building-role-mining-for-rbac-optimization skill (Anthropic-Cybersecurity-Skills)

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What it does. Apply bottom-up and top-down role mining techniques, including clustering Part of mukul975/Anthropic-Cybersecurity-Skills (817 security skills) (mukul975/Anthropic-Cybersecurity-Skills).
Upstream mukul975/Anthropic-Cybersecurity-Skills
Skill file skills/building-role-mining-for-rbac-optimization/SKILL.md
License Apache-2.0 (skill folder LICENSE)
Author mukul975
Fetched 2026-09-10

Install

  • npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill building-role-mining-for-rbac-optimization, or copy the skill folder into ~/.claude/skills/building-role-mining-for-rbac-optimization/.
  • Raw file: curl -sL https://raw.githubusercontent.com/mukul975/Anthropic-Cybersecurity-Skills/HEAD/skills/building-role-mining-for-rbac-optimization/SKILL.md

SKILL.md (verbatim)

name: building-role-mining-for-rbac-optimization
description: Apply bottom-up and top-down role mining techniques, including clustering
  algorithms and formal concept analysis, to discover optimal RBAC roles from existing
  user-permission assignments, consolidating overlapping roles and enforcing least
  privilege. Use when an identity program needs to reduce role explosion or redesign
  its RBAC role set from access data.
domain: cybersecurity
subdomain: identity-access-management
tags:
- rbac
- role-mining
- identity-governance
- access-control
- least-privilege
- clustering
version: '1.0'
author: mahipal
license: Apache-2.0
nist_csf:
- PR.AA-01
- PR.AA-02
- PR.AA-05
- PR.AA-06
mitre_attack:
- T1078
- T1098
- T1069

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:

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

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,
            "user_count": len(cluster_users),
            "users": cluster_users.tolist(),
            "coverage": permission_frequency[permission_frequency >= 0.8].mean()
        }

    return roles, labels

Step 3: Formal Concept Analysis

def mine_roles_fca(upa_matrix, min_support=3):
    """Mine roles using Formal Concept Analysis (frequent closed itemsets)."""
    from itertools import combinations

    users = upa_matrix.index.tolist()
    permissions = upa_matrix.columns.tolist()

    concepts = []

    # Find all maximal permission sets shared by at least min_support users
    for size in range(len(permissions), 0, -1):
        for perm_combo in combinations(permissions, size):
            perm_set = set(perm_combo)
            # Find users who have ALL permissions in this set
            matching_users = []
            for user in users:
                user_perms = set(upa_matrix.columns[upa_matrix.loc[user] == 1])
                if perm_set.issubset(user_perms):
                    matching_users.append(user)

            if len(matching_users) >= min_support:
                # Check if this is a closed concept (no superset with same extent)
                is_closed = True
                for concept in concepts:
                    if set(matching_users) == set(concept["users"]) and \
                       perm_set.issubset(set(concept["permissions"])):
                        is_closed = False
                        break

                if is_closed:
                    concepts.append({
                        "permissions": list(perm_set),
                        "users": matching_users,
                        "support": len(matching_users)
                    })

        if len(concepts) > 100:  # Limit for performance
            break

    return concepts

Step 4: Evaluate and Select Roles

def evaluate_role_set(roles, upa_matrix):
    """Evaluate the quality of a mined role set."""
    total_assignments = upa_matrix.values.sum()
    covered_assignments = 0
    extra_assignments = 0

    for role_name, role_data in roles.items():
        role_perms = set(role_data["permissions"])
        for user in role_data["users"]:
            user_perms = set(upa_matrix.columns[upa_matrix.loc[user] == 1])
            covered = role_perms.intersection(user_perms)
            extra = role_perms - user_perms
            covered_assignments += len(covered)
            extra_assignments += len(extra)

    metrics = {
        "total_roles": len(roles),
        "total_assignments": total_assignments,
        "covered_assignments": covered_assignments,
        "coverage_rate": covered_assignments / total_assignments if total_assignments else 0,
        "extra_permissions": extra_assignments,
        "deviation_rate": extra_assignments / (covered_assignments + extra_assignments) if (covered_assignments + extra_assignments) else 0,
        "avg_role_size": np.mean([len(r["permissions"]) for r in roles.values()]),
        "avg_users_per_role": np.mean([r["user_count"] for r in roles.values()]),
    }
    return metrics

Step 5: Business Validation

After mining candidate roles:

  1. Map mined roles to business functions (department, job title)
  2. Conduct workshops with business unit managers to validate role definitions
  3. Identify outlier permissions that indicate misconfiguration
  4. Refine roles based on feedback and re-evaluate metrics
  5. Document role definitions with business justification

Validation Checklist

  • User-permission matrix extracted from all identity sources
  • Multiple mining algorithms compared (clustering, FCA)
  • Optimal role count determined via silhouette analysis or WSC
  • Coverage rate exceeds 95% of existing assignments
  • Deviation rate below 5% (minimal extra permissions)
  • Mined roles validated with business stakeholders
  • Role hierarchy defined (parent-child inheritance)
  • Exception/outlier permissions documented
  • Migration plan created for transitioning to new role model
  • Ongoing role governance process defined

References

Other files in this skill

assets/template.md (verbatim)

Role Mining Project Template

Project Overview

Field Value
Organization
Project Lead
Start Date
Target Completion
Identity Sources AD / Azure / AWS / Applications

Data Collection Summary

Source Users Permissions Assignments
Active Directory
AWS IAM
Azure AD
Applications
Total (Deduplicated)

Mining Results

Algorithm Roles Found Coverage Deviation WSC
Clustering (k=___)
Intersection Mining
Selected Approach

Proposed Role Definitions

Role Name Department Permissions Users Status
Draft/Validated/Approved

Stakeholder Validation

Business Unit Reviewer Roles Reviewed Approved Date
Yes/No

Migration Plan

  • Roles created in identity governance platform
  • User-role assignments configured
  • Individual permission grants removed
  • Test users validated access
  • Full migration completed
  • Post-migration access verification
  • Old permissions cleanup confirmed

references/api-reference.md (verbatim)

API Reference: Role Mining for RBAC Optimization

Input Format (CSV)

user,entitlement,system
john.doe,read_files,FileServer
john.doe,write_files,FileServer
jane.smith,read_files,FileServer

Role Mining Algorithms

Bottom-Up Mining

Finds exact permission sets shared by >= N users.

  • Input: user-permission matrix
  • Output: candidate roles with exact permission sets
  • Parameter: min_users (default: 2)

Top-Down Mining (Jaccard Clustering)

Groups users by permission similarity.

Jaccard(A, B) = |A ∩ B| / |A ∪ B|
  • Threshold >= 0.8: strict similarity
  • Threshold >= 0.6: moderate clustering

Optimization Metrics

Metric Description
Total Assignments Sum of all user-permission pairs
Candidate Roles Discovered role count
Role Coverage Users assigned to candidate roles
Avg Permissions/User Assignment density
Outlier Count Users with unique permissions

SailPoint IdentityNow Role Mining API

POST https://{tenant}.api.identitynow.com/beta/role-mining-sessions
Authorization: Bearer TOKEN
{
  "scope": {"included": {"identityIds": [...]}},
  "minEntitlementPopularity": 2,
  "pruneThreshold": 50
}

SailPoint Role Mining Status

GET /beta/role-mining-sessions/{sessionId}
GET /beta/role-mining-sessions/{sessionId}/potential-roles

CyberArk Identity Role Optimization

GET /Roles/GetRoleMembers?name={role}
POST /Roles/OptimizeRoles
{"minUsers": 3, "maxRoles": 50}

NIST RBAC Model Levels

Level Description
Core RBAC Users, roles, permissions, sessions
Hierarchical Role inheritance
Constrained Separation of duty (SoD)
Symmetric Permission-role review

references/standards.md (verbatim)

Role Mining for RBAC Optimization - Standards Reference

RBAC Standards

ANSI/INCITS 359-2012 - Core RBAC

  • Defines User, Role, Permission, Session abstractions
  • Role assignment: users are assigned to roles
  • Permission assignment: permissions are assigned to roles
  • Role hierarchy: senior roles inherit junior role permissions
  • Separation of Duty constraints (static and dynamic)

NIST RBAC Model (SP 800-162)

  • Core RBAC: Basic user-role and role-permission mappings
  • Hierarchical RBAC: Role inheritance relationships
  • Constrained RBAC: Static and dynamic separation of duties
  • Symmetric RBAC: Combined user-centric and permission-centric views

Identity Governance Standards

ISO 27001:2022 - A.5.15 Access Control

  • Access control policy based on business and security requirements
  • Roles determined by job function
  • Regular review of access rights
  • Formal authorization for privilege changes

NIST SP 800-53 Rev 5

  • AC-2: Account Management
  • AC-3: Access Enforcement
  • AC-5: Separation of Duties
  • AC-6: Least Privilege
  • AC-16: Security and Privacy Attributes
  • AC-24: Access Control Decisions

Role Mining Research

Key Algorithms

  • RoleMiner (Vaidya et al., 2007): Iterative role mining minimizing WSC
  • CompleteMiner / FastMiner (Vaidya et al., 2006): Complete vs. approximate algorithms
  • ORCA (Schlegelmilch & Steffens, 2005): Clustering-based approach
  • Graph Optimization (Lu et al., 2008): Graph-based role mining

Quality Metrics

  • Weighted Structural Complexity: min(|UA| + |PA| + |Roles|)
  • Boolean Matrix Decomposition error
  • Jaccard similarity between mined and original access
  • Role coverage percentage

references/workflows.md (verbatim)

Role Mining for RBAC Optimization - Workflows

End-to-End Role Mining Workflow

Phase 1: DATA COLLECTION (Week 1-2)
    ├── Export user-permission data from all identity sources
    │   ├── Active Directory group memberships
    │   ├── Cloud IAM role assignments
    │   ├── Application-level permissions
    │   └── Database access grants
    ├── Collect HR data (job titles, departments, cost centers)
    ├── Normalize data into User-Permission Assignment (UPA) matrix
    └── Clean data: remove disabled accounts, system accounts

Phase 2: ANALYSIS (Week 3-4)
    ├── Run clustering algorithms (hierarchical, k-means)
    ├── Run Formal Concept Analysis for exact role candidates
    ├── Compare results using WSC and coverage metrics
    ├── Identify optimal number of roles via silhouette analysis
    └── Map candidate roles to organizational structure

Phase 3: VALIDATION (Week 5-6)
    ├── Present candidate roles to business unit managers
    ├── Validate each role against job descriptions
    ├── Identify and resolve outlier permissions
    ├── Define role hierarchy (inheritance relationships)
    └── Agree on role names and descriptions

Phase 4: IMPLEMENTATION (Week 7-8)
    ├── Create roles in identity governance platform
    ├── Assign users to validated roles
    ├── Remove individual permission assignments
    ├── Test access for sample users in each role
    └── Document role definitions and approval chain

Phase 5: GOVERNANCE (Ongoing)
    ├── Monitor for permission drift
    ├── Quarterly role effectiveness review
    ├── Re-run mining annually to detect new patterns
    └── Track role count and WSC metrics over time

Data Normalization Workflow

Raw Data Sources
    │
    ├── AD: user → group → permissions
    │       Normalize to: user_id, permission_id
    │
    ├── AWS: user/role → policy → actions
    │       Normalize to: user_id, permission_id
    │
    ├── Azure: user → role → permissions
    │       Normalize to: user_id, permission_id
    │
    └── Applications: user → app_role → features
            Normalize to: user_id, permission_id

Merge all sources → Deduplicate → Create UPA matrix

Role Consolidation Workflow

Mining produces N candidate roles
    │
    ├── Remove roles with < 3 users (outliers)
    │
    ├── Merge roles with > 90% Jaccard similarity
    │
    ├── Identify hierarchical relationships:
    │   └── If Role A permissions ⊂ Role B permissions
    │       → Role A is junior to Role B
    │
    ├── Check for SoD violations:
    │   └── Does any role combine conflicting permissions?
    │       → Split into separate roles if needed
    │
    └── Final role set with hierarchy and constraints

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