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02/09/2026 1:29 pm
TL;DR: Enterprises should modernize authorization in phases, starting with centralized policy infrastructure, then mapping current policy sprawl, delivering API and token quick wins, extending into fine-grained data control, and finally governing agentic AI at runtime, according to PlainID. The operational lesson is that authorization has become a cross-domain control plane, and static, application-bound policies no longer scale.
NHIMG editorial — based on content published by PlainID: How to Get Started with Authorization Strategy
Questions worth separating out
A: Start with the policy infrastructure, then build an authorization inventory, then target high-value quick wins such as API and token controls.
Q: Why do fragmented authorization policies create more risk in API, data, and AI environments?
A: Fragmented policies create risk because each system applies access decisions differently, so governance becomes inconsistent and difficult to audit.
Q: What breaks when authorization rules stay embedded in code?
A: Governance breaks first, because access logic becomes scattered across services and harder to review consistently.
Practitioner guidance
- Map policy ownership across PAP, PDP, and PEP layers Identify where policy is authored, where decisions are made, and where enforcement happens in each major application and service path.
- Build an authorization inventory by identity type and decision point Catalog human users, service accounts, API consumers, and AI workflows separately, then document which systems they reach, what decisions they trigger, and where sensitive data is involved.
- Prioritise quick wins in token and API authorization Target high-volume API paths and session token flows first because they deliver visible governance gains without forcing a full application rewrite.
What's in the full article
PlainID's full article covers the operational detail this post intentionally leaves for the source:
- Step-by-step implementation guidance for centralizing authorization infrastructure across existing systems.
- Concrete examples of token enrichment and API access control patterns in modern architectures.
- Practical detail on extending authorization into fine-grained data access and agentic AI workflows.
- A phased adoption sequence that helps teams decide where to start and what to modernize next.
👉 Read PlainID's authorization strategy guide for APIs, data, and AI workflows →
Authorization strategy for APIs, data, and AI workflows: what changes now?
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02/09/2026 1:30 pm
Authorization sprawl is now an identity governance problem, not just an application design issue. When policy logic lives inside code, every new API, data platform, or AI workflow creates another control variant. That pattern fragments auditability and makes consistent governance impossible at scale. The field should treat authorization as part of the identity plane, not as a local developer decision.
A few things that frame the scale:
- 44% of developers are reported to follow security best practices for secrets management, according to The State of Secrets in AppSec.
- 63% of organisations say secrets sprawl is growing faster than their ability to govern it, according to The State of Secrets in AppSec.
A question worth separating out:
Q: What should security teams do when AI agents need access to tools and data?
A: Security teams should treat AI agents as runtime access actors and separate them from static machine identities. Limit tool scope, define approval gates, and require explicit revocation triggers for sessions and delegated access. The goal is to prevent broad runtime behaviour from inheriting static privileges.
👉 Read our full editorial: Authorization strategy for AI and API sprawl needs a phased model
