AI Access Sentinel

Identity Threat Detection and Response (ITDR) Platform

ML-powered ITDR platform combining UEBA, anomaly detection, and CrowdStrike Falcon threat intelligence for Zero Trust environments

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The Problem

Identity-Based Attacks Are #1 Threat Vector According to industry research: 80% of breaches involve compromised credentials (Verizon DBIR 2024) 99% of organizations experienced identity-based attacks (CrowdStrike 2024) $4.5M average cost of a data breach (IBM 2024) Traditional IAM systems are reactive - they enforce policies but cannot: Detect credential compromise in real-time Identify lateral movement patterns Predict privilege escalation attempts Correlate identity threats across systems

What Security Teams Need Modern SOC teams require: Real-time threat detection for identity attacks Behavioral analytics to establish baselines ML-powered anomaly detection at scale Threat intelligence integration (CrowdStrike, etc.) Automated response capabilities Risk-based access decisions This is ITDR - Identity Threat Detection and Response.

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The Solution: AI Access Sentinel

AI Access Sentinel combines Machine Learning, UEBA (User Entity Behavior Analytics), and CrowdStrike Falcon ITDR to provide:

Capability	Technology	Outcome
Anomaly Detection	Isolation Forest, LSTM, Transformer	Detect unusual access patterns in real-time
Risk Scoring	6-Factor ML Model	Quantify user risk from 0-100
Threat Intelligence	CrowdStrike Falcon ITDR	Correlate with global threat data
Role Mining	K-Means Clustering	Discover actual vs assigned permissions
Access Prediction	Random Forest	Recommend approve/deny decisions

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Screenshots

Dashboard Views

Anomaly Detection Real-time threat identification

Risk Scoring 6-factor risk assessment

Role Mining ML-discovered access patterns

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Why CrowdStrike Falcon ITDR? (v1.1)

The Integration Rationale CrowdStrike Falcon ITDR was chosen for v1.1 because: Industry Leader - Gartner Magic Quadrant leader in endpoint/identity protection Real-time Threat Intel - Global visibility into identity attacks MITRE ATT&CK Mapping - Native technique/tactic classification FalconPy SDK - Official Python SDK for seamless integration Enterprise Adoption - Used by Fortune 500 companies globally Skills Demonstrated API integration with enterprise security tools Webhook processing and event correlation OAuth2 authentication flows Real-time threat intelligence processing

Before vs After Metric v1.0 v1.1 Risk Factors 5 6 (+Falcon) Threat Sources ML only ML + Falcon Attack Types Anomalies + 13 ITDR types Response Time Minutes Seconds Supported Attack Types Credential Theft (T1003) Lateral Movement (T1021) Privilege Escalation (T1078) Brute Force (T1110) Golden/Silver Ticket Kerberoasting DCSync

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Architecture

                                    DATA SOURCES
    ┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
    │   IAM Logs      │    │  Auth Events    │    │  CrowdStrike    │
    │  (10K+ events)  │    │  (MFA, SSO)     │    │  Falcon ITDR    │
    └────────┬────────┘    └────────┬────────┘    └────────┬────────┘
             │                      │                      │
             └──────────────────────┼──────────────────────┘
                                    │
                                    ▼
    ┌─────────────────────────────────────────────────────────────────┐
    │                    PROCESSING PIPELINE                          │
    │  ┌─────────────┐  ┌─────────────┐  ┌─────────────────────────┐ │
    │  │  Ingestion  │─▶│ Preprocess  │─▶│  Feature Engineering    │ │
    │  │             │  │             │  │  + Falcon Event Parser  │ │
    │  └─────────────┘  └─────────────┘  └─────────────────────────┘ │
    └─────────────────────────────┬───────────────────────────────────┘
                                  │
                                  ▼
    ┌─────────────────────────────────────────────────────────────────┐
    │                    ML + THREAT INTEL SUITE                      │
    │                                                                 │
    │  ┌──────────────┐  ┌──────────────┐  ┌──────────────────────┐  │
    │  │   Anomaly    │  │   Access     │  │  Alert Correlator    │  │
    │  │  Detection   │  │  Prediction  │  │  (Falcon + ML)       │  │
    │  │ ────────────│  │ ────────────│  │ ────────────────────│  │
    │  │ Iso. Forest  │  │ Rand. Forest │  │ Confidence Scoring   │  │
    │  │ LSTM         │  │ 92% Accuracy │  │ MITRE Mapping        │  │
    │  │ Transformer  │  │              │  │                      │  │
    │  └──────────────┘  └──────────────┘  └──────────────────────┘  │
    │                                                                 │
    │  ┌──────────────┐  ┌──────────────┐  ┌──────────────────────┐  │
    │  │ Role Mining  │  │ Risk Scorer  │  │  Falcon Connector    │  │
    │  │ ────────────│  │ ────────────│  │ ────────────────────│  │
    │  │ K-Means      │  │ 6-Factor     │  │ FalconPy SDK         │  │
    │  │ Hierarchical │  │ 0-100 Scale  │  │ OAuth2 + Webhooks    │  │
    │  └──────────────┘  └──────────────┘  └──────────────────────┘  │
    └─────────────────────────────┬───────────────────────────────────┘
                                  │
            ┌─────────────────────┼─────────────────────┐
            ▼                     ▼                     ▼
    ┌──────────────┐      ┌──────────────┐      ┌──────────────┐
    │   FastAPI    │      │  Streamlit   │      │   React      │
    │   REST API   │      │  Dashboard   │      │   Frontend   │
    │              │      │              │      │              │
    │ 15+ Endpoints│      │ Real-time    │      │ Modern UI    │
    │ Falcon ITDR  │      │ Monitoring   │      │ Dark Theme   │
    └──────────────┘      └──────────────┘      └──────────────┘

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6-Factor Risk Scoring Model

How It Works The risk scorer combines 6 weighted factors: FACTOR_WEIGHTS = { 'anomaly_score': 22.5%, # ML detection 'peer_deviation': 15.0%, # vs similar users 'sensitive_access': 15.0%, # high-value resources 'failed_attempts': 11.25%, # auth failures 'policy_violations': 11.25%, # rule breaches 'falcon_threat': 25.0%, # CrowdStrike intel } Risk Levels Score Level Action 0-30 LOW Monitor 31-60 MEDIUM Review 61-85 HIGH Investigate 86-100 CRITICAL Block + Alert

API Response Structure { "user_id": "string", "risk_score": "number (0-100)", "risk_level": "LOW | MEDIUM | HIGH | CRITICAL", "factor_scores": { "anomaly_score": "number", "peer_deviation": "number", "sensitive_access": "number", "failed_attempts": "number", "policy_violations": "number", "falcon_threat": "number" }, "falcon_context": { "active_alerts": "number", "alert_types": ["array of alert types"], "max_severity": "string" }, "recommendations": ["array of action items"] }

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Quick Start

Prerequisites

• Python 3.9+
• pip package manager

Installation

# Clone repository
git clone https://github.com/MikeDominic92/ai-access-sentinel.git
cd ai-access-sentinel

# Create virtual environment
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Configure environment
cp .env.example .env

Run the Platform

# Start API Server
uvicorn src.api.main:app --reload --port 8000

# Start Dashboard (new terminal)
streamlit run dashboard/app.py

# Start Frontend (new terminal)
cd frontend && npm install && npm run dev

Access Points

• API Docs: http://localhost:8000/docs
• Dashboard: http://localhost:8501
• Frontend: http://localhost:3000

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API Endpoints

Core Endpoints

Method	Endpoint	Description
POST	/api/v1/analyze/access	Analyze single access event
POST	/api/v1/analyze/batch	Batch analysis
GET	/api/v1/user/{id}/risk-score	Get user risk score
POST	/api/v1/roles/discover	ML role mining
GET	/api/v1/model/metrics	Model performance

Falcon ITDR Endpoints (v1.1)

Method	Endpoint	Description
POST	/api/v1/falcon/webhook	Receive Falcon alerts
GET	/api/v1/falcon/status	Connection status
GET	/api/v1/falcon/user/{id}/risk	Falcon-enriched risk
POST	/api/v1/falcon/sync	Manual alert sync
GET	/api/v1/falcon/correlations	View correlations

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Use Cases

1. Credential Compromise Detection Scenario: Attacker steals employee credentials via phishing. Detection: Impossible travel (NY to Russia in 10 min) Unusual resource access pattern Off-hours authentication Response: Block access, force MFA, alert SOC. Outcome: Attack stopped before lateral movement.	2. Insider Threat Detection Scenario: Employee downloads sensitive files before resignation. Detection: Volume spike in data access Off-hours activity increase Access to unusual resources Response: Alert security, review access logs. Outcome: Data exfiltration prevented.
3. Privilege Escalation Prevention Scenario: Compromised account requests admin access. Detection: 0% of peer group has this access Escalation pattern matches known attacks Falcon alert: T1078 Response: Deny request, flag account. Outcome: Lateral movement blocked.	4. Role Optimization Scenario: 200+ custom roles with overlap. Discovery: ML clusters users into 15 natural groups Identifies 85 ghost permissions Finds 30 over-privileged accounts Action: Consolidate roles, reduce attack surface. Outcome: 85% complexity reduction.

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Project Structure

ai-access-sentinel/
├── src/
│   ├── api/                 # FastAPI REST endpoints
│   │   ├── main.py          # Application entry point
│   │   └── schemas.py       # Pydantic models + Falcon schemas
│   ├── models/              # ML models
│   │   ├── anomaly_detector.py
│   │   ├── access_predictor.py
│   │   ├── role_miner.py
│   │   └── risk_scorer.py   # 6-factor model
│   ├── integrations/        # v1.1: External integrations
│   │   ├── crowdstrike_connector.py
│   │   ├── falcon_event_parser.py
│   │   └── alert_correlator.py
│   └── data/                # Data generators
├── dashboard/               # Streamlit dashboard
├── frontend/                # React/Next.js UI
├── notebooks/               # Jupyter experiments
├── tests/                   # Test suite (96% coverage)
└── docs/                    # Documentation

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Skills Demonstrated

This project demonstrates proficiency in:

Category	Technologies
Machine Learning	scikit-learn, TensorFlow, LSTM, Transformer, Isolation Forest
Backend Development	Python, FastAPI, Pydantic, async/await
API Integration	CrowdStrike FalconPy SDK, OAuth2, Webhooks
Data Engineering	pandas, numpy, feature engineering
Security	ITDR, UEBA, MITRE ATT&CK, Zero Trust
Frontend	React, Next.js, Streamlit
DevOps	Docker, pytest, CI/CD

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Roadmap

• v1.0: Core ML models (Anomaly, Prediction, Clustering)
• v1.1: CrowdStrike Falcon ITDR integration
• v2.0: Real-time streaming (Apache Kafka)
• v2.1: SIEM integration (Splunk, Sentinel)
• v3.0: SOAR automated remediation

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Author

Mike Dominic

• GitHub: @MikeDominic92
• Focus: IAM Governance, Role Attestation, Access Analytics

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Chainguard IT Engineer (Identity/IAM) Alignment

This project demonstrates key competencies for senior IAM engineering roles:

Requirement	Evidence
Role attestation processes	Role attestation engine in src/attestation/
Segregation of duties assessments	SoD conflict detection and review campaigns
IAM governance and RBAC	Role mining and permission analysis
Access review automation	Campaign management and decision tracking
Compliance reporting	SOC 2/ISO 27001/NIST evidence generation

See also: src/attestation/README.md for role attestation documentation.

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AI-Powered Identity Governance with Role Attestation
_{Demonstrates Role Attestation, SoD Detection, Access Analytics, and Compliance Automation}