Three things to know about ArtificiAL Intelligence (AI) security

Vulnerability to Attacks

AI systems, particularly machine learning models, can be vulnerable to various types of attacks. These include adversarial attacks, where small, intentionally designed changes to input data can cause an AI model to make incorrect decisions or predictions. For example, slight alterations to an image can trick an image recognition system into misidentifying it. Protecting AI systems from these vulnerabilities requires rigorous testing and the development of models that can detect and resist such manipulations.

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Data Privacy and Security

AI systems often rely on vast amounts of data, including personal information, to train and operate effectively. Ensuring the privacy and security of this data is paramount. Breaches can lead to significant privacy violations and potential misuse of personal information. Techniques such as differential privacy, federated learning, and secure multi-party computation are among the methods used to protect data privacy while allowing AI systems to learn from data without compromising sensitive information.

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Ethical and Fair Use

The development and deployment of AI systems must be guided by ethical principles to ensure fairness, transparency, and accountability. There is a risk that AI systems may inadvertently perpetuate or amplify biases present in their training data, leading to unfair or discriminatory outcomes. This is particularly critical in high-stakes applications such as criminal justice, hiring, and lending. Efforts in AI ethics focus on creating frameworks and guidelines for the responsible use of AI, including ensuring that AI systems are auditable, explainable, and free from bias.

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Resources

Podcast Episodes

WHAT ARE DEEPFAKES WITH DR. DONNIE WENDT

SHOWMECON: HOW AI WILL IMPACT CYBERSECURITY ENHANCEMENTS AND THREATS WITH JAYSON E. STREET

Blogs and Guides

Deploy Securely - This is a great blog on AI security and compliance. It discusses challenges faced by organizations with AI governance. The author offers advice on how to navigate these challenges. Some of the important points from this blog are that organizations should avoid building expensive AI governance programs and focus on building trust with customers and auditors.

Analyzing AI Application Threat Models - A great write up on the different threats for AI applications that can be used as part of a threat model.

Joint Guidance on Deploying AI Systems Securely - CISA has released guidance on best practices for deploying and operating externally developed artifical intelligence (AI) systems.