Control Agentic AI with Intent-Based Permissions
▼ Summary
– Action-based permissions have been the foundational security control in identity and access management systems for decades, similar to seatbelts in early cars.
– These traditional permissions are insufficient for AI agents because they only control what actions are performed without understanding the purpose behind them.
– Intent-based permissions represent the next evolution, examining context and purpose to determine if actions align with approved business objectives.
– This new approach allows AI systems to operate autonomously while dynamically preventing actions that don’t match legitimate business purposes.
– A hybrid model combining both action-based guardrails and intent-based governance creates a security system that is both protective and adaptive.
The evolution of identity and access management (IAM) is reaching a critical juncture, much like automotive safety did as cars grew more powerful. For years, action-based permissions have served as the foundational seatbelts of enterprise security, defining what users or systems are permitted to do. However, with the emergence of agentic AI and autonomous software agents that operate independently and make decisions on a large scale, IAM must advance. It now requires intent-based permissions that comprehend not just what an AI agent is doing, but the underlying reason behind its actions.
Action-based permissions continue to be the bedrock of most IAM frameworks. They function by listing permitted operations, such as read, write, update, or delete, or by providing limited API access. For human users or predictable automated scripts, this approach is effective. It upholds the principle of least privilege, generates clear audit logs, and simplifies compliance reporting. Yet, when applied to AI agents, these controls reveal their limitations.
To prevent workflow disruptions, administrators frequently assign excessively broad permissions, inadvertently creating security vulnerabilities. Conversely, overly rigid restrictions can hinder beneficial activities and annoy business teams. Crucially, action-based systems only record the “what” of an operation, completely missing the “why.” If an AI agent tries to erase a dataset, is it performing a scheduled maintenance task or carrying out a malicious act? The permission framework has no way of knowing. These controls can keep the agent within its assigned boundaries, but they cannot interpret its goals or the purpose of its actions. They act like a seatbelt, offering protection only after an incident has occurred, rather than preventing it.
Intent-based permissions elevate IAM by analyzing the purpose driving an action. They incorporate contextual elements like the nature of the task, the sensitivity of the data involved, user delegation status, and real-time risk indicators into access decisions. This is analogous to a car’s adaptive driver assistance system, which proactively steers the vehicle away from dangers to avoid accidents altogether. Intent-based permissions enable AI systems to work autonomously while dynamically blocking activities that deviate from legitimate business objectives.
For instance, an AI agent could be permitted to access customer personally identifiable information (PII) if its goal is to resolve a support ticket, but the same access would be denied if the agent’s task is model training. This method introduces a layer of semantic understanding into IAM, connecting permissions not just to actions, but to intended outcomes.
Human operators naturally bring context to their activities. When a payroll specialist accesses salary information, it is generally assumed to be part of their job responsibilities. AI agents, however, lack this inherent contextual awareness. They can combine operations in unexpected sequences, producing behaviors that administrators did not foresee and that traditional permissions might fail to properly restrict.
By constantly assessing purpose, intent-based controls allow organizations to grant access adaptively, only when actions correspond with approved business goals. This minimizes security blind spots, avoids both excessive and insufficient permissions, and fosters productivity without sacrificing safety.
In many respects, intent-based IAM extends the concepts of zero trust and least privilege into the AI era. It poses two questions: “Is this action permitted?” and “Is this action appropriate considering the current purpose, context, and risk level?”
This is not to suggest that action-based permissions are now outdated. Effective security relies on multiple, overlapping layers of control. Combining action-based guardrails with intent-based governance results in a system that is both protective and adaptive, offering comprehensive security coverage.
Transitioning to a hybrid IAM model that incorporates intent will necessarily occur in stages. A phased roadmap provides a practical path forward for organizations ready to make this shift.
(Source: HelpNet Security)
