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Cybersecurity For AI Systems: Protecting Data, Models and Interfaces

Discover how to protect AI systems from cybersecurity threats including data poisoning, prompt injection, model theft, and API vulnerabilities. CENSUS's whitepaper covers AI security best practices for data, models, interfaces and platform resilience.

As AI adoption accelerates, organizations face expanding attack surfaces and novel security risks across data, models, and interfaces. This whitepaper from CENSUS breaks down AI cybersecurity into four critical pillars: data security and privacy, model integrity, interface and integration security, and platform robustness. It examines key threats-including prompt injection, model poisoning, data breaches, API weaknesses and supply chain attacks—while outlining a multi-layered, defense-in-depth strategy built on Zero Trust architecture, confidential computing, and cryptographic safeguards. Ideal for security leaders and AI product teams, the paper offers a practical framework for building resilient, compliant, and trustworthy AI-powered systems.

CENSUS-AI-SYSTEMS-DATA-PROTECTION-WhitePaper

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