AI-Accelerated Zero Trust Architecture Platform
ZeroTrust-AI brings machine learning to zero trust architecture — replacing static, rule-based security with continuous behavioral analytics, adaptive access control, and intelligent microsegmentation.
The zero trust security market is projected to reach $22.6 billion by 2027. The urgency is clear:
- 84% of organizations experienced identity-related breaches in the past year
- $4.45M average cost of a data breach (IBM, 2023)
- 277 days average time to identify and contain a breach
- 68% of breaches involved a human element (Verizon DBIR)
Traditional perimeter security assumes internal traffic is trusted. It is not. Static access policies cannot adapt to the reality of compromised credentials, insider threats, and lateral movement.
Static policies fail because threats are dynamic. ZeroTrust-AI uses behavioral analytics and graph neural networks to:
| Traditional Zero Trust | ZeroTrust-AI |
|---|---|
| Binary allow/deny rules | Continuous risk-scored decisions |
| Authentication at login only | Continuous verification throughout session |
| Manual microsegmentation | AI-discovered communication clusters |
| Signature-based detection | Behavioral anomaly detection |
| Static privilege assignment | Dynamic, context-aware access |
| Manual policy tuning | Learned baselines + automated recommendations |
ZeroTrust-AI implements all five pillars of zero trust:
- Never trust, always verify — Every access request is evaluated against behavioral baselines, device health, network context, and threat intelligence
- Least privilege access — The policy engine recommends minimum permissions based on observed access patterns
- Assume breach — Lateral movement detection uses GNN analysis to find credential hopping, privilege escalation paths, and anomalous graph traversals
- Microsegmentation — Flow analysis automatically discovers workload clusters and generates segment policies
- Continuous monitoring — Trust scores degrade over time and are continuously re-evaluated
┌─────────────────────────────────────────────────────────────────┐
│ ZeroTrust-AI │
│ │
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────────────┐ │
│ │ Behavioral │ │ Adaptive │ │ Microsegmentation │ │
│ │ Analytics │ │ Access │ │ Engine │ │
│ │ │ │ Control │ │ │ │
│ │ • Baselines │ │ • Risk-based│ │ • Flow analysis │ │
│ │ • Anomaly │──│ decisions │ │ • Cluster discovery │ │
│ │ • Sessions │ │ • Continuous│ │ • Policy recommend │ │
│ │ • Patterns │ │ verify │ │ • Segment mgmt │ │
│ └──────┬───────┘ └──────┬───────┘ └──────────┬───────────┘ │
│ │ │ │ │
│ ┌──────┴───────┐ ┌──────┴───────┐ ┌──────────┴───────────┐ │
│ │ Lateral │ │ Risk │ │ Policy Engine │ │
│ │ Movement │ │ Scoring │ │ │ │
│ │ │ │ │ │ • YAML policies │ │
│ │ • GNN graph │ │ • Composite │ │ • Simulation │ │
│ │ • Cred hops │──│ scoring │──│ • Conflict detect │ │
│ │ • Priv esc │ │ • Threat │ │ • Least privilege │ │
│ │ • Embeddings │ │ intel │ │ recommendations │ │
│ └──────────────┘ └──────────────┘ └──────────────────────┘ │
│ │
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────────────┐ │
│ │ Identity │ │ REST API │ │ Web Dashboard │ │
│ │ Registry │ │ (Flask) │ │ (Flask) │ │
│ └──────────────┘ └──────────────┘ └──────────────────────┘ │
└─────────────────────────────────────────────────────────────────┘
pip install -e ".[dev]"zerotrust-ai demoThis runs a complete scenario: identity setup, behavioral baseline learning, anomaly detection, risk scoring, access decisions, and lateral movement detection.
zerotrust-ai baseline --events 1000 --entities 50zerotrust-ai analyze --entity user-001 --hour 3 --location unknown-regionzerotrust-ai detect --nodes 20 --edges 60# Interactive policy demo
zerotrust-ai policy
# Load from YAML
zerotrust-ai policy --file sample_data/policies.yamlzerotrust-ai dashboard --host 127.0.0.1 --port 5000# Start API server
flask --app "zerotrust_ai.api.app:create_app()" run --port 8080
# Access decision
curl -X POST http://localhost:8080/api/v1/access/decide \
-H "Content-Type: application/json" \
-d '{"entity_id": "alice", "resource": "db-prod", "action": "read", "network_zone": "internal", "mfa_verified": true}'
# Risk score
curl -X POST http://localhost:8080/api/v1/risk/score \
-H "Content-Type: application/json" \
-d '{"entity_id": "bob", "behavior_score": 0.8, "device_health": 0.3}'docker-compose up -d
# API on :8080, Dashboard on :5000| Module | Description |
|---|---|
behavioral/ |
Behavioral analytics — baseline learning, anomaly detection, session analysis, pattern recognition |
access/ |
Adaptive access control — risk-based decisions, continuous verification, context-aware policies |
microseg/ |
Microsegmentation — flow analysis, cluster discovery, segment management, policy recommendations |
lateral/ |
Lateral movement detection — GNN-based graph analysis, credential hopping, privilege escalation |
risk/ |
Risk scoring — composite scores from behavior + device + network + threat intel + auth strength |
policy/ |
Policy engine — YAML policies, simulation (what-if), conflict detection, least-privilege recommendations |
identity/ |
Identity context — user/service/device registry, identity correlation, session tracking |
api/ |
REST API — access decisions, risk queries, policy management, behavioral reports |
dashboard/ |
Web dashboard — trust heatmap, risk timeline, lateral movement visualization, decision log |
The behavioral engine learns per-entity statistical profiles using online (streaming) algorithms — no raw event storage required:
- Time-of-day distributions — When does this user normally work?
- Resource access frequencies — What does this user normally access?
- Session duration statistics — How long are normal sessions? (Welford's algorithm for running mean/variance)
- Geographic patterns — Where does this user normally connect from?
- Entropy analysis — How predictable is this user's behavior?
The lateral movement detector uses a two-layer graph neural network (pure NumPy, no framework dependencies):
h_v' = ReLU(W_self · h_v + W_neigh · AGG(h_u : u ∈ N(v)) + b)
Node embeddings capture structural patterns. When access patterns change (new credential hops, new paths to high-value targets), embedding distances from the baseline reveal lateral movement.
Risk scores combine five weighted signals:
risk = w_b · behavior + w_d · (1 - device_health) + w_n · (1 - network_trust) + w_t · threat_intel + w_a · (1 - auth_strength)
Default weights: behavior=0.30, device=0.20, network=0.15, threat=0.20, auth=0.15
# Run all tests
pytest
# With coverage
pytest --cov=zerotrust_ai --cov-report=term-missing
# Specific module
pytest tests/test_behavioral.py -vzerotrust-ai/
├── src/zerotrust_ai/
│ ├── behavioral/ # Behavioral analytics
│ ├── access/ # Adaptive access control
│ ├── microseg/ # Microsegmentation
│ ├── lateral/ # Lateral movement detection
│ ├── risk/ # Risk scoring
│ ├── policy/ # Policy engine
│ ├── identity/ # Identity registry
│ ├── api/ # REST API
│ ├── dashboard/ # Web dashboard
│ └── cli.py # CLI interface
├── tests/ # 50+ tests
├── sample_data/ # Synthetic data
├── pyproject.toml
├── Dockerfile
├── docker-compose.yml
└── README.md
Corey A. Wade — CISSP, CCIE #14124
- PhD candidate in AI + Security
- 20+ years in network security architecture
- Building at the intersection of AI and zero trust
MIT — see LICENSE