Intake and risk tiering
A consistent intake process captures purpose, affected populations, decision impact, data dependencies, human oversight, and operational context. Risk tiering then determines the depth of assessment and approval required.
AI governance frameworks
Turn federal AI requirements into an operating governance model.
Elvaris translates NIST and OMB expectations into practical governance mechanisms: intake records, risk tiers, impact assessments, named decision rights, release gates, evidence requirements, and monitoring triggers.
Named governance decisions
Operating models identify who recommends, reviews, approves, challenges, and monitors. Release gates connect those decision rights to required evidence so governance functions as a repeatable process.
Human oversight and monitoring
Controls address consequential decisions, override authority, escalation, drift triggers, and evidence retention. The design makes human-in-the-loop claims observable and testable.
Assessment outputs
Designed for decision, remediation, and audit.
- Consistent AI use-case intake records
- Defensible high-impact risk determinations
- Named decision rights and release gates
- Testable human-oversight controls
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