Jane Street

About

Doppel is an AI-native platform focused on detecting and disrupting social engineering attacks. We build systems that identify AI-powered impersonation, phishing, and fraud across the internet, link them into real-time threat graphs, and actively dismantle attacker infrastructure.

Our work combines LLM-driven detection, large-scale threat intelligence, and autonomous response systems with training and simulation to strengthen human defenses. Our mission is to protect the world from social engineering attacks every day.

A challenge for the 3Blue1Brown Audience

At Doppel, every decision comes down to reasoning about p(y) — the probability that a message is malicious given what we observe. Here's a puzzle that captures the core of that challenge.

Setup. You're a cracked security team defending your users against a known phisher. Each day, the two of you are locked in a cat-and-mouse fight. The attacker sends a phishing campaign and chooses how often to use each of n persuasive phrases in their messages — things like "urgent", "verify account", "free gift", "security alert", "payroll".

The attacker selects a distribution \mathbf{p} = (p_1, p_2, \ldots, p_n) over phrases T = \{t_1, t_2, \ldots, t_n\}, where p_i = P(\text{attacker uses } t_i).

Each phrase t_i has an intrinsic social-engineering payoff s_i: if a message using t_i reaches a user unflagged, it succeeds with probability s_i. If the message is flagged or fails to convince, payoff = 0.

Your detector. You don't have the bandwidth to inspect every message perfectly — these phrases appear innocently in normal traffic too. Instead, you run a lightweight detector that monitors phrase frequencies. Given a baseline distribution \mathbf{q} = (q_1, q_2, \ldots, q_n) representing natural phrase frequencies, you raise an alarm when:

for some constant C. (You might recognize this quantity.)

The question. What distribution \mathbf{p} should the attacker choose to maximize their expected payoff?

Hint: the optimal strategy isn't deterministic.

Featured work

Threat Classification with OpenAI RFT

Doppel's threat classification system uses OpenAI's GPT-5 and Reinforcement Fine-Tuning (RFT) to make real-time decisions on whether a detected signal is malicious, benign, or ambiguous. Each signal passes through multiple LLM prompts purpose-built for different threat types — assessing impersonation risk, brand misuse, and social engineering patterns.

The system learns continuously: human analysts review and correct classifications, those corrections fine-tune models via OpenAI's RFT platform, and improved models are redeployed to production. The result is a tightening feedback loop that cut analyst workloads by 80% and reduced threat mitigation from hours to minutes.

Eliminating ML Infrastructure Tax with Modal

Running ML at scale used to mean managing a maze of Docker builds, Cloud Run services, IAM auth layers, and cold starts — overhead that crowded out the actual modeling work. Doppel replaced that stack with Modal's function-based inference, collapsing multi-step service pipelines into direct function calls.

The result: warm builds dropped from 10–30 minutes to under a minute, the team gained automatic scaling for traffic spikes, and engineers shifted their attention from infrastructure management to model development.