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AI Governance built for financial services operations

Artian embeds governance and controls into every workflow, providing oversight, trace lineage and human approvals where required.

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Governance doesn't stop at the model

When agents take actions across systems, governance has to extend beyond the model. Controls need to govern what agents can access, what actions they can take, when human approvals are required, and how every action can be traced and reconstructed afterward.

Getting AI approved for production is one challenge. Governing it in production is another. Artian is designed for both.

  1. Define what agents can do

    Enforce operational and policy boundaries with fine-grained access controls, separation of duties, isolated environments, data residency and legal-entity controls, and guardrails that prevent out-of-policy actions.

    • Access Control
    • Separation of Duties
    • AI Governance
    Organization permissions matrix granting roles access to agents across teams

  2. Keep people in control

    Require human review or approvals for high-impact actions, routing decisions to the right people based on risk and your defined controls.

    • Human-in-the-Loop
    • Approvals
    • Human Oversight
    Agent trace log with completed steps and one step waiting on user input

  3. Trace and audit every action

    Maintain end-to-end lineage across data, decisions and actions, with citable sources and immutable audit logs that provide a complete record for review, reporting and audit.

    • Data Lineage
    • Auditability
    • Traceability
    Lineage graph linking case evidence, approvals and change plans across workflow tasks

  4. Govern and validate AI behavior

    Apply your SDLC and MDLC standards across agents, with testing evidence, continuous online and offline evaluations, ongoing monitoring, verifier agents, circuit breakers, checkpoints, and rollbacks.

    • SDLC
    • MDLC
    • Model Risk
    • Evals & Monitoring
    Bar chart of findings by severity above agent risk scores

AI Governance that carries forward

Artian's governance components are reusable, so an approval you win for one component can help accelerate the next workflow.

Work within the controls you already have

Artian is designed to operate within your existing governance and control frameworks, rather than requiring a separate control environment.

What happens end to end

  1. Receive exceptions
  2. Assemble context
  3. Analyze root cause
  4. Decide remediation
  5. Acquire approvals
  6. Execute remediations

FAQs

  • What is AI governance for financial services?

    It is the set of controls that decide what AI may do inside a regulated institution, and that prove afterward what it did. Once agents act across systems, that reaches past the model itself: what an agent can access, which actions it can take, when a person has to approve, and how every step can be traced and reconstructed for review, reporting and audit.

  • What is the difference between model governance and AI agent governance?

    Model governance validates a model: its testing, its performance and its risk. Agent governance also has to cover what happens next, when an agent reads data, calls systems and takes actions. Artian governs both, applying your model risk standards to the model and access controls, approvals and end-to-end lineage to every action an agent takes.

  • How does Artian work within existing governance and control frameworks?

    Artian is designed to operate inside the governance and control frameworks you already run, rather than requiring a separate control environment. Your policies, approval routes and SDLC and MDLC standards apply to agents, and the governance components are reusable, so an approval you win for one workflow helps accelerate the next.

  • How does Artian support human oversight and approval for AI agents?

    High-impact actions can require human review or approval before they run. Artian routes each decision to the right people based on its risk and your defined controls, and your team can inspect, override or pause an agent at any time.

  • How does Artian provide data lineage and auditability for AI agents?

    Every action carries end-to-end lineage across the data it used, the decisions it made and the actions it took, with citable sources. Immutable audit logs keep a complete record, so any outcome can be traced and reconstructed afterward for review, reporting and audit.

  • How does Artian support model risk management?

    Artian applies your MDLC standards across agents, with testing evidence, continuous online and offline evaluations and ongoing monitoring. Verifier agents, circuit breakers, checkpoints and rollbacks keep agent behavior inside the limits your model risk function sets.

  • How can financial institutions scale AI governance across multiple workflows?

    Start with one workflow and reuse its governed foundation for the next. Artian's governance components are reusable, so the controls, approvals and evidence you establish once carry forward, and each new workflow reaches production faster instead of starting its approval from scratch.