CyberStrikeAI is an AI-native cybersecurity platform that orchestrates authorized penetration testing and security operations through AI agents, evidence tracking, and auditable workflows.
The tool addresses the challenge of coordinating complex security assessments by connecting planning, execution, human oversight, and evidence collection in a single workspace. It uses Eino-powered agents to drive operations, integrates MCP-native tools for extensibility, applies RAG knowledge systems to inform decisions, and models attack chains to analyze security posture. Operations are recorded as auditable evidence that feeds back into the system, allowing subsequent operations to learn from prior results.
Teams conducting authorized security testing should evaluate this tool if they need visibility into how AI agents make decisions during penetration testing, want to maintain an auditable record of all actions taken, or require human oversight checkpoints before high-risk operations execute. The platform is built in Go and includes capabilities for visual workflow design, WebShell access, and C2 functionality. The README emphasizes that users must own or have explicit authorization for any systems tested, and recommends reviewing the security model and hardening guide before enabling high-risk features in shared or production environments.
The project shows active development with regular updates to core functionality. The codebase demonstrates attention to security concerns through dedicated documentation on security hardening and threat modeling. Documentation is maintained in multiple languages, indicating effort toward accessibility for a broader audience. The tool integrates with established frameworks and protocols, suggesting a focus on compatibility with existing security workflows rather than isolation.
