AI Security
Generative AI vs predictive AI: how do they differ in risk?
Predictive AI scores or classifies from data. Generative AI creates new content. See how the two differ in use, failure modes and the controls each needs.
Oct 18, 20264 min read
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Generative AI vs predictive AI: how do the security risks differ?
Predictive AI classifies or forecasts. Generative AI produces new content. NIST treats both as attackable, with different failure modes. What to defend.
Oct 18, 20264 min read
AI Security
What are the main generative AI security risks?
NIST names 12 generative AI risks and OWASP ranks 10 for LLM apps. Here is how the two lists overlap and which ones engineers should handle first.
Oct 18, 20264 min read
AI Security
What challenges does generative AI bring to application security teams?
GenAI adds new app risks, such as prompt injection and excessive agency, plus AI written code to review. Map them to OWASP and NIST, then set clear rules.
Oct 18, 20264 min read
AI Security
How do you build a security roadmap for frontier AI use?
Build a frontier AI security roadmap on NIST's four functions, with an inventory first and OWASP risks as a test list. Here is a sequence to start now.
Oct 18, 20265 min read
AI Security
What should a security checklist for frontier AI models include?
A practical checklist for teams adopting highly capable AI models: data, access, agents, supply chain, testing and monitoring, drawn from NIST and OWASP.
Oct 18, 20264 min read
AI Security
What is federated learning and does it protect privacy?
Federated learning trains a shared model without moving raw data to one place. It reduces exposure but does not guarantee privacy. See what else you need.
Oct 18, 20264 min read
AI Security
What is explainable AI and where does it matter in practice?
Explainable AI means a system can give reasons for its outputs that people can understand and trust. NIST's four principles show how to test that claim.
Oct 18, 20264 min read
AI Security
What Is Explainable AI and How Do You Test an Explanation?
NIST frames explainable AI with four principles: explanation, meaningful, accuracy and knowledge limits. Learn how to test whether your system meets them.
Oct 18, 20264 min read
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