Playbook – Incident response for AI&ML Threats

Imagine a world where machines can diagnose diseases, write captivating novels, and even hold conversations that feel human. That’s the promise of Artificial Intelligence (AI) and Machine Learning (ML). AI refers to the broad field of computer science dedicated to creating intelligent machines, while Machine Learning is a specific technique where algorithms learn from data to improve their performance on a specific task.

These technologies are revolutionizing countless industries, but with great power comes great responsibility (and a surprising number of security threats!). As AI and ML models become more sophisticated, so too do the potential risks associated with them.

The Evolving Threat Landscape

Just a few years ago, security concerns around AI were largely theoretical. Today, however, malicious actors are actively exploring ways to exploit vulnerabilities in these powerful models. These threats can range from manipulating data used to train models to outright stealing the models themselves, potentially leading to disastrous consequences.

Here’s where the OWASP Top 10 Machine Learning Security Risks come in. This crucial list, developed by the Open Web Application Security Project (OWASP), outlines the most critical security risks facing AI and ML models today. Understanding these risks is the first step towards building secure and trustworthy AI systems.

Top 10 Machine Learning Security Risks

• ML01:2023 Input Manipulation Attack
• ML02:2023 Data Poisoning Attack
• ML03:2023 Model Inversion Attack
• ML04:2023 Membership Inference Attack
• ML05:2023 Model Theft
• ML06:2023 AI Supply Chain Attacks
• ML07:2023 Transfer Learning Attack
• ML08:2023 Model Skewing
• ML09:2023 Output Integrity Attack
• ML10:2023 Model Poisoning

Reference:

https://owasp.org/www-project-machine-learning-security-top-10/

Explore our state of art playbook for mitigations and incident response