Sponsored by

Whitepaper

Securing AI from the Inside Out

AI systems present novel security challenges that emerge from the training data, model architecture, and inference infrastructure rather than from conventional application vulnerabilities. This whitepaper maps the threat landscape specific to AI, covering adversarial attacks that manipulate model outputs, data poisoning that corrupts training pipelines, and model inversion attacks that reconstruct sensitive training data from query responses. For security teams supporting AI initiatives across the enterprise, it provides a layered defense framework that addresses AI security at the data ingestion, model development, deployment, and monitoring phases. Practitioners will find controls they can implement without slowing AI development velocity, giving security teams the confidence to support rapid AI adoption while maintaining the governance and integrity standards the business requires.