Essentials of AI security

- [Instructor] AI is transforming industries like pharma, healthcare, finance, retail, and others by enabling smarter fraud detection, more effective customer service, data-driven decision-making, and so much more. However, as powerful as AI is, it also introduces new security challenges, which require innovative solutions. Unlike traditional software, AI systems are dynamic in nature. They learn from the data and adapt over time based on the type of data which is fed to them. This adaptability makes AI powerful but also exposes it to evolving threats. To understand AI security, let's first clarify the difference between an AI model, AI product, and an AI system. The AI model is the core computational engine that learns from the data, analyzes it, and makes predictions or identifies patterns. For example, GPT-4 is a large language model that generates human-like text. While there are other models that help detect fraud, recognize images, or predict trends, the model holds the AI's intelligence, essentially being the brain of the system. Once the model is developed, it is then integrated into an AI product or multiple AI products, which are applications or user interfaces that deliver value using the model's core capabilities. The product provides a user-friendly experience while leveraging the AI model's intelligence to make decisions or predictions. However, the security of the model is crucial to maintaining the product's integrity. If the model is compromised through adversarial attacks, data poisoning, or reverse engineering, it could lead to incorrect predictions or biased outputs, undermining the functionality and trustworthiness of the AI product. An AI system is the complete solution that includes both the model and the product. It's a combination of all the components that work together to provide the end results to help solve specific problems or delivering certain tasks. Securing the entire AI system ensures that both the model and the product, as well as their entire ecosystem, works securely together to deliver reliable outcomes. Now let's talk about how AI differs from traditional software in key areas. Traditional software follows predefined rules and performs tasks in a fixed and predictable manner. AI systems, on the other hand, learn from the data and adapt over time, making their behavior dynamic and less predictable. In traditional software, data follows specific instructions to produce a predefined output, such as calculating the wages in a payroll system. In AI, on the other hand, data drives learning and decision-making. For decision-making, traditional software is deterministic. It follows fixed and preset rules or logic applied to it. On the other hand, AI is probabilistic, making predictions based on patterns in data, which introduces complexity and potential risks. The traditional software requires manual updates to fix errors. For example, if a payroll system calculates taxes incorrectly due to a recent change in tax law, a developer must update the software code to correct the issue. On the other hand, AI systems adapt to new data automatically. But this can introduce subtle errors or biases, like misclassifying employee categories, which are harder to detect and fix later. Traditional software is easier to audit. AI models, especially complex ones, are often like black box systems, making it harder to understand how the decisions are made in that black box, which poses security and compliance challenges. In traditional software, the code is embedded in the application itself, while in AI systems, the model's code resides on servers or cloud platforms trained on large datasets. The AI product integrates this model to interact with users or other systems. With these differences in mind, AI security presents unique challenges due to its reliance on data, continuous learning, and models evolving over time. AI systems depend on data to learn, and if this data is manipulated, such as through data poisoning attacks or other attacks, it can result in harmful outcomes like fraudulent transactions or biased decisions being made. Protecting data integrity is essential for AI security. Unlike traditional software, AI models face unique security risks. For example, there are adversarial attacks which might subtly manipulate data to trick the model into making incorrect decisions. Additionally, model theft is another attack which poses a risk, where attackers may reverse engineer the AI model to steal intellectual property or compromise its performance, making model security a critical concern. Unlike traditional software, AI products evolve over time as the models they use learn from new datasets. This requires continuous monitoring and updates to the underlying models, ensuring that the product remains secure and adaptive to emerging threats. Thus, an ongoing security approach is essential rather than a one-time fix. As AI becomes integral to business operations, leaders must address its unique security challenges. AI systems must be secure at launch and as they evolve, requiring robust security throughout the entire lifecycle, from data and model protection to safeguarding AI products in use. In the next video, we will explore common vulnerabilities and strategies to proactively address these risks. Let's get started.