For the CISO
AI on the production floor, governed like you'd have built it yourself.
Most vendors bolt AI onto an ERP and ask you to trust it. Cortrova routes every AI request through a seven-stage governance pipeline with zero bypass paths by architecture, logs 100% of mutations and AI requests, and deploys fully air-gapped when the data can't leave the building. Unlimited users means no shared logins eroding your access model.
The challenge
- !Operations wants AI on production data, and the vendor answers about model governance don't survive a second question
- !Audit trails are partial - some systems log changes, some don't, and reconstructing who changed what means correlating exports
- !ITAR-controlled and CUI-bearing programs need deployment isolation that most SaaS vendors can't actually offer
- !Every point system in the plant is another identity store, another attack surface, and another set of shared logins on the floor
How Cortrova answers
- ✓Every AI request passes through a seven-stage governance pipeline - team guard, kill switch, rate limit, budget check, scope validation, model call, audit log - with zero bypass paths by architecture, so governance is not a policy layered on top but the only route a request can take
- ✓100% audit coverage of mutations and AI requests means every data change and every AI action is logged as it happens - the audit trail is complete by construction, not assembled for the auditor
- ✓The kill switch and team guard stages give you an off switch and a scoping boundary for AI activity, so enabling 65 embedded agents never means losing control of them
- ✓TLS 1.2+ in transit, AES-256 at rest, SSO over SAML 2.0 or OIDC, MFA, and 12-role RBAC cover the baseline controls without compensating workarounds
- ✓Cloud, on-premises, and fully air-gapped deployment options - with embedded AI running on on-premises models in isolated environments - support ITAR-controlled deployments and CMMC 2.0 Level 2 readiness
Governed AI
Seven stages between an AI request and an action.
The pipeline is not a filter that suspicious requests get routed through - it is the only path any AI request can take. Each stage can stop a request cold, and the final stage writes the record either way.
Team guard and kill switch
The first stages confirm the requesting team is permitted to use AI at all and that no kill switch is engaged - your ability to halt AI activity, per team or globally, is checked before anything else runs.
Rate limit and budget check
Requests are throttled against rate limits and checked against budget before a model is invoked, so a runaway process or misconfigured agent hits a wall instead of a bill.
Scope validation before the model call
The request's scope is validated against what the requester is entitled to touch before the model is called - an agent cannot reach data or actions outside its granted scope.
Audit log, always
The final stage writes the request, its outcome, and its context to the audit log. Combined with 100% coverage of mutations, every AI action and every data change is reconstructable.
How it works
Adopting For the CISO.
Discovery scopes the security review
The first implementation phase covers deployment model, data classification, and identity architecture, so your security review runs against a concrete design rather than a sales deck.
Deploy to your boundary
Cloud, on-premises, or fully air-gapped with no external calls - in isolated environments the embedded AI runs on on-premises models, so ITAR-controlled and CUI-bearing programs keep data inside the boundary.
Wire identity and roles before data
SSO via SAML 2.0 or OIDC, MFA, and 12-role RBAC are configured during setup, so access is scoped from the first login - and unlimited users means no license pressure toward shared accounts.
Go live with the audit trail already on
Audit coverage of mutations and AI requests is architectural, not optional - it is complete from the first transaction at a validated go-live, which most manufacturers reach in a typical 4-8 weeks.
FAQ
Questions, answered.
Where are the AI models hosted?
That depends on your deployment. In cloud deployments the platform manages model access through the same governed pipeline as everything else. On-premises and fully air-gapped deployments can run the embedded AI on on-premises models with no external calls, so production data never leaves your boundary. Either way, every model call passes scope validation before it executes and lands in the audit log after.
What does the audit trail actually cover?
100% of mutations and 100% of AI requests. Every data change and every AI action is logged as it happens, including requests the pipeline blocked. Because coverage is architectural rather than configured per module, there is no unlogged path to find during an assessment - the record is complete by construction.
Can an AI agent take an action it shouldn't?
The architecture is built against it. Every request from all 65 embedded agents passes the seven-stage pipeline - team guard, kill switch, rate limit, budget check, scope validation, model call, audit log - and there are zero bypass paths by architecture. Scope validation runs before the model call, so an agent cannot reach data or actions outside its grant, and the kill switch halts AI activity when you decide it should.
Does Cortrova support ITAR and CMMC requirements?
Cortrova supports ITAR-controlled deployments through its on-premises and fully air-gapped options, and supports CMMC 2.0 Level 2 readiness with its access controls, encryption, and complete audit coverage. CMMC certification applies to your organization and its assessed environment rather than to a software product, so Cortrova's role is giving that environment controls an assessor can verify.
Get started
See Cortrova through the ciso's screen.
We'll tailor a demo to your role, your KPIs, and your operation.