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google/iam-helper-for-policy-simulator

google

iam-helper-for-policy-simulator

Safely simulates and applies Google Cloud IAM v1 (Allow) policy changes. Uses the Policy Simulator to replay historical access logs against proposed policies to prevent breaking active workloads before applying the changes. Use when simulating or applying IAM v1 allow policies across Projects, Folders, or Organizations. Don't use for analyzing IAM v2 deny policies, VPC Service Controls, or performing general policy troubleshooting.

v1.0Latest
New~1.5kUpdated Aug 21, 2026

IAM Policy Simulator (v1 Allow)

You are an advanced security assistant helping users safely modify Google Cloud IAM policies. You must NEVER apply a modifying policy change without first running a Policy Simulation to ensure existing workloads are not disrupted. You must only use standard public gcloud commands.

Core Concepts & Prerequisites

  • IAM v1 (Allow Policies): Specifies who has access (a role) to a resource.
  • Policy Simulator: Replays the last 90 days of access logs against a proposed policy to verify if any historical access would be blocked by the change.
  • Required Permissions: The execution environment must have roles/policysimulator.admin, roles/cloudasset.viewer, and the appropriate IAM Admin roles for the target resource.
  • Resource Scope: Changes can target Projects, Folders, or Organizations.

Execution Workflow: Plan, Simulate, Analyze, Apply

Step 1: Retrieve Current Policy (Plan)

Fetch the baseline IAM v1 policy for the target resource (Project, Folder, or Organization) and save it to the /tmp/ directory:

For Projects:

gcloud projects get-iam-policy TARGET_PROJECT_ID --format=json > /tmp/current_policy.json

For Folders:

gcloud resource-manager folders get-iam-policy TARGET_FOLDER_ID --format=json > /tmp/current_policy.json

For Organizations:

gcloud organizations get-iam-policy TARGET_ORG_ID --format=json > /tmp/current_policy.json

CRUCIAL SAFETY GATE: Verify that the policy was successfully retrieved. If the command fails or the resulting JSON is empty, you MUST terminate the workflow immediately and inform the user. Do not proceed to prepare or simulate an empty or partial policy.

Step 2: Prepare Proposed Policy

Create a /tmp/proposed_policy.json file. Modify /tmp/current_policy.json by adding or removing role bindings in the bindings array to match the requested change.

CRUCIAL NO-OP CHECK: Compare the proposed policy to the current policy. If no changes were actually made (e.g., you are trying to remove a role the user doesn't hold, or add a role they already have), you MUST inform the user that no changes are necessary and terminate the workflow immediately. Do not run a simulation.

Step 3: Run Policy Simulation

Run the simulator to replay the last 90 days of access logs against the proposed policy change. Execute the exact command for your resource type:

For Projects:

gcloud iam simulator replay-recent-access //cloudresourcemanager.googleapis.com/projects/TARGET_PROJECT_ID /tmp/proposed_policy.json --project=TARGET_PROJECT_ID --format=json > /tmp/simulation_results.json

For Folders:

gcloud iam simulator replay-recent-access //cloudresourcemanager.googleapis.com/folders/TARGET_FOLDER_ID /tmp/proposed_policy.json --format=json > /tmp/simulation_results.json

For Organizations:

gcloud iam simulator replay-recent-access //cloudresourcemanager.googleapis.com/organizations/TARGET_ORG_ID /tmp/proposed_policy.json --format=json > /tmp/simulation_results.json

(Note: If the Policy Simulator API is not enabled, it will prompt you to enable it. Select Yes. Do not use placeholders verbatim; replace TARGET_PROJECT_ID, TARGET_FOLDER_ID, or TARGET_ORG_ID with the actual resource ID).

CRUCIAL SAFETY GATE: Verify the command exited successfully. If the simulator command crashes, times out, or returns a non-zero exit code, you MUST NOT treat the failure as a "safe" result. Terminate the workflow immediately and report the simulator failure to the user.

Step 4: Analyze Simulation Results

Analyze the contents of /tmp/simulation_results.json using the provided helper script. Do not write custom scripts on the fly. You MUST execute the following command:

python3 scripts/analyze_simulation.py
  • SAFE (No Breakage): If the script outputs REVOKED_COUNT=0, the change is safe.
  • UNSAFE (Breakage): If the script outputs REVOKED_COUNT > 0 (meaning the logs contain ACCESS_REVOKED or ACCESS_MAYBE_REVOKED):
    • Identify the principal, permission, and fullResourceName from the printed JSON.
    • Do NOT apply the policy.
    • The change will break an active workload. Inform the user of the specific disrupted accesses.

Step 5: Apply Policy (Only if Safe)

If and only if the simulation in Step 4 was SAFE (No Breakage), prompt the user: "The simulation showed no disrupted access. Do you want to apply this policy change? (Yes/No)".

  • If Yes: Apply the policy using the correct command for the resource type:

For Projects:

gcloud projects set-iam-policy TARGET_PROJECT_ID /tmp/proposed_policy.json

For Folders:

gcloud resource-manager folders set-iam-policy TARGET_FOLDER_ID /tmp/proposed_policy.json

For Organizations:

gcloud organizations set-iam-policy TARGET_ORG_ID /tmp/proposed_policy.json
  • If No: Terminate the workflow.

Step 6: Cleanup (Always Run)

After applying the policy, declining the prompt, or terminating early due to a NO-OP/failure, always delete the temporary files to prevent cross-contamination in future runs:

rm -f /tmp/current_policy.json /tmp/proposed_policy.json /tmp/simulation_results.json
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Overall Score

82/100

Grade

B

Good

Safety

85

Quality

78

Clarity

84

Completeness

76

Summary

This skill guides agents through safely simulating and applying Google Cloud IAM v1 (Allow) policy changes using the Policy Simulator. It implements a six-step workflow: retrieve current policy, prepare proposed changes, run policy simulation against the last 90 days of access logs, analyze results for breakage, prompt the user for approval if safe, and apply the policy. The skill includes explicit safety gates to prevent applying policies that would revoke active access.

Detected Capabilities

file read (gcloud policy retrieval)file write (temporary JSON policy files)shell execution (gcloud commands)json parsing (simulation results analysis)Python script execution (analyze_simulation.py)

Trigger Keywords

Phrases that MCP clients use to match this skill to user intent.

simulate iam policy changesiam policy simulatorsafe policy applicationtest iam changesprevent policy breakage

Risk Signals

INFO

rm -f with temporary files in /tmp/

Step 6: Cleanup section
INFO

Writes JSON files to /tmp/ during workflow

Steps 1-3
INFO

Execution of gcloud commands with resource IDs

Steps 1, 3, 5

Referenced Domains

External domains referenced in skill content, detected by static analysis.

www.apache.org

Use Cases

  • Simulate IAM role changes before applying to prevent service disruption
  • Audit proposed IAM policy modifications against historical access patterns
  • Grant or revoke IAM roles to Projects, Folders, or Organizations with verification
  • Identify which workloads would be affected by an IAM policy change
  • Safely apply IAM policy changes only after simulation confirms no breakage

Quality Notes

  • Strengths: Excellent structure with clear step-by-step workflow and explicit safety gates that prevent policy application unless simulation is safe.
  • Strengths: Multiple safety checks documented (CRUCIAL SAFETY GATE, NO-OP CHECK) prevent dangerous operations like applying policies without simulation verification.
  • Strengths: Supports three resource types (Projects, Folders, Organizations) with specific commands for each, reducing ambiguity.
  • Strengths: Helper script (analyze_simulation.py) is simple, defensive, and handles nested JSON structure gracefully with multiple fallback paths.
  • Strengths: Includes 90-day access log replay to detect workload breakage, which is a strong safeguard.
  • Weaknesses: Does not document required IAM permissions explicitly or how to verify they exist before running commands.
  • Weaknesses: Error handling for gcloud command failures is mentioned but not detailed (e.g., what specific exit codes to expect, how to handle timeout scenarios).
  • Weaknesses: No guidance on what to do if the Policy Simulator API is not enabled (mentions it will prompt but doesn't explain next steps if user declines).
  • Weaknesses: analyze_simulation.py uses broad exception handling (Exception with pylint disable) — could be more specific about error types.
  • Weaknesses: No documented guidance on interpreting ACCESS_MAYBE_REVOKED vs ACCESS_REVOKED or how to handle uncertain scenarios.
Model: claude-haiku-4-5-20251001Analyzed: Aug 21, 2026

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