Agents work inside workflows
Every agent has an objective, defined inputs and outputs, and scoped tools, and it acts only within the process it belongs to.
Security
Put AI in production without giving up control.
Mogno is a governed operational AI platform. Every app, agent, workflow, and integration runs under one model of tenant isolation, permissions, human approvals, encrypted credentials, and audit. So the business moves fast, while IT keeps control of data, security, and what goes live.
Built for security review: tenant isolation, role-based access, encrypted secrets, sandboxed code, human approvals, full audit, and your own model keys.
Governance
One governance model under everything your teams build
Ungoverned AI creates real exposure: actions with no approval, credentials in the wrong place, and code no one reviewed. Mogno closes that gap by making the controls the architecture. Apps, agents, workflows, data, and integrations share the same tenant model, permissions, and audit from the first app, so nothing reaches production outside of it.
On by default
AI governance
AI that acts inside the workflow, never outside it
Mogno agents are governed participants in a process, not a chat on the side. They propose, summarize, extract, compare, and prepare, and governance decides what each one is allowed to run on its own.
Sensitive actions wait for approval
Approving a payment, a contract, or a write to a system of record always pauses for a named human, by policy.
Scoped permissions and data
Each agent reaches only the tables, files, and systems it is granted, and nothing beyond that grant.
Structured execution records
Every action an agent takes is logged with its context and result, so any decision can be reviewed after the fact.
Enterprise readiness
Built to pass a security review, not just a demo
The questions your security, data, and infrastructure teams bring to any vendor, answered up front: how data is protected, what happens when something fails, how incidents are handled, and how integrations stay contained.
01 Data boundaries
Data protection and privacy
- Tenant data stays in dedicated schemas, with data boundaries agreed before any pilot.
- Retention policies and PII-aware audit logs keep personal data controlled over its lifecycle.
- Bring your own model keys, so prompts and data reach only the providers you approve.
02 Checkpointed
Business continuity and recovery
- Agent sessions and workflows recover from checkpoints, resuming from the last completed step.
- File storage is versioned, and the platform is built to survive a single-node failure.
- Nightly health checks watch model providers, data, and services.
03 SIEM-ready
Incident response and monitoring
- Every sensitive action emits structured logs and telemetry, ready for your SIEM.
- Rate limiting and account lockout blunt credential abuse.
- Documented runbooks cover authentication incidents and account compromise.
04 Read-first
Integration and API security
- Integrations start read-first, so nothing is written back without explicit approval.
- API keys are scoped per workspace, hashed at rest, and revocable at any time.
- Each app and agent reaches only the systems it is granted.
How governance is set up
Governance is set up and reviewed before anything goes live
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Set up governance
Workspaces and environments, roles and permissions, credential and data policies, and authorized integrations are configured first.
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Review before go-live
Security reviews permissions, approvals, and what each app and agent can access, then signs off before anything is published.
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Go live with audit on
The workflow reaches production with logging, approvals, and observability active from the first run.
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Evidence and expansion
Audit trails, runbooks, and answers to your security questionnaire support review, and the same model carries to the next workflow.
FAQ
What security and IT teams ask
Where does our data live, and how is it isolated?
Each customer runs in its own tenant, with relational data in dedicated schemas and the tenant always sourced from the server session, so one customer's data never crosses into another's.
Can we use our own AI provider and model keys?
Yes. You can bring your own model keys, so prompts and data go to the provider you approve, under your own account.
How do you handle credentials and secrets?
Credentials are encrypted at rest and resolved on the server at run time. They are never placed in the agent's context or in app code, and visibility is limited per workspace.
How do you keep AI-generated apps and code safe?
Custom code is checked against dangerous patterns before it is saved and runs in an isolated sandbox, and generated apps are blocked from common injection patterns at build time.
Are we locked in? Can we export our apps and data?
No lock-in. Your apps and data stay yours to export, and you are not tied to one cloud's identity or model stack.
Do you support our security review and questionnaire?
Yes. We support your security questionnaire and can share an architecture packet and runbooks during evaluation, alongside a pilot with agreed data boundaries and success criteria.
Where is the platform hosted, and how is data residency handled?
Mogno runs on enterprise cloud infrastructure, and the data boundaries and residency for your workspace are agreed before the pilot, so you know where your data sits.
Do you train AI models on our data?
No. Mogno orchestrates AI inside your workflows, it does not train models on your data. With your own model keys, how prompts and data are handled is governed by your agreement with the provider you choose.
Get started
Put AI in production your security team can stand behind.
See how Mogno runs apps, agents, and workflows under one governed model, with the tenant isolation, approvals, and audit your security team needs, in a live walkthrough.