Intent-based access control is an approach to governing how data is used by applications and AI systems, based on the purpose and context of each request.

As AI agents interpret data, make decisions, and trigger actions across systems, access control must extend beyond identity and permissions to evaluate intent. This includes understanding why data is being requested, how it will be used, and whether that use aligns with policies, consent, and operational constraints.

Each request is evaluated in real time against a combination of signals such as relationships, provenance, sensitivity, trust, and execution context. This ensures that AI-driven actions are grounded in data that is appropriate for the task, under the conditions in which it is being used.

In practice, intent-based access control operates at the point of data retrieval and action, continuously assessing whether an AI system or application should be allowed to proceed. It provides traceability into what was requested, how intent was interpreted, and why a decision was made, enabling accountable and policy-aligned execution.

With IndyKite, intent-based access control is delivered through a context-driven control layer that governs how data is retrieved, interpreted, and acted on at runtime. It ensures that AI systems operate with precision, using data in ways that are consistent with enterprise policies, contextual constraints, and trust requirements.

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