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Solution family

Know which AI agents are running, what they can touch, and what they did.

DuluthPath inventories the AI agents operating across your enterprise, enforces access and approval policy, and provides the audit evidence auditors and regulators are starting to ask for — including DuluthPath's own agents.

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Organizational problem

AI agents are proliferating faster than enterprise governance can keep up. Different lines of business adopted different tools. Some agents have read access they shouldn't. Some execute actions without human approval. Nobody holds a complete inventory. Auditors are starting to ask for one.

Why existing systems miss it

Each AI deployment is governed inside the tool that deployed it. There is no cross-tool inventory. There is no cross-tool policy. The CISO cannot answer 'which AI agents have access to our customer PII?' from a single source today.

Signals DuluthPath analyzes

  • AI agents operating against connected systems that are not in the central inventory.
  • Access patterns on sensitive data that exceed the stated purpose of the agent.
  • Agent actions that executed without the human approval the policy requires.
  • Decisions the agent cannot explain against the evidence it had at the time.
  • AI investments without a measurable business outcome mapped to them.
  • Policy violations against the configured guardrails (data residency, data class, action class).

How DuluthPath detects the issue

DuluthPath discovers AI agents through the same read access it uses for problem detection — API logs, service accounts, audit trails from connected platforms. The inventory is reconciled against the central policy register continuously. New agent appearances trigger a review event.

Root-cause analysis

For each policy violation the Orchestrator identifies the agent, the action class, the data class touched, the policy clause breached, and the owning business sponsor. The analysis chain is itself auditable.

Financial, customer, operational and compliance impact

financial impact

Avoid future AI-governance fines; defend the AI investment with measurable outcomes.

customer impact

Fewer customer-data incidents; auditable explanations when customers ask.

operational impact

A single AI inventory the CIO/CISO/legal team can rely on in a review.

compliance impact

Audit-ready AI inventory, policy conformance log, explainability evidence per decision.

Recommended actions

  • Compliance-approved revocation of excess data permissions.
  • Policy-violation escalation to the AI steering committee with the full evidence pack.
  • Approval-gate enforcement on action classes previously running unchecked.
  • Agent-retirement workflow for abandoned or value-less deployments.

Approval and execution controls

Compliance / Legal approve every policy enforcement action. Hash-chained log of every approval, rejection and executed enforcement. Rollback plans documented.

Outcome verification

Did the agent's access actually get reduced? Did the next audit cycle find fewer policy breaches? Each enforcement action is verified in the next review window.

Relevant integrations

Microsoft Dynamics 365SalesforceQuickBooks OnlineSAP Business One

See /integrations for the per-connector status (self-service live · available with assisted activation · planned).

Industry adaptations

  • Financial Services — AI model governance against regulator-published model-risk frameworks.
  • Life Sciences — AI-assisted decisions on validated workflows with evidence preserved.
  • Government & Nonprofit — AI-decision explainability for citizen-facing processes.
  • Healthcare — PHI access controls on clinical-support AI agents.

Next step

Find your highest-value govern enterprise ai problem.

Run a 5-minute Problem Scan, or request a scoped Enterprise Value Diagnostic. We'll quantify the exposure and hand you a prioritized action list.

Run a Problem Scan Request a Value Diagnostic