BEGINNER · 12 MIN · FREE LESSON
IAM users roles & policies
Explore iam users roles & policies through a visual, step-by-step model.
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BEGINNER · 12 MIN · FREE LESSON
Explore iam users roles & policies through a visual, step-by-step model.
Loading interactive lesson…
Flattened case-sensitive grant/deny statements, conditions, role trust and short-lived issued-session fixtures. Explicit matching deny overrides matching allow; otherwise default deny. A caller cannot claim a role principal without a trusted session bound to its original identity. MFA/source/environment flags represent trusted exercise context, never a pattern for accepting unverified client claims. AWS and GCP have additional distinct hierarchy/boundary evaluators not modeled here. AWS: IAM identity policies / role trust concepts. GCP: IAM role binding / service account concepts. Real AWS IAM and Google IAM evaluation is richer and structurally different: organizations, resource policies, boundaries, conditions and identity federation are outside this flattened exercise. Inputs are trusted lesson scenarios, never an app authorization pattern. No cloud resources are created.
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IAM identity policies / role trust concepts: Authentication identifies a caller; authorization decides whether its current identity and context may perform this action.
This is not full AWS IAM/SCP/resource/boundary evaluation or Google allow/deny/access-boundary hierarchy. Use native policy simulators and least-privilege review for a real deployment.
Policy statements checked, seed 42. Plotted sample sizes respect each lesson’s explicit input limits.
Keyboard: Space play/pause · ←/→ step · R restart · 1–5 speed. Reduced motion disables transitions.
Permission to assume a role is different from the permissions of its session.
Load the average scenario and step through the initial state before playing. Read the variables and final result, then switch to the worst scenario. Pause before a decision and predict which explicit condition determines the next action.
A bounded policy evaluator matches case-sensitive action/resource globs, then checks declared MFA, environment and source CIDR conditions. Explicit deny overrides matching allow; missing allow denies. Role trust issues an expiring session bound to the original principal. A caller cannot forge a reserved role identity.
AWS: IAM identity policies / role trust concepts. Google Cloud: IAM role binding / service account concepts. Select the provider mapping before loading input. Product names identify concepts; they do not create resources, imply identical APIs or promise equivalent service guarantees.
Compare an explicit deny with a broad allow, then expire a role session and retry. Change source CIDR and MFA independently rather than assuming a role name proves authentication. In Try mode, choose the next modeled decision and use the hint to inspect its preconditions. Explain a changed result using the actual input rather than the provider logo.
The player displays the model's operation metric and declared bounds. Compare best, average and worst presets before changing only one parameter. Simulation operation counts are different from provider quotas, cloud bills, throughput benchmarks and wall-clock service latency.
References show wildcard matching and deny-overrides evaluation after principal/session scoping. Trust and expiry are implemented in the player. All four languages show local reference logic rather than provider SDK calls. Validate inputs and account for the unmodeled production concerns before adapting a reference.
Use this scenario when reviewing IAM identity policies / role trust concepts or IAM role binding / service account concepts: identify the workload assumption, failure boundary and evidence needed for the selected design. Record one alternative and the constraint it changes.
Real AWS IAM and Google IAM evaluation is richer and structurally different: organizations, resource policies, boundaries, conditions and identity federation are outside this flattened exercise. Inputs are trusted lesson scenarios, never an app authorization pattern.
Answer the mechanism question, distinguish the exercise from a real cloud operation, and justify a controlled failure experiment. Re-run the scenario if your prediction disagrees with the traced state.
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