1
// Multi-tenant data isolation architecture
2
pub struct TenantIsolation {
3
    tenant_id: Uuid,
4
    data_boundaries: DataBoundary,
5
    encryption_keys: TenantKeyStore,
6
    resource_quotas: ResourceLimits,
7
}
8

9
impl TenantIsolation {
10
    pub fn enforce_isolation(&self, request: &Request) -> Result<(), IsolationError> {
11
        // Cryptographic isolation at the data layer
12
        self.verify_tenant_context(request)?;
13
        self.apply_encryption_boundary()?;
14
        self.enforce_resource_limits()?;
15
        Ok(())
16
    }
17
}

1
// Custom threat visualization plugin
2
export class ThreatVisualizationPlugin implements Plugin {
3
  private threatDataService: ThreatDataService;
4
  private correlationEngine: CorrelationEngine;
5

6
  public setup(core: CoreSetup): void {
7
    // Register custom visualization types
8
    expressions.registerFunction(threatMapVisualization);
9
    expressions.registerFunction(attackChainTimeline);
10
    expressions.registerFunction(riskScoreMatrix);
11

12
    // Enhanced security analytics
13
    this.registerSecurityDashboards(core);
14
  }
15

16
  private async renderThreatLandscape(data: SecurityEvents[]): Promise<Visualization> {
17
    const correlated = await this.correlationEngine.correlate(data);
18
    const enriched = await this.enrichWithThreatIntel(correlated);
19
    return this.visualizationRenderer.render(enriched);
20
  }
21
}

1
use tokio::time::{interval, Duration};
2
use sysinfo::{System, SystemExt, ProcessExt};
3

4
pub struct InvinsenseAgent {
5
    system: System,
6
    collectors: Vec<Box<dyn DataCollector>>,
7
    transmitter: SecureTransmitter,
8
}
9

10
impl InvinsenseAgent {
11
    pub async fn monitor(&mut self) -> Result<(), MonitorError> {
12
        let mut interval = interval(Duration::from_secs(5));
13

14
        loop {
15
            interval.tick().await;
16

17
            // Collect system metrics
18
            self.system.refresh_all();
19

20
            // Gather security events
21
            let events = self.collect_security_events().await?;
22

23
            // Apply local correlation
24
            let correlated = self.local_correlation(&events)?;
25

26
            // Secure transmission to platform
27
            self.transmitter.send_encrypted(correlated).await?;
28
        }
29
    }
30

31
    async fn collect_security_events(&self) -> Result<Vec<SecurityEvent>, CollectionError> {
32
        let mut events = Vec::new();
33

34
        for collector in &self.collectors {
35
            let collected = collector.collect().await?;
36
            events.extend(collected);
37
        }
38

39
        Ok(events)
40
    }
41
}

1
// Event processing pipeline
2
pub struct EventPipeline {
3
    // Layer 1: High-speed ingestion
4
    ingestion: KafkaConsumer,
5

6
    // Layer 2: Stream processing
7
    stream_processor: FlinkProcessor,
8

9
    // Layer 3: Complex event correlation
10
    correlation: CorrelationEngine,
11

12
    // Layer 4: ML-based analysis
13
    ml_analyzer: TensorFlowModel,
14
}

1
#[cfg(target_os = "windows")]
2
mod windows_collector {
3
    use windows::Win32::System::EventLog;
4

5
    pub fn collect_events() -> Vec<Event> {
6
        // Windows-specific event collection
7
    }
8
}
9

10
#[cfg(target_os = "linux")]
11
mod linux_collector {
12
    use inotify::{Inotify, WatchMask};
13

14
    pub fn collect_events() -> Vec<Event> {
15
        // Linux-specific event collection using inotify
16
    }
17
}

1
# GitLab CI/CD pipeline for Invinsense
2
security-scan:
3
  parallel:
4
    matrix:
5
      - SCAN_TYPE: [sast, dast, dependency, container]
6
  script:
7
    - |
8
      case $SCAN_TYPE in
9
        sast)
10
          semgrep --config=auto --json -o sast-report.json .
11
          ;;
12
        dast)
13
          zap-full-scan.py -t $TARGET_URL -r dast-report.html
14
          ;;
15
        dependency)
16
          cargo audit
17
          safety check
18
          ;;
19
        container)
20
          trivy image $IMAGE_NAME
21
          ;;
22
      esac
23
  artifacts:
24
    reports:
25
      security: "*-report.*"

1
pub struct AdaptiveThreatIntel {
2
    base_models: Vec<ThreatModel>,
3
    environment_profile: EnvironmentProfile,
4
    learning_rate: f64,
5
}
6

7
impl AdaptiveThreatIntel {
8
    pub fn adapt(&mut self, observed_threats: &[Threat]) {
9
        // Learn from local environment
10
        for threat in observed_threats {
11
            self.update_models(threat);
12
            self.adjust_detection_thresholds(threat);
13
        }
14

15
        // Generate custom IoCs
16
        let custom_iocs = self.generate_environment_specific_iocs();
17
        self.distribute_to_agents(custom_iocs);
18
    }
19
}

1
pub struct ZeroTrustEngine {
2
    identity_verifier: IdentityVerifier,
3
    device_trust: DeviceTrustEvaluator,
4
    context_analyzer: ContextAnalyzer,
5

6
    pub fn evaluate_access(&self, request: &AccessRequest) -> AccessDecision {
7
        let identity_score = self.identity_verifier.verify(&request.identity);
8
        let device_score = self.device_trust.evaluate(&request.device);
9
        let context_score = self.context_analyzer.analyze(&request.context);
10

11
        // Continuous verification
12
        match (identity_score, device_score, context_score) {
13
            (score, _, _) if score < 0.5 => AccessDecision::Deny,
14
            (_, _, context) if context.is_anomalous() => AccessDecision::Challenge,
15
            _ => AccessDecision::AllowWithMonitoring,
16
        }
17
    }
18
}

1
pub struct RemediationOrchestrator {
2
    playbooks: HashMap<ThreatType, RemediationPlaybook>,
3
    executors: Vec<Box<dyn RemediationExecutor>>,
4

5
    pub async fn remediate(&self, threat: &Threat) -> RemediationResult {
6
        let playbook = self.select_playbook(threat);
7

8
        // Pre-remediation snapshot
9
        let snapshot = self.create_system_snapshot().await?;
10

11
        // Execute remediation
12
        let result = self.execute_playbook(playbook, threat).await?;
13

14
        // Verify success
15
        if !self.verify_remediation(&result).await? {
16
            self.rollback(snapshot).await?;
17
            return RemediationResult::Failed;
18
        }
19

20
        RemediationResult::Success(result)
21
    }
22
}

1
apiVersion: apps/v1
2
kind: Deployment
3
metadata:
4
  name: invinsense-core
5
  namespace: invinsense
6
spec:
7
  replicas: 3
8
  selector:
9
    matchLabels:
10
      app: invinsense-core
11
  template:
12
    metadata:
13
      labels:
14
        app: invinsense-core
15
    spec:
16
      containers:
17
      - name: correlation-engine
18
        image: infopercept/invinsense-correlation:latest
19
        resources:
20
          requests:
21
            memory: "4Gi"
22
            cpu: "2"
23
          limits:
24
            memory: "8Gi"
25
            cpu: "4"
26
      - name: detection-engine
27
        image: infopercept/invinsense-detection:latest
28
        env:
29
        - name: ML_MODEL_PATH
30
          value: "/models/latest"
31
      - name: response-orchestrator
32
        image: infopercept/invinsense-response:latest

1
version: '3.8'
2
services:
3
  opensearch:
4
    image: opensearchproject/opensearch:2.11.0
5
    environment:
6
      - discovery.type=single-node
7
      - OPENSEARCH_JAVA_OPTS=-Xms512m -Xmx512m
8
    volumes:
9
      - opensearch-data:/usr/share/opensearch/data
10

11
  invinsense-api:
12
    build: ./api
13
    environment:
14
      - OPENSEARCH_URL=http://opensearch:9200
15
      - RUST_LOG=debug
16
    depends_on:
17
      - opensearch
18

19
  invinsense-ui:
20
    build: ./ui
21
    ports:
22
      - "3000:3000"
23
    depends_on:
24
      - invinsense-api
25

26
volumes:
27
  opensearch-data:

1
// Example: Optimizing correlation engine
2
// Before: O(n²) correlation
3
pub fn correlate_naive(events: &[Event]) -> Vec<Correlation> {
4
    let mut correlations = Vec::new();
5
    for i in 0..events.len() {
6
        for j in i+1..events.len() {
7
            if events[i].correlates_with(&events[j]) {
8
                correlations.push(Correlation::new(&events[i], &events[j]));
9
            }
10
        }
11
    }
12
    correlations
13
}
14

15
// After: O(n log n) with intelligent indexing
16
pub fn correlate_optimized(events: &[Event]) -> Vec<Correlation> {
17
    let index = build_correlation_index(events);
18
    index.find_correlations()
19
}