CardinalOps Threat-Informed Detection Engineering is an AI-powered platform designed to optimize detection coverage across SIEM and EDR environments. It uses agentic workflows, large language models, and generative AI to automate detection engineering tasks, helping security operations centers (SOCs) identify and close detection gaps without requiring additional staff or tooling. The platform provides unified MITRE ATT&CK mapping across SIEM and EDR, offering heatmap views of detection coverage, health scores, and filters for security layers, APTs, and custom threat groups. It continuously delivers new, pre-tuned detection rules tailored to the organization's environment and SIEM/EDR syntax, developed by expert security researchers. It also identifies broken rules by diagnosing root causes such as missing log events, parsing issues, schema drift, and logic errors. For noisy rules, the platform uses AI-assisted pattern recognition and statistical analysis to recommend targeted log exclusions and reduce alert fatigue. A Threat Intelligence Operations (TI-Ops) module allows users to upload threat reports or integrate threat intelligence platforms and feeds, automatically extracting TTPs and generating curated detections mapped to MITRE coverage gaps. A Unified Exposure Management module correlates detection and prevention controls with asset inventory and vulnerability data to prioritize remediation. The Cardinal AI engine underpins all automation through agentic workflows, LLMs for MITRE mapping and TTP extraction, and GenAI for contextual reasoning and mitigation evaluation.