Cerbos Synapse centralises authorization context for AI and human requests

At a glance

What this is: Cerbos Synapse is a context enrichment layer for authorization that centralises identity, resource, and relationship data before policy evaluation.

Why it matters: It matters because IAM, NHI, and emerging agentic access models all fail when policy engines receive incomplete or inconsistent context across services.

👉 Read Cerbos's announcement on Synapse for centralized authorization context

Context

Authorization quality depends on the completeness and consistency of the data that reaches the policy engine. In practice, many teams rebuild that context in application code, which creates drift, audit gaps, and brittle integrations whenever identity sources or resource systems change.

For IAM programmes, the problem is not only access control logic. It is the hidden plumbing that gathers attributes, joins them to the request, and keeps that process stable across human users, service-backed workflows, and AI-driven requests.

Key questions

Q: How should security teams centralise authorization context across applications?

A: Security teams should centralise attribute assembly in a governed layer that serves both applications and infrastructure enforcement points. That layer should resolve principal, resource, and relationship data once, expose a consistent input model to the policy engine, and log the sources used so reviewers can trace every decision.

Q: Why do AI agents complicate authorization decisions for IAM teams?

A: AI agents complicate authorization because they often do not carry a complete human identity context by default. Without enrichment, the policy engine sees an incomplete request and teams either over-permit access or hard-code exceptions. The better pattern is to evaluate the agent, the delegating human, and the resource together.

Q: What breaks when authorization data is rebuilt in every service?

A: When each service rebuilds authorization data independently, policy becomes dependent on local middleware, upstream API changes, and team-specific assumptions. That creates drift, inconsistent audits, and expensive rework whenever identity sources or resource systems change. The security loss is not just duplication, but loss of a shared control point.

Q: How can organisations reduce authorization drift between gateways and applications?

A: Organisations can reduce drift by forcing both gateway enforcement and application enforcement through the same context resolution and policy evaluation path. That way, perimeter checks and in-app decisions use the same attributes, the same freshness rules, and the same audit trail.

How it works in practice

Authorization context enrichment and policy inputs

Authorization context enrichment is the step that supplies the policy decision point with the attributes it needs to evaluate access. That usually includes principal data such as role, department, or delegating identity, plus resource data such as ownership, classification, or status. When every application assembles those inputs differently, policy becomes dependent on local middleware and per-team assumptions. A central enrichment layer makes the input model explicit and reusable, so the same request shape can be evaluated consistently across services and infrastructure.

Practical implication: move attribute assembly out of application code and into a governed enrichment layer with auditable source mappings.

Policy Information Point limits in distributed systems

A traditional Policy Information Point looks clean on paper because the policy engine can query external systems during evaluation. At scale, that model couples policy decisions to the latency and reliability of downstream systems, and it hides caching and fallback choices inside policy behaviour. The result is an authorization path that is difficult to reason about, difficult to test, and hard to keep consistent across teams. Separating data resolution from policy evaluation restores a clearer control boundary.

Practical implication: keep external lookups outside the evaluation path where reliability, freshness, and fallback can be controlled explicitly.

AI agent authorization context and delegated identity

AI agents break the assumption that the request context already contains a complete human identity. An agent may present only an agent ID or a partial reference to the user on whose behalf it is acting, which leaves the policy engine with an incomplete view of who initiated the action and what they should be allowed to do. Enrichment can combine the delegating human, the agent itself, and the target resource into one decision input. That matters because authorization for agents is not just about the tool call. It is about the delegation chain behind it.

Practical implication: treat delegated agent requests as enriched identity transactions, not as ordinary API calls with a single principal.

NHI Mgmt Group analysis

Authorization context is the hidden control plane of externalized access decisions. The article describes a common enterprise failure pattern: policy engines are asked to decide with incomplete or locally assembled data. That creates drift between applications, makes audits depend on code inspection, and turns every identity source change into a distributed rework exercise. The practitioner lesson is that context governance is now as important as policy authoring.

AI agent access exposes the weakness of request models built for human-paced identity. An agent often does not carry enough human identity context by default, which forces teams either to over-permit access or hard-code exceptions. That is a governance failure, not just an integration problem. The practitioner implication is that delegated agent identity must be treated as a first-class authorization subject, not an incidental field in the request.

Context resolution belongs in a governed layer, not inside application middleware. Rebuilding the same joins in every service creates operational debt, and operational debt becomes security debt when attribute freshness, fallback, and logging vary by team. The architecture described here moves those choices into a single control point. The practitioner implication is to standardise how principal and resource attributes are resolved before evaluation.

Least privilege degrades quickly when policy decisions depend on stale or partial enrichment. The article makes clear that policy changes should take effect on the next request, but that promise only works if enrichment is trustworthy and current. In identity governance terms, access is only as bounded as the context supplied at decision time. The practitioner implication is to measure authorization not just by policy coverage, but by context quality and source reliability.

Runtime authorization for agents needs a named concept: delegation-aware context resolution. This is the practice of binding the human behind the agent, the agent identity, and the resource context into one policy transaction. It matters because agentic access can look legitimate while still missing the attributes that make least privilege enforceable. The practitioner implication is to design for delegation chains, not isolated tokens or tool calls.

From our research:

• 97% of NHIs carry excessive privileges, increasing unauthorised access and broadening the attack surface, according to Ultimate Guide to NHIs.
• 91.6% of secrets remain valid five days after the targeted organisation is notified, showing a critical gap in remediation procedures.
• For a deeper baseline: Top 10 NHI Issues helps teams map where excessive privilege, visibility gaps, and lifecycle failures converge.

What this signals

Delegation-aware context resolution is likely to become a standard requirement as AI-mediated access expands, because policy engines can no longer rely on a single caller identity to explain intent, authority, and resource fit. Teams that keep attribute joins inside service code will struggle to prove consistency across human and non-human access paths.

The practical signal for IAM and security architects is that authorization observability now needs to include context quality, not just decision outcome. If the source of an attribute is opaque, stale, or team-specific, the programme is already carrying hidden policy drift even if the PDP itself is behaving correctly.

For practitioners

• Inventory every attribute source used in authorization decisions Document which systems provide principal, resource, and relationship data today, then mark where each service rebuilds the same joins in custom middleware. Treat duplicated context logic as a governance control point, not just a code pattern.
• Move context resolution out of application code Route authorization requests through a shared enrichment layer so changes to IdP mappings, resource metadata, or relationship lookups happen once and are visible to security and compliance reviewers.
• Model delegated agent requests separately from human requests Require the policy input to include the agent identity, the delegating human, and the resource in one transaction so access checks reflect the actual delegation chain instead of a partial caller ID.
• Test policy changes against context freshness and fallback behaviour Validate how authorization behaves when upstream identity or resource systems are slow, unavailable, or inconsistent, and define where cache use is acceptable versus where live lookups are mandatory.
• Align infrastructure and application enforcement on the same policy path Ensure gateways and services evaluate through the same enrichment and policy flow so perimeter checks and in-app decisions do not diverge over time.

Key takeaways

• Authorization fails quietly when policy engines receive incomplete or inconsistently assembled context, even if the policy logic itself is correct.
• AI agents intensify that problem because delegated requests often need both human and machine identity context to be evaluated safely.
• Centralised enrichment and shared policy paths are the architectural difference between repeatable governance and per-service access-control drift.

Key terms

• Authorization Context: Authorization context is the set of identity, resource, and relationship attributes a policy engine needs to decide access. In practice it is the evidence behind the decision, not the decision itself. If that evidence is incomplete, stale, or assembled differently by each service, policy consistency and auditability degrade quickly.
• Policy Information Point: A Policy Information Point is the component that supplies attributes to a policy decision point during evaluation. It can be clean in theory, but in real systems it often inherits latency, freshness, and reliability problems from the systems it queries. That makes it a major control surface for authorization governance.
• Delegation Chain: A delegation chain is the path from the original human actor through any service account, token, AI agent, or intermediary that acts on the request. For governance, the chain matters because the effective authority of the action may differ from the identity field visible at the endpoint. Strong authorization must account for that full chain.
• Context Resolution: Context resolution is the process of turning a sparse request, such as a user ID or agent ID, into a complete policy input. It joins the right attributes from the right systems at decision time. When it is fragmented across services, the organisation loses consistency, audit visibility, and control over freshness.

Deepen your knowledge

Authorization context enrichment and delegated agent identity are covered in the NHI Foundation Level course, the industry's only accredited NHI security programme. If you are standardising access decisions across services and AI-driven workflows, it is worth exploring.

This post draws on content published by Cerbos: Cerbos Synapse and centralized authorization context for policy decisions. Read the original.