentFull 4-layer governance frameworkCompliance and audit evidence requirements
Long-term audit evidence (> 30 days)Data Cloud Audit Trail + S3/SIEM exportAudit Trail retention is only 30 days natively
BYOLLM governanceExplicit routing through Trust Layer + extended auditBYOLLM calls bypass audit unless explicitly routed
EU AI Act high-risk use case4-layer framework + conformity assessment documentationAugust 2026 compliance deadline
Real-time anomaly detectionSIEM integration with Audit Trail exportAudit Trail refreshes hourly, not real-time
  1. AI inventory — List all deployed and planned AI/ML features: Agentforce agents, Einstein Prediction, BYOLLM models, third-party AI integrations. This drives the governance scope.
  2. Regulatory mapping — Map each AI use case to applicable regulations (EU AI Act risk categories, financial services AI guidance, healthcare AI requirements). Identify high-risk use cases requiring conformity assessment documentation.
  3. Layer-by-layer design — Design each governance layer independently: model lifecycle (Layer 1), Trust Layer and guardrails (Layer 2), audit and observability (Layer 3), responsible AI controls (Layer 4).
  4. Audit trail architecture — Design the Data Cloud → S3/SIEM export pipeline. Determine retention period and access controls for audit records.
  5. Policy-as-Code design — Define all governance policies as code artifacts (topic guardrails, data masking rules, action allowlists). Establish code review process for policy changes.
  6. BYOLLM routing — For any BYOLLM integration, explicitly confirm routing through Trust Layer for audit capture. Document which models are routed and which are not.
  7. Review — Confirm Trust Layer is not presented as complete governance. Confirm 30-day Audit Trail retention limitation is addressed with export. Confirm BYOLLM routing is documented.

Review Checklist

Salesforce-Specific Gotchas

  1. Trust Layer is not a complete AI governance framework — Enabling Trust Layer addresses prompt safety and zero-data-retention. It does not address model lifecycle governance, long-term audit evidence, fairness monitoring, or regulatory documentation. Organizations that enable Trust Layer and consider governance done are exposed.
  2. AI Audit Trail requires Data Cloud and has 30-day retention — Organizations without Data Cloud have no native AI Audit Trail. Even with Data Cloud, the 30-day retention window means compliance evidence must be exported within 30 days or it is permanently lost.
  3. BYOLLM calls bypass the audit trail by default — BYOLLM model invocations do not automatically appear in the Generative AI Audit Trail. Explicit routing configuration through the Trust Layer is required. Organizations that add BYOLLM integrations after initial governance setup frequently miss this.

Output Artifacts

ArtifactDescription
AI governance architecture document4-layer framework design with technology mapping per layer
Audit trail architecture designData Cloud → S3/SIEM pipeline with retention and alert design
Policy-as-Code specificationsTopic guardrails, data masking rules, and action allowlists as deployable artifacts
Regulatory compliance matrixEU AI Act or equivalent risk category mapping with control evidence requirements

Dependencies

Package files