Start with the identities that can already authenticate, then trace where those identities can assume roles, read secrets, access storage, or invoke services. The goal is to find valid paths from low privilege to high impact before an attacker does. Map the chain, not just the permission set, because the danger is usually in how controls combine.
Why This Matters for Security Teams
Cloud identity attack paths are rarely obvious from a single policy or role definition. The real risk sits in composition: an identity with read access to one service, permission to assume a second role, and access to a secret store can often become an administrative foothold without a single “admin” grant. NIST’s NIST SP 800-53 Rev 5 Security and Privacy Controls remains useful here because it ties identity, access enforcement, and monitoring to concrete control objectives.
Security teams often miss these paths when they review permissions in isolation or rely on manual approvals as proof of safety. That approach can overlook transitive trust, service-to-service access, and role chaining across accounts, subscriptions, or projects. Attackers look for exactly those joins because they turn routine access into privilege escalation, persistence, or data exposure. The same pattern now shows up in AI-assisted intrusion workflows, where operators rapidly enumerate and chain identity exposures, as discussed in Anthropic — first AI-orchestrated cyber espionage campaign report.
In practice, many security teams encounter the blast radius only after a stolen token, overbroad role, or exposed secret has already been used to move laterally.
How It Works in Practice
The practical method is to build an identity graph, then test it for reachable impact. Start with every principal that can already authenticate, including workforce accounts, workload identities, service principals, and API clients. From there, trace the edges that matter operationally: assume-role permissions, group membership, secret read access, federation trust, storage permissions, queue and message access, CI/CD deploy rights, and service invocation paths. The question is not “what can this identity do in theory?” but “what can it reach without additional human approval?”
A useful workflow is to rank paths by exploitability and impact:
- Can the identity obtain new credentials or tokens?
- Can it read secrets, certificates, or signing material?
- Can it modify code, infrastructure, or policy?
- Can it access logs, backups, or data stores that increase attacker visibility?
- Can it pivot into a higher-trust account, tenant, or environment?
For attack-pattern thinking, map these paths to behaviors in the MITRE ATT&CK Enterprise Matrix, especially account abuse, credential access, and privilege escalation techniques. That helps defenders connect cloud misconfiguration to detection logic and incident response playbooks. Where automation and AI are involved, security teams should also consider whether an attacker could use prompt injection, tool abuse, or model-guided enumeration to accelerate discovery of identity paths. In that case, the MITRE ATLAS adversarial AI threat matrix becomes relevant for understanding how AI-assisted workflows can support reconnaissance and abuse.
For prioritisation, focus first on paths that cross trust boundaries, such as from developer roles into production control planes, from workload identities into secret managers, or from read-only storage into configuration systems that can be modified indirectly. Then verify whether monitoring can actually see those transitions, because a path that is technically possible but invisible to logging is still a practical attacker path. These controls tend to break down when organisations span multiple cloud accounts and identity providers because trust relationships, token scopes, and logging standards are not normalised across the environment.
Common Variations and Edge Cases
Tighter path analysis often increases operational overhead, requiring organisations to balance visibility against speed of change. That tradeoff is real in cloud environments where ephemeral workloads, just-in-time access, and delegated administration change constantly. Current guidance suggests that path assessment should be continuous, but there is no universal standard for the exact graph model or scoring method yet.
Edge cases matter. In serverless and container-heavy environments, the attacker may not need a human account at all if a workload identity can reach secrets, metadata services, or deployment hooks. In development environments, broad access is sometimes tolerated for speed, but the risk is that test data, pipeline credentials, and production-like trust links become a shortcut into live systems. In regulated environments, teams should be especially careful when identity paths can reach customer data, financial controls, or security logging, because those paths create both compromise risk and compliance exposure.
Defenders should also treat AI-assisted operations as a force multiplier rather than a separate problem. The rise of autonomous tooling means that enumeration of cloud identity paths can happen faster and at greater scale, so manual review alone is rarely enough. Where possible, use continuous scanning, policy-as-code, and detective controls that alert on newly created trust paths, not just on known bad activity. That is the difference between reviewing a snapshot and defending a living identity system.
Related resources from NHI Mgmt Group
- How should security teams handle exposed cloud keys before attackers use them?
- How should security teams assess an identity verification provider before trusting it with onboarding flows?
- How should security teams assess identity risk before an acquisition closes?
- How should security teams map application attack paths in cloud environments?
