CyberSentinel AI launches autonomous cybersecurity platform According to CybersecurityNews (June 20, 2026), CyberSentinel AI v3.0 is an open-source cybersecurity platform that integrates 33 real-world penetration-testing and threat-intel tools with a provider-agnostic AI layer. The platform supports Claude, GPT-4o, OpenRouter, and fully offline local inference via Ollama (default model: qwen2.5:7b), and executes scanners such as Nmap, SQLMap, Nikto, Nuclei, and OWASP ZAP inside a sandboxed Kali container. Per cybersecuritynews.com, the platform deploys via Docker Compose across seven containerized services including a Next.js frontend, FastAPI backend, Neo4j knowledge graph, ChromaDB RAG store grounded in MITRE, CIS, and NIST, and an ELK Stack SIEM. The platform can run up to five tools concurrently and includes input/output guardrails blocking prompt injection and SSRF attacks. System requirements: Docker Desktop and a minimum of 8GB RAM. The project is published on GitHub at 3sk1nt4n/cybersentinel-ai. What happened Per CybersecurityNews (June 20, 2026), CyberSentinel AI v3.0 is an open-source platform combining 33 penetration-testing and threat-intelligence tools with a provider-agnostic AI engine. Supported inference providers include Claude, GPT-4o, OpenRouter, and fully offline local inference via Ollama (using qwen2.5:7b as the default local model). The platform executes real scanners including Nmap, SQLMap, Nikto, Nuclei, and OWASP ZAP inside a sandboxed Kali container, and is available on GitHub at 3sk1nt4n/cybersentinel-ai. Technical details Per CybersecurityNews, the platform deploys via Docker Compose across seven containerized services. A Next.js frontend (port 3000) provides a streaming chat interface; a FastAPI backend (port 8000) handles AI routing, intent classification, and tool orchestration. Security scans execute inside an isolated Kali container. Supporting data infrastructure includes Neo4j for knowledge-graph mapping of attack surfaces and MITRE ATT&CK techniques, ChromaDB as a RAG engine grounded in MITRE, CIS, and NIST frameworks, and an ELK Stack (Elasticsearch + Kibana) pre-seeded with security events as a SIEM. The AI engine can classify