-- ============================================================================
-- 🛡️ ROCHESTON ZELC PROGRAMMING LANGUAGE
-- 🏢 Organization: Rocheston
-- 📜 Platform: Rocheston Agentic AI Cybersecurity Automation Platform
-- ============================================================================

-- Playbook: Cross-Domain XDR Correlation (XDR Keywords)
-- Intent: Demonstrates 'correlation', 'telemetry', 'signal_to_noise', and 'lookback'. 
-- Connects disparate events into a single "Attack Story" for rapid remediation.
-- Infrastructure: Zelfire XDR Lake, AINA Correlation Engine, Rosecoin.
-- Note: 'confidence_score' determines if the response is fully autonomous.

package soc.zelxdr_core
use xdr, threat_intel, cloud, identity, slack, rosecoin

-- ============================================================================
-- 1. CONFIGURATION GOVERNORS (Strongly Typed)
-- ============================================================================
⚙️ keep MIN_CONFIDENCE: Integer = 85
⚙️ keep LOOKBACK_WINDOW: Duration = 30d
⚙️ keep NOISE_FLOOR: Decimal = 0.1

-- ============================================================================
-- 2. THE ENTRY POINT (The Correlation Engine)
-- ============================================================================
🔥 check XDRCrossDomainCorrelation
  
  -- Trigger Gate: Multiple low-fidelity alerts fire across different domains
  ⚠️ when multi_domain_signals_detected
    
    🔹 set incident_id: String = xdr generate_story_id
    🔹 set endpoint_signal: telemetry = xdr get_telemetry from "Endpoint"
    🔹 set cloud_signal: telemetry = xdr get_telemetry from "AWS-CloudTrail"
    
    🚨 alert info message "XDR ANALYSIS: Correlating signals from Endpoint and Cloud. Signal-to-Noise: {xdr.signal_to_noise}."

    -- ============================================================================
    -- 3. THE ANALYTICAL SYNTHESIS (correlation, indicators, lookback)
    -- ============================================================================
    ⚡ do
      
      -- Action 1: Perform 'lookback' to find historical indicators of the same actor
      -- Voice-friendly: "XDR perform lookback..."
      🔹 set historical_hits: Array = xdr lookback for event.actor_id window LOOKBACK_WINDOW
      
      -- Action 2: Run the 'correlation' logic to link the PowerShell to the API spike
      -- Links 'powershell' (Host) + 'sts:AssumeRole' (Cloud) + 's3:GetObject' (Data).
      🔹 set attack_story: Object = xdr correlate endpoint_signal with cloud_signal
      
      -- Action 3: Calculate the 'confidence_score' for the synthesized threat
      🔹 set confidence: Integer = xdr calculate_confidence attack_story

      -- ============================================================================
      -- 4. THE AUTONOMOUS RESPONSE (confidence_score, warroom, suppress)
      -- ============================================================================
      -- Action 4: If confidence is high, trigger autonomous remediation
      ⚠️ when confidence >= MIN_CONFIDENCE
         🚨 alert critical message "XDR ATTACK STORY: High-confidence ({confidence}%) exfiltration detected. Initiating XDR-Lockdown."
         
         -- Triggering the SOAR playbook from File #147
         ☁️ playbooks trigger "GlobalOrchestrationFlow" with { "affected_assets": attack_story.assets }
      🔴 end
      
      -- Action 5: If confidence is low, move to the 'warroom' for human triage
      ⚠️ when confidence < MIN_CONFIDENCE and confidence > 50
         🚨 alert warning message "XDR TRIAGE: Moderate confidence. Moving to Warroom for analyst review."
         ☁️ xdr open_warroom id incident_id participants ["SOC-Tier-2", "AINA"]
      🔴 end
      
      -- Action 6: Suppress the signal if it's below the 'noise_floor'
      ⚠️ when xdr.signal_to_noise < NOISE_FLOOR
         ☁️ xdr suppress_signal incident_id reason "Low_Signal_To_Noise"
      🔴 end

      -- ============================================================================
      -- 5. THE INTELLIGENCE UPDATE (threat_intel, cold_storage)
      -- ============================================================================
      -- Action 7: Update global 'indicators' based on the new attack pattern
      ☁️ threat_intel add_indicator type "behavioral_pattern" value attack_story.logic
      
      -- Action 8: Move the synthesized story to 'cold_storage' for future training
      ☁️ storage archive incident_id to "xdr-history-lake"

      -- Action 9: Notify the Global Security Director
      📡 notify slack channel "#xdr-intelligence-feed" message "👁️ *XDR Story Synthesized* \n*ID:* `{incident_id}` \n*Domains:* `Host, Cloud, Identity` \n*Confidence:* `{confidence}%` \n*Remediation:* `AUTONOMOUS_SOAR_TRIGGERED`"

      -- ============================================================================
      -- 6. EVIDENCE & FINAL ANCHOR
      -- ============================================================================
      📝 evidence record "XDR_Correlation_Success" details {
          story: incident_id,
          correlated_events: [endpoint_signal.id, cloud_signal.id],
          final_verdict: "THREAT_NEUTRALIZED",
          action: "XDR_STORY_LOCKED_AND_ANCHORED"
      }
      
      -- Anchor the full XDR story and its correlation logic to Rosecoin
      ⛓️ rosecoin anchor evidence_pack "latest"
      
    🔴 end
  🔴 end
🔴 end