AI agent authorization and ReBAC: where policy engines fall short

Policy engines are a poor fit when authorization depends on changing relationship state. The article shows that fast evaluation does not solve stale inputs, and stale inputs are what break the decision. This is not just a tooling preference, it is a governance boundary: fixed policies assume access can be pre-decided, while AI agent authorization increasingly depends on what the agent is connected to right now. Practitioners should treat static policy as insufficient whenever ambient context drives the access decision.

A few things that frame the scale:

• Broken Access Control topped the OWASP Top 10 list in 2025 (and 2021) and this problem will be exacerbated as AI systems evolve, according to The State of Secrets in AppSec.
• Only 44% of developers are reported to follow security best practices for secrets management, exposing a significant developer behaviour gap.

A question worth separating out:

Q: How do ReBAC and IAM help with AI agent authorization?

A: ReBAC helps IAM by expressing access as relationships between identities and resources, which is a better fit for agents that are shared across documents, teams, and tools. It gives practitioners a way to evaluate current state instead of rewriting rules for every access path. That makes it easier to govern dynamic agent access without losing contextual accuracy.

👉 Read our full editorial: AI agent authorization exposes the limits of policy engines