AI Trust Infrastructure for Enterprise AI
AI systems are gaining access to enterprise data, tools, APIs and workflows. MRCortex is building the trust infrastructure organizations need to discover AI, understand its authority, govern supported consequential activity, and preserve verifiable evidence of what occurred.
AI systems are no longer limited to generating text. Agents can call APIs, use tools, access enterprise data, and take part in business workflows. That creates a new class of questions for organizations:
Traditional monitoring alone does not answer all of these.
Identify and characterize AI systems, agents, runtimes, relationships and relevant access through supported discovery mechanisms. Where a fact can’t be established, Discovery records it as UNKNOWN rather than guessing.
Visibility is not authority.
Evaluate supported requests against deterministic governance controls, authority context and organizational policy. Consequential authority stays structurally separate from an AI’s proposal.
Capability does not confer authority.
Record and verify integrity-protected evidence of governed activity, so organizations can verify and review governed decisions, relevant changes and recorded system history.
Evidence is not permission.
Discovery detecting, SARVA deciding, COSMOS recording — an illustrative view.
Capability
what it can do
≠Authority
what it may invoke
≠Permission
what policy allows
An AI may be technically capable of an action without being authorized to perform it — and authorization is not the same as being permitted by policy. SARVA keeps the three separate: what an AI can do, what authority it holds, and what governance permits.
Know what AI is operating and how it connects to the organization.
Separate AI capability from organizational authority.
Preserve evidence of governed activity and relevant changes.
Reduce uncertainty around AI systems participating in consequential workflows.
Keep people in the governance process where required.
The core governance and evidence architecture is implemented and tested. MRCortex is completing the enforced consequential-execution architecture required for Customer-Ready V1.
9,208
registered automated engineering checks
0
failures at M5-R1 closure evidence d5284fe
86
environment-skipped checks reported separately and not counted as passed
Registered automated engineering checks — not a certification, external validation or assurance claim.
Customer-Ready V1 in development
Governed decisions are recorded in a hash-linked (SHA-256) trail. Modifications are detectable.
Each governed decision is linked to the policy version in force at the time.
Escalated decisions require human approval with recorded justification.
Designed with enterprise assurance requirements in mind, and mapped or being mapped to the EU AI Act, NIST AI RMF, ISO 42001, ISO 27001 and GDPR. Alignment, not certification.
Technical architecture, governance model and engineering evidence for MRCortex AI Trust Infrastructure. Available on request.
Request the overviewYou can't govern what you can't see.
Built for
AI governance · AI oversight · compliance · auditability · enterprise trust
