Kindo
The governed AI platform at the foundation of the stack. Every AI interaction across the organization, chat, agents, or API, passes through enforceable controls before it reaches a model, a tool, or a data source.
Ungoverned AI tools were never built to be accountable.
The tools your employees reach for by default share three design flaws, and each one lands on the security team's desk.
The log is a shrug.
Consumer AI tools keep no record you can inspect. When legal asks who sent what data to which model last quarter, there is no answer, because nothing was ever written down.
Secrets leave on the first keystroke.
Employees paste API keys, customer records, and internal documents into public chatbots every day. The data crosses your boundary the moment they hit enter, with no filter in between.
Policy is a PDF. Enforcement is nowhere.
Each tool has its own admin model, and most offer one switch: on or off. There is no way to allow reads but block writes, or approve one model but not another.
Three outcomes. Here is how Kindo delivers them.
Faster compliance
Complete visibility
Automated management
Governance as architecture, not add-on.
Comprehensive audit logging
Every prompt, response, tool call, and model interaction logged in structured JSON. Fully exportable for compliance review and SIEM ingestion.
DLP on every request
Presidio-powered filters scan every request and response for PII, credentials, API keys, and custom patterns before data reaches a model.
Granular RBAC
Tiered user groups, from basic access through administrative, with distinct permissions for models, integrations, and tool actions per group.
Tool-action-level control
Enable ticket-system reads while blocking creates, updates, and deletes. Governance at the individual action level, not just the integration level.
Model access controls
Administrators decide which providers and models are available, with DLP enforcement that can vary by model tier and sensitivity.
SaaS, hybrid, or on-prem
All governance controls stay enforced regardless of where the platform runs, including self-managed deployments where no data leaves your premises.
Org-wide usage statistics
Total agent runs, success rates, credit consumption, and per-user and per-agent breakdowns over configurable time windows.
Agent monitoring
Full visibility into every agent, scheduled, triggered, or on-demand: creator, last run, run counts, and completion rates.
Sharing permissions
Organizational controls govern external sharing, minimum roles for sharing, and whether integration connections can be pinned to specific agents.
| Capability | SaaS | Self-managed |
|---|---|---|
| AI chat, agents, workflows | ✓ | ✓ |
| DLP, RBAC, audit logging | ✓ | ✓ |
| SSO and API access | ✓ | ✓ |
| Custom / private model hosting | N/A | ✓ |
| Data stays on your infrastructure | N/A | ✓ |
| Certified environments (FedRAMP boundary, IL4/5) | N/A | ✓ |
See the platform under a governed workload.
Request a technical briefing and walk through policy enforcement, audit export, and deployment models with the team.