Enterprise AI Governance for Self-Hosted Deployments
Regulated enterprises adopting self-hosted AI must establish governance processes that demonstrate model reliability, data provenance, and access accountability. This framework translates Homegrown Intelligence workflow capabilities into controls that satisfy audit requirements under SOC 2, HIPAA, and emerging EU AI Act provisions. The framework is organized into three domains: model validation, lifecycle management, and access governance.
Model Validation and Qualification
Before a workflow version enters production, it must pass a qualification gate comprising accuracy benchmarking on a curated holdout set, fairness analysis across demographic subgroups, and robustness testing against adversarial inputs. Accuracy benchmarks compare the candidate model against the current production baseline using a paired bootstrap test with 10000 resamples. Fairness analysis computes demographic parity difference and equal opportunity difference across groups defined in the customer's data dictionary. Robustness testing injects Gaussian noise at signal-to-noise ratios from 10 dB to 40 dB and requires output quality metrics to remain within 5 percent of the clean-signal baseline. Qualification results are recorded in a signed attestation document stored alongside the workflow artifact in the registry.
Model Lifecycle Policies
Each workflow version is assigned a lifecycle state: development, qualification, production, deprecated, or retired. The transition from qualification to production requires sign-off from two authorized reviewers in the governance dashboard. Deprecated versions continue serving inference but emit a warning header in API responses indicating the recommended upgrade path. Retirement removes the container image from the registry after a sixty-day notice period during which clients receive a 301 redirect to the replacement version. Policy enforcement is implemented through Open Policy Agent rules evaluated at deployment time; a deployment attempting to use a retired version is rejected with a descriptive error message listing available alternatives.
Access Governance and Segregation of Duties
Role-based access control distinguishes four principal roles: viewer, operator, deployer, and administrator. Viewers can inspect logs, metrics, and model cards but cannot modify configuration. Operators can restart workflows, adjust resource allocations, and trigger manual rollbacks. Deployers promote workflow versions between lifecycle states and manage canary configurations. Administrators manage role assignments, audit log retention policies, and certificate authorities. Every state transition and configuration change is recorded in an immutable audit log with actor identity, timestamp, previous state, new state, and a rationale comment. Quarterly access reviews are facilitated through a report that lists all principals, their assigned roles, and their last login timestamp.
Incident Response Integration
When a workflow health check fails or a metric breaches its threshold, the incident response process follows a documented playbook. The first responder acknowledges the alert within fifteen minutes through PagerDuty. Diagnosis begins with a triage script that collects current logs, resource utilization snapshots, and recent deployment history. If the root cause is identified as a configuration drift, the remediation restores the last known good configuration from the version-controlled backup. If the root cause is a model regression, the playbook initiates an automated rollback to the previous qualified version and opens a bug ticket in the issue tracker with attached diagnostic data.