AI systems introduce a new class of security risks that traditional cybersecurity practices were not designed to handle. These guides cover the unique vulnerabilities of AI-powered applications and the practical defences you need to protect them. You will learn about prompt injection attacks that trick AI into ignoring its instructions, adversarial inputs that cause models to misclassify data, data poisoning threats that corrupt training sets, and model extraction techniques that steal your proprietary AI. The topic also covers red teaming strategies for proactively finding weaknesses, security best practices for API-based AI services, and governance frameworks for managing AI risk across your organisation. You will find practical guidance on securing the full AI pipeline, from data collection through model deployment to ongoing monitoring. Whether you are a developer building AI features into your product, a security professional assessing new AI tools, or a leader responsible for your organisation's AI risk posture, these guides give you the knowledge to deploy AI systems that are resilient against both current and emerging threats.
Learn essential security practices for AI systems. From data protection to model security—practical steps to keep your AI implementations safe from threats.
Learn how to secure AI APIs against common attacks. From authentication to rate limiting—practical techniques for building secure AI interfaces.
Harden AI against adversarial examples, data poisoning, and evasion attacks. Testing and defense strategies.
Systematically test AI systems for failures, biases, jailbreaks, and harmful outputs. Build robust AI through adversarial testing.
Adversaries manipulate AI behavior through prompt injection. Learn attack vectors, detection, and defense strategies.