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.
Deep dives, practical guides, and incident analyses from engineers who build Safeguard. No fluff, no vendor FUD — just what you need to ship secure software.
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.
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.
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.
A practical checklist for teams adopting highly capable AI models: data, access, agents, supply chain, testing and monitoring, drawn from NIST and OWASP.
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.
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.
NIST frames explainable AI with four principles: explanation, meaningful, accuracy and knowledge limits. Learn how to test whether your system meets them.
Judge an AI coding assistant by where code and secrets go, what it may run and how you can audit it. A test plan for data flow, permissions and output.
Vendor AI security claims are easy to make and hard to verify. Ask for tests you can run, scope in writing, failure examples and evidence behind numbers.
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