Phase 5: Advanced Security & Intelligence - Completion Summary¶

Status: Complete Date: 2026-02-09

Overview¶

Phase 5 implements advanced security intelligence capabilities across 6 major areas with 24 sub-tasks. All components are fully tested with 1400+ tests in the suite.

Phase Structure¶

5.1: ML-Based Threat Detection¶

Task	Component	File	Tests
5.1.1	Anomaly Detection (Isolation Forest)	`ml/anomaly_detector.py`	26
5.1.2	Behavioral Baseline Learning	`ml/behavioral_baseline.py`	26
5.1.3	Threat Scoring Framework	`ml/threat_scoring.py`	34
5.1.4	Threat Intelligence Integration	`ml/threat_intel.py`	33

5.2: Advanced HITL¶

Task	Component	File	Tests
5.2.1	Historical Risk Scoring	`hitl/risk_scorer.py`	23
5.2.2	Trust Manager	`hitl/trust.py`	24
5.2.3	Auto-Approval Policies	`hitl/auto_approval.py`	25
5.2.4	Context-Aware Decisions	`hitl/context_engine.py`	23

5.3: Credential Lifecycle Management¶

Task	Component	File	Tests
5.3.1	Automated Secret Rotation	`rotation.py`	30
5.3.2	Zero-Downtime Rotation	`rotation.py`	27
5.3.3	Rotation Verification	`verification.py`	29
5.3.4	Emergency Rotation Triggers	`emergency_rotation.py`	25

5.4: Network Security Hardening¶

Task	Component	File	Tests
5.4.1	TLS Certificate Pinning	`cert_pinning.py`	30
5.4.2	Deep Packet Inspection	`dpi.py`	33
5.4.3	Protocol Filtering	`protocol_filter.py`	37
5.4.4	Traffic Anomaly Detection	`ml/traffic_anomaly.py`	36

5.5: Audit Enhancements¶

Task	Component	File	Tests
5.5.1	SIEM Integration	`siem_integration.py`	69
5.5.2	Automated Alert Rules	`alert_rules.py`	65
5.5.3	Compliance Reports	`compliance_reports.py`	36
5.5.4	Security Dashboard	`dashboard.py`	41

5.6: Integration & Testing¶

Task	Component	File	Tests
5.6.1	Integration Tests	`tests/integration/test_phase5_integration.py`	17
5.6.2	Performance Benchmarks	`tests/performance/test_phase5_benchmarks.py`	20
5.6.3	Security Validation	`tests/security/test_phase5_security_validation.py`	32
5.6.4	Documentation	`docs/phase5_completion_summary.md`	-

Architecture¶

Security Layer (`src/harombe/security/`)¶

security/
├── ml/
│   ├── anomaly_detector.py    # Isolation Forest per-agent models
│   ├── behavioral_baseline.py # Statistical baseline learning
│   ├── threat_scoring.py      # Multi-factor threat scoring
│   ├── threat_intel.py        # Threat intelligence feeds
│   ├── traffic_anomaly.py     # Network traffic ML detection
│   └── models.py              # Shared Pydantic models
├── hitl/
│   ├── core.py                # HITL gate, risk classifier
│   ├── risk_scorer.py         # Historical risk scoring
│   ├── trust.py               # Agent trust management
│   ├── auto_approval.py       # Auto-approval policies
│   └── context_engine.py      # Context-aware decisions
├── siem_integration.py        # Splunk/Elasticsearch/Datadog export
├── alert_rules.py             # Automated alert rule engine
├── compliance_reports.py      # PCI DSS/GDPR/SOC 2 reports
├── dashboard.py               # Real-time security dashboard
├── cert_pinning.py            # TLS certificate pinning
├── dpi.py                     # Deep packet inspection
├── protocol_filter.py         # Protocol-level filtering
├── rotation.py                # Credential rotation
├── verification.py            # Rotation verification
├── emergency_rotation.py      # Emergency rotation triggers
├── audit_db.py                # SQLite audit database
├── audit_logger.py            # Async audit logging
├── gateway.py                 # MCP security gateway
├── network.py                 # Network isolation/egress
├── secrets.py                 # Secret scanning
├── vault.py                   # Credential vault backends
└── injection.py               # Secure env injection

Key Design Patterns¶

ML Pipeline¶

Per-agent models: Each agent gets its own Isolation Forest model and StandardScaler
Feature extraction: Events → feature vectors (temporal, resource, behavioral)
Dual detection: Statistical Z-score + ML Isolation Forest (60/40 weight)
Minimum sample guards: Models won't train with insufficient data

SIEM Integration¶

Platform abstraction: SIEMExporter base class with Splunk/ES/Datadog implementations
Buffered export: Configurable batch_size with background flush worker
Retry logic: Exponential backoff for HTTP transport failures
Event normalization: AuditEvent → SIEMEvent with severity mapping

Alert Rules¶

Condition matching: Field-level operators (eq, ne, contains, in, gt, lt)
Windowed counting: N events in T seconds threshold rules
Deduplication: Configurable cooldown to prevent alert storms
Multi-channel: Slack, Email, PagerDuty notification support

Compliance Reports¶

Framework-specific: PCI DSS, GDPR, SOC 2 with mapped controls
Automated checks: 8 check functions assess controls from audit data
Export formats: HTML (styled), JSON (machine-readable)
Evidence-based: Each control includes evidence summaries and findings

Dashboard¶

Cached metrics: TTL-based MetricsCache (default 60s)
12 key metrics: Activity (4), Security (6), Performance (2)
Trend data: Hourly time series for events, errors, denials, tool_calls
WebSocket-ready: JSON-serializable snapshots for real-time updates

Performance Results¶

Component	Metric	Target	Actual
Anomaly Detection	Per-event latency	<50ms	~3ms
Anomaly Detection	Throughput	>200/sec	260/sec
Model Training	1000 events	<2s	~0.15s
SIEM Event Conversion	Per-event	<1ms	~0.01ms
SIEM Export Throughput	Events/sec	>100	>1000
Alert Rule Evaluation	Per-event (10 rules)	<10ms	~0.3ms
Dashboard Computation	Full metrics	<200ms	~50ms
Dashboard Cached	Access time	<1ms	~0.002ms
Compliance Report	Generation	<500ms	~100ms
HTML Export	Per report	<100ms	~1ms
Traffic Detection	Per-connection	<20ms	~1ms
Baseline Learning	1000 connections	<1s	~0.08s
Baseline Comparison	Per-event	<1ms	~0.005ms

Security Validation¶

32 security validation tests cover:

SQL injection prevention (5 tests): Parameterized queries protect all fields
SIEM credential security (3 tests): Token handling, no credential leaks
Alert rule injection resistance (3 tests): Dunder field blocking, ReDoS prevention
Compliance report integrity (3 tests): Accurate data reflection, XSS considerations
Dashboard data security (4 tests): Cache isolation, aggregated-only output
ML model robustness (4 tests): Poisoning resistance, extreme values, safe defaults
Traffic evasion resistance (4 tests): Data exfiltration detection, unusual ports
Baseline security (3 tests): Poisoned event tolerance, minimum sample enforcement
SIEM event validation (3 tests): Timestamp format, severity mapping, completeness

Test Summary¶

Category	Tests
Phase 5.1 (ML)	119
Phase 5.2 (HITL)	95
Phase 5.3 (Credentials)	111
Phase 5.4 (Network)	136
Phase 5.5 (Audit)	211
Phase 5.6 (Integration)	69
Total Phase 5	741
Full Suite	1400+