AI Security and Safety

Imagine spending months training a machine learning model on sensitive patient data, only to have an attacker determine whether a specific individual’s records were used in your training dataset. This isn’t science fiction. It’s a membership inference attack, and it’s one of the most pressing privacy threats facing AI systems today.
Membership inference attacks exploit a fundamental vulnerability in how machine learning models learn. When a model trains on data, it inevitably memorizes some information about its training examples. Attackers leverage this behavior by querying your model and analyzing its responses to determine whether a specific data point was part of the …

Imagine training an AI model for months, investing thousands of dollars in computing power, only to discover that hidden within your training data are carefully planted digital landmines. These invisible threats, known as data poisoning attacks, can turn your trustworthy AI system into a manipulated tool that produces incorrect results, spreads misinformation, or creates dangerous security vulnerabilities. In 2023 alone, researchers documented hundreds of poisoned datasets circulating openly online, some downloaded thousands of times by unsuspecting developers.
Data poisoning occurs when attackers deliberately corrupt the training data that teaches AI models how to behave. Think of it like adding …

Test your model against intentionally manipulated inputs before deployment. Take a trained image classifier and add carefully calculated noise to test images—imperceptible changes that can cause a 90% accurate model to fail catastrophically. This reveals vulnerabilities that standard accuracy metrics miss entirely.
Implement gradient-based attack simulations during your evaluation phase. Generate adversarial examples using techniques like Fast Gradient Sign Method (FGSM), where slight pixel modifications fool models into misclassifying stop signs as speed limit signs. Understanding how attackers exploit your model’s decision boundaries is the first step toward building resilience.
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Every artificial intelligence system you use today traveled through a complex global supply chain before reaching your device—and that journey creates security vulnerabilities that governments and enterprises can no longer ignore. The Federal Acquisition Supply Chain Security Act (FASCSA), enacted in 2018, gives federal agencies unprecedented authority to identify and exclude compromised technology products and services from government systems. While initially focused on hardware and telecommunications, this legislation now stands at the forefront of AI security as agencies grapple with how to safely procure machine learning models, training data, and AI development tools.
The stakes are remarkably …