Approval workflows
Pause execution on high-consequence actions and route a structured review to an authorized person.
Governance framework & AI policies
Governance is the foundational layer that makes Rival enterprise-grade. Rather than treating compliance and risk oversight as afterthought controls, Rival builds permissions, human approval checkpoints, model guardrails, and audit logging directly into the platform architecture. That design lets organizations scale automated AI safely while keeping human judgment at the decision points that matter. For the plain-language definition, see What is Governance.
Governance-first architecture
Section titled “Governance-first architecture”In traditional software deployments, governance controls are often bolted on after launch. Rival takes a governance-first approach, enforcing organizational security rules before any agent or workflow runs in production.
- Upfront policy configuration — administrators set allowed models, data boundaries, and role permissions before teams adopt the platform.
- Controlled innovation — builders create tools and workflows freely inside pre-vetted parameters, without risking a security or compliance breach.
- No shadow AI — every active agent, function, and workflow stays visible, owned, and audited across the organization.
The four control surfaces
Section titled “The four control surfaces”Guardrails & allowed models
Restrict which foundation models can run, and enforce data-privacy limits at the platform runtime.
Roles & permissions
Define what each role can build, run, and govern, so access matches responsibility.
Audit logs
Keep a complete, time-stamped history of every run, decision, and policy event.
Human-in-the-loop
Section titled “Human-in-the-loop”The core operational principle of Rival governance is straightforward: high-consequence actions retain human oversight. Workflows handle routine data collection, summarization, and preparation automatically. When an execution path reaches a step that carries financial, operational, or legal risk, execution pauses until an authorized team member signs off.
Operations that typically require human approval:
- Committing financial spend or processing transaction refunds.
- Sending external emails, notifications, or public releases.
- Modifying sensitive database records or customer PII.
- Provisioning elevated system access or granting administrative permissions.
Programmatic AI policies
Section titled “Programmatic AI policies”AI policies convert corporate security guidelines into machine-enforced platform rules. Enterprise Admins configure them centrally, and they apply across all users, agents, and workflows.
- Model eligibility — mandate which foundation models are approved for specific teams or tasks.
- Data privacy boundaries — restrict agents from passing sensitive customer data or internal PII to external APIs.
- Execution gates — define the operational thresholds that trigger mandatory human review.
- Automated enforcement — non-compliant actions are blocked automatically, rather than relying on individual memory or a policy document nobody reads.
Scaling governance with adoption
Section titled “Scaling governance with adoption”As adoption expands across business units, governance has to scale without a human inspecting every run.
- Role-based guardrails — set clear permission boundaries for admins and team members.
- Departmental scoping — partition visibility, cost controls, and review tasks across structured teams.
- Automated auditing — organization-wide activity logs capture every query, model response, and approval step automatically.
The aim is that an organization’s hundredth workflow is as secure and compliant as its first, without a hundred times the administrative effort.
Core governance components
Section titled “Core governance components”| Governance pillar | Platform feature | Operational outcome |
|---|---|---|
| Human oversight | Approval workflows | Pauses execution for human review on high-risk actions |
| Policy control | Guardrails and allowed models | Restricts unapproved models and enforces data-privacy rules |
| Operational tracking | Audit logs | Maintains a complete, time-stamped history of every execution |
A worked example: PII and approval policy
Section titled “A worked example: PII and approval policy”Consider a financial services company automating customer support responses.
- Policy setup — an Enterprise Admin configures a global policy: any workflow handling customer PII must run on an approved enterprise model and require human approval before sending an external message.
- Automated compliance — a team member builds a new account-management agent using RivalBot.
- Enforced guardrails — when the agent tries to email an account summary containing sensitive data, the platform intercepts the action, masks the PII, and routes a draft to the team manager for sign-off before it sends.