Use cases

Adversarial Security

Adversarial machine learning is a present, growing threat. As the development of AI systems in combat scenarios accelerates, the risk and complexity of these attacks are going to grow. This is why adversarial security is key.

Leaders need confidence that their ML models will withstand these potential threats, which can derail AI strategy.

How VESPR Validate Drives Mission Success

Measure adversarial robustness

This is a key performance indicator as governments increase the deployment of AI/ML on the front lines.

A process to expedite confident deployments

A repeatable, independent solution for testing and validation enables stakeholders to efficiently answer questions related to a model’s adversarial security, removing uncertainty.

Prepared for the future

Ongoing testing ensures models are resilient after deployment as the adversarial ML ecosystem rapidly evolves.

ML Failure - Adverse Weather Conditions

Confidence, Security,and Speed

VESPR Validate is the solution to quickly identify if AI/ML systems are secure against threats, building widespread trust in these technologies and gaining a strategic edge on adversaries.


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