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Cobalt’s crowdsourced application security solution transforms today’s broken pen testing model into a data driven engine fueled by their global talent pool of trusted pen testers. Their SaaS platform delivers …
Rapid7 is engineering better security with simple, innovative solutions for IT security's most critical challenges. Rapid7 security data and analytics solutions collect, contextualize, correlate, and analyze the security data you …
The NodeZero™ platform empowers organizations to continuously find, fix, and verify exploitable attack surfaces. It is the flagship product of Horizon3.ai, founded in 2019 by former industry and U.S. National …
Bugcrowd delivers the ultimate in security assessment for the enterprise. The pioneer and innovator in crowdsourced security testing for the enterprise, Bugcrowd combines the power of more than 65,000 security …
Founded by mathematicians and cyber defense experts in 2013, Darktrace is a global leader in cybersecurity AI, delivering complete AI-powered solutions in its mission to free the world of cyber …
HackerOne is a SaaS platform that enables security researchers to find and report security holes to companies before they can get exploited. More than 400 companies, including Adobe, Yahoo, Twitter, …
Synack is a security company revolutionizing how enterprises view cybersecurity through a hacker’s eyes. Synack’s hacker powered security platform arms clients with hundreds of the world’s most skilled, highly vetted …
Aikido is the get-it-done security platform for developers. Aikido centralizes all necessary code and cloud security scanners in one place. Created by pragmatic engineers, they put open-source solutions and the …
Chainstack provides unified access to multichain node and data APIs, distributed compute and storage, identity management and security testing, and the ever-expanding list of services and tools to build amazing …
Astra is a cybersecurity SaaS company headquartered in the U.S. and India. Astra is the trusted security partner for leading brand including Muthoot Finance, Loom, CompTIA, NIIT, Goldcast, HackerRank, Sprinto, …
Picus Security, the leading security validation company, gives organizations a clear picture of their cyber risk based on business context. Picus transforms security practices by correlating, prioritizing, and validating exposures …
Scrut Automation is on a mission to simplify risk-based information security for cloud-native companies. Security can be complicated. With hundreds of assets and controls to monitor, an increasing number of …
CyberProof, a UST company, helps their clients transform their security to a cost-effective, cloudnative technology architecture. Their next-generation Managed Detection & Response (MDR) service is built to support large, complex …
Hadrian’s digital security model focuses on automated event-based scanning. Their approach maps vulnerabilities in an organisations’ entire attack surface infrastructure, and provides the critical insights to fortify them. Unlike traditional …
Clover Security delivers design-led product security, using AI agents to embed security into the earliest stages of development, replicating the thinking of experienced security architects to surface risks early, anticipate …
Adversarial Exposure Validation (AEV) is a technique used mainly in machine learning and data science to detect dataset shift or data leakage between different datasets (usually training vs. validation/test datasets). It helps determine whether the model might perform unrealistically well because the datasets are not distributed the same way.
AEV trains a secondary model whose job is to answer one question:
“Can a model tell whether a sample comes from dataset A or dataset B?”
For example:
Dataset A → Training data
Dataset B → Validation/Test data
You label them like this:
| Data Source | Label |
|---|---|
| Training dataset | 0 |
| Validation/Test dataset | 1 |
Then you train a classifier to distinguish them.
Combine datasets
Merge training and validation/test data.
Add source labels
Training samples → label 0
Validation/test samples → label 1
Train a classifier
Any classifier works (e.g., logistic regression, random forest, gradient boosting).
Measure performance
Evaluate using metrics like AUC.
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