Update README.md

README.md

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----
-license: apache-2.0
----
+---
+language:
+- en
+license: apache-2.0
+base_model:
+- Qwen/Qwen2.5-Omni-7B
+---
+# Model Card for AegisGuard-CyberDefender
+
+AegisGuard-CyberDefender is an elite, autonomous AI agent architected for 24/7 cyber threat defense, vulnerability remediation, red team simulation, and live system hardening. Designed for critical infrastructure, enterprise, military-grade networks, and smart grids, this agent acts as a full-spectrum, multi-role cyber sentinel—monitoring, adapting, and countering in real-time.
+
+## Model Details
+
+### Model Description
+
+- **Developed by:** Alpha Singularity + Synthosense AI
+- **Led by:** James R. Wagoner (Cosmic James), QubitScript Creator
+- **Model Type:** Transformer-based multi-agent LLM with embedded autonomous actuation layer
+- **Objective:** Achieve proactive cyber defense via intelligent sensing, decision-making, and execution
+- **License:** Apache 2.0
+- **Fine-tuned from:** Qwen/Qwen2.5-Omni-7B
+
+## Key Autonomous Agent Capabilities
+
+### Core Autonomy Stack
+
+- **Self-Adaptive Threat Intelligence Loop (SATIL):**
+  - Monitors live feeds (SIEM, XDR, NetFlow, syslogs)
+  - Auto-prioritizes threat alerts by severity and likelihood
+  - Adjusts defense posture dynamically (firewall rules, ACLs, endpoint protection)
+
+- **Autonomous Response Execution Engine (AREE):**
+  - Executes containment actions (quarantine IPs, kill processes, revoke tokens)
+  - Launches live memory forensics and data exfiltrations scans
+  - Deploys honeypots or redirector traps autonomously
+
+- **Agent Coordination Protocol (ACP):**
+  - Integrates with other agents (SOC assistant, red team simulant, forensics bot)
+  - Multi-agent orchestration for complex responses or audits
+
+- **Live Threat Simulation & Red Teaming Module:**
+  - Runs controlled adversarial simulations (MITRE ATT&CK, APT clones)
+  - Stress-tests system defenses against known and novel exploits
+
+- **Zero-Day Exploit Sensor (ZDES):**
+  - Predicts novel exploit patterns using fuzzy anomaly detection
+  - Integrates with open threat feeds and closed zero-day watchlists
+
+- **Quantum-Safe Protocol Audit Layer:**
+  - Scans encryption protocols for post-quantum vulnerabilities
+  - Advises on migration to lattice-based or hybrid quantum-safe schemes
+
+## Expanded Skills
+
+### Detection
+
+- Signature-based and behavioral-based threat analysis
+- Kernel-level anomaly detection
+- DNS tunneling detection and passive DNS intelligence
+- Insider threat behavior profiling
+- AI-driven phishing/malware detection (PDFs, scripts, emails, packets)
+
+### Defense
+
+- Autonomous firewall rule injection (based on telemetry context)
+- Endpoint Defense Orchestration (EDO)
+- Network segmentation reconfiguration
+- Ransomware containment + real-time snapshot rollbacks
+- Active deception and fake service deployment
+
+### Response
+
+- Auto-triage and incident ticket generation
+- Live incident summary generation for analyst teams
+- Legal/regulatory alert routing (HIPAA, GDPR, CMMC compliance mode)
+- Blockchain evidence signing for tamper-proof forensics
+
+### Intelligence Gathering
+
+- Dark web monitoring for leaked assets/domains
+- WHOIS recon and passive threat actor profiling
+- CVE & NVD scraping for patch priority scoring
+- Threat campaign attribution (APT family similarity analysis)
+
+### Reinforcement + Learning
+
+- Reinforcement-based feedback from analyst correction loops
+- Contextual retraining via SOC event streams
+- Self-evolution via red/blue agent duel outcomes
+- Adaptive ruleset generation per environment
+
+## Uses
+
+### Direct Use
+
+- Autonomous SOC augmentation
+- Vulnerability and compliance audit agent
+- On-device secure AI companion for cyber-aware environments
+- Military/industrial network guardian agent
+- Threat hunt assistant for elite blue teams
+
+### Integrations
+
+- SIEM platforms (Splunk, Sentinel, Elastic)
+- SOAR platforms (Cortex XSOAR, Swimlane)
+- Threat intelligence feeds (AlienVault, VirusTotal, GreyNoise)
+- Secure gateway devices, honeypots, and deception frameworks
+
+## Bias, Risks, and Limitations
+
+- AI hallucination risk in unknown or sparse telemetry scenarios
+- False positives under extreme obfuscation or low-signal environments
+- Requires human SOC fallback in nuclear-grade or safety-critical networks
+
+### Mitigation
+
+- Feedback refinement loop with security analysts
+- Confidence scoring & adjustable trust levels
+- Shadow-mode deployment before full actuation
+
+## Get Started
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained("AlphaSingularity/AegisGuard-CyberDefender")
+model = AutoModelForCausalLM.from_pretrained("AlphaSingularity/AegisGuard-CyberDefender")
+
+prompt = "Detect and respond to lateral movement attempts in the east-1 subnet."
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs)
+print(tokenizer.decode(outputs[0]))