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AI governance at runtime
Control what AI systems are allowed to do during execution. IndyKite brings policy, intent and context together to make and enforce authorization decisions at the point of action.
Evaluate each consequential action at runtime
Keep agent authority aligned with the delegated task
Trace why each action was allowed, limited or denied

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Trusted by leading enterprises

Close the gap between approval and action
An approved agent can still attempt an unauthorized action. IndyKite connects governance, identity and security at execution to decide whether this agent should take this action, under these conditions, right now.
Policy: define the rules
Define permitted workflows, actions and resources, the conditions that apply, and where human approval is required.
Intent: preserve the purpose
Keep downstream actions aligned with the task delegated to the agent, even when its owner has broader permissions.
Context: understand the request
Connect identity, data sensitivity, consent and transaction history to evaluate the circumstances of each action.
Authorization: enforce the decision
Allow, restrict, escalate or deny each consequential action as it occurs, with a traceable record of why.
Put runtime governance into action
01
Connect trusted context
Use the Identity Knowledge Graph to connect actors, data and relationships. Bring provenance and TrustScore signals into runtime decisions.
02
Evaluate each requested action
Bring policy, delegated intent and current transaction context together to decide whether the action should proceed.
03
Enforce and trace the outcome
Release, filter or mask data; allow, escalate or deny actions. Retain the identity chain, context and policy behind each decision.
Hear from our customers
Reitan Retail
With IndyKite’s Platform, we will be able to support new and market-unique methods of onboarding and securing customers, inject more value for customer loyalty programs, drive new flexible payment and check-out solutions, and cross-brand benefits, ensuring our customers enjoy personalized, relevant, and rewarding experiences every time they engage.
Erik Torkildsen,
Head of Identity and Payments, Rema 1000
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Rockwell Automation
IndyKite’s platform offers the scalability, flexibility, and security needed to optimize our data management across our enterprise systems.
Ryan Carpenter,
Vice President, Commercial Programs & Operations, Rockwell Automation
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Deutsche Telekom
IndyKite serving as an aggregator not only for Deutsche Telekom but for the entire CAMARA community and a broad range of services opens up possibilities for a range of clients that can benefit from accessing rich data they would not otherwise have access to - demonstrating the power of this initiative in action.
Peter Arbitter,
SVP MACE, a Deutsche Telekom initiative
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PACCAR
It’s helping PACCAR more quickly and reliably deliver value to our customers and make better decisions faster internally. We’re excited to be working with IndyKite.
Dallas Thornton,
Director Digital Services, PACCAR
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AI runtime governance questions

AI runtime governance controls what an AI system is allowed to do during execution. It evaluates policy, delegated intent and current context, then enforces a decision at the point of action.

An action may depend on data sensitivity, consent, the refund amount, previous decisions or the current workflow state. Bringing these signals together helps determine whether the next action is appropriate right now.

Approval establishes what an agent is permitted to do in principle. Each consequential action still needs a decision based on the specific task, requested resource and conditions at that moment.

IndyKite retains the identity chain, delegated intent, requested action, applicable policy, transaction context and authorization outcome so teams can reconstruct why a request was allowed, limited, escalated or denied.

It connects existing governance, identity and security controls at the point of execution. Those systems remain essential; the runtime control layer brings their signals together to make and enforce an action-level decision.

A user may have broader permissions than they intend to delegate for a task. Preserving the original purpose lets downstream requests be evaluated against that narrower authority as work moves between agents, tools and applications.

Choose a focused agent workflow. Define the actions and resources it can use, the conditions that apply and where approval is required. Then connect intent, context and enforcement at each consequential decision point.

Resources

Blog

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August 28, 2026

Agentic AI needs a new control model

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The Enterprise Guide to EU AI Act Compliance

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