Aikido Security has introduced Altar-1, an open-source artificial intelligence model designed to perform defensive cybersecurity tasks entirely within an organization's own infrastructure This article explores aikido security introduced. . The model is aimed at helping security teams utilize advanced AI for vulnerability discovery and penetration testing without sending source code, internal documentation, or security findings to external cloud-based inference services.
This includes banks with data residency obligations, healthcare providers handling sensitive patient information, and industrial organizations operating in isolated or air-gapped operational technology environments. This model is designed to operate within customer environments and continuously identify, exploit, and validate weaknesses across an attack surface. Altar-1, which adds local AI reasoning capabilities, allows security testing to process sensitive technical context without transmitting it to a third party.
The company first applied AWQ INT4 quantization, reducing the model to 488.2 GB, then used expert pruning to remove components less relevant to targeted security workloads. Additionally, the company incorporated multilingual text during calibration to ensure language understanding for reviewing documentation, business rules, application behavior, and user interfaces in different languages. Aikido employed Cerebras REAP (Router-weighted Expert Activation Pruning) to select experts based on router weights and output magnitude, preserving coding, cybersecurity, and natural-language reasoning capabilities.
The firm intends to refine the model, investigate lower-bit formats, and fine-tune future iterations for enhanced code analysis, remediation, tool utilization, and long-term security workflows.
