Doppel

Solution

Doppel rebuilt its defense stack around OpenAI models so threat analysis could move from manual review into autonomous, explainable decisions. The pipeline starts with heuristic filtering and o4-mini-based feature extraction over millions of domains, URLs, and accounts. Each suspicious signal is then evaluated by multiple GPT-5 prompts tailored to different threat questions, such as impersonation risk, brand misuse, or social-engineering patterns. An o4-mini model improved through reinforcement fine-tuning synthesizes those confirmations into a structured classification, after which a second GPT-5 pass validates the decision, generates justification, and can automatically trigger enforcement when confidence is high. Low-confidence cases are routed to analysts, and those decisions flow back into the RFT loop so future judgments become more consistent. This architecture gives customers faster action and clearer explanations for digital surfaces where threats can do damage within minutes.

Data flow

Domains, URLs, accounts, and other threat signals enter Doppel's pipeline, where heuristics and o4-mini reduce noise and extract features. GPT-5 and RFT-enhanced classifiers then assess malicious intent, produce labels and justifications, and either auto-enforce or escalate uncertain cases to analysts.