building-role-mining-for-rbac-optimization

Files (8)

• LICENSE
• SKILL.md
• template.md · assets/
• api-reference.md · references/
• standards.md · references/
• workflows.md · references/
• agent.py · scripts/
• process.py · scripts/

SKILL.md (first lines)

# Building Role Mining for RBAC Optimization

## Overview

Role mining is the process of analyzing existing user-permission assignments to discover optimal roles for a Role-Based Access Control (RBAC) system. Organizations accumulate excessive permissions over time through job changes, project assignments, and ad-hoc access grants, leading to "role explosion" where thousands of granular roles exist with significant overlap. Role mining uses data analysis -- including clustering algorithms, formal concept analysis, and graph-based methods -- to consolidate permissions into a minimal set of roles that accurately represent business functions while enforcing least privilege.


## When to Use

- When deploying or configuring building role mining for rbac optimization capabilities in your environment
- When establishing security controls aligned to compliance requirements
- When building or improving security architecture for this domain
- When conducting security assessments that require this implementation

## Prerequisites

- Export of current user-permission assignments (CSV/database)
- Identity governance platform or directory service access
- Python 3.9+ with pandas, scikit-learn, numpy
- Understanding of organizational structure and job functions
- Stakeholder access for role validation workshops

## Core Concepts

### Role Mining Approaches

| Approach | Description | Best For |
|----------|-------------|----------|
| Bottom-Up | Analyze existing permissions to discover common patterns | Large datasets with organic permission growth |
| Top-Down | Design roles from business requirements and job descriptions | Greenfield RBAC or organizational restructuring |
| Hybrid | Combine bottom-up analysis with top-down business validation | Most production environments |

### Role Mining Algorithms

**1. Permission Clustering**: Group users with similar permission sets using k-means or hierarchical clustering. Users in the same cluster share a common role.

**2. Formal Concept Analysis (FCA)**: Mathematical framework that identifies complete set of concepts (user groups sharing exact permission sets) from a binary user-permission matrix.

**3. Graph-Based Mining**: Model users and permissions as a bipartite graph, then find dense subgraphs representing candidate roles.

**4. Boolean Matrix Decomposition**: Decompose the user-permission matrix U into U ≈ R × P where R maps users to roles and P maps roles to permissions.

### Role Mining Metrics

| Metric | Formula | Target |
|--------|---------|--------|
| Role Count | Total distinct roles after mining | Minimize |
| Coverage | Permissions explained by mined roles / Total permissions | > 95% |
| Weighted Structural Complexity (WSC) | Sum of role-user + role-permission assignments | Minimize |
| Deviation | Extra permissions not covered by assigned roles | < 5% |

## Workflow

### Step 1: Extract User-Permission Data

Collect the current access state from all identity sources:

```python
import pandas as pd
import numpy as np

# Load user-permission assignments
# Format: user_id, permission_id (one row per assignment)
assignments = pd.read_csv("user_permissions.csv")

# Create binary user-permission matrix (UPA matrix)
upa_matrix = assignments.pivot_table(
    index="user_id",
    columns="permission_id",
    aggfunc="size",
    fill_value=0
)
upa_matrix = (upa_matrix > 0).astype(int)

print(f"Users: {upa_matrix.shape[0]}")
print(f"Permissions: {upa_matrix.shape[1]}")
print(f"Assignments: {assignments.shape[0]}")
print(f"Density: {upa_matrix.values.sum() / upa_matrix.size:.2%}")
```

### Step 2: Bottom-Up Role Discovery Using Clustering

```python
from sklearn.cluster import AgglomerativeClustering
from sklearn.metrics import silhouette_score

def find_optimal_clusters(matrix, max_k=50):
    """Find optimal number of roles using silhouette analysis."""
    scores = []
    for k in range(2, min(max_k, matrix.shape[0])):
        clustering = AgglomerativeClustering(
            n_clusters=k, metric="jaccard", linkage="average"
        )
        labels = clustering.fit_predict(matrix)
        score = silhouette_score(matrix, labels, metric="jaccard")
        scores.append((k, score))

    optimal_k = max(scores, key=lambda x: x[1])[0]
    return optimal_k, scores

def mine_roles_clustering(upa_matrix, n_clusters):
    """Mine roles using hierarchical clustering on Jaccard distance."""
    clustering = AgglomerativeClustering(
        n_clusters=n_clusters, metric="jaccard", linkage="average"
    )
    user_matrix = upa_matrix.values
    labels = clustering.fit_predict(user_matrix)

    roles = {}
    for cluster_id in range(n_clusters):
        cluster_users = upa_matrix.index[labels == cluster_id]
        cluster_permissions = upa_matrix.loc[cluster_users]

        # Core role = permissions held by >80% of cluster members
        permission_frequency = cluster_permissions.mean()
        core_permissions = permission_frequency[permission_frequency >= 0.8].index.tolist()

        roles[f"Role_{cluster_id}"] = {
            "permissions": core_permissions,

From mukul975/Anthropic-Cybersecurity-Skills, Apache-2.0.

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