Why red-teaming your AI protects your brand and your users
Why traditional testing isn’t enough Most organizations validate AI systems with internal QA or benchmark datasets, but these don’t simulate adversarial conditions. Real users (or bad actors) may try prompts that testers never imagined — seeking confidential data, bypassing safety filters, or eliciting unethical instructions. Recent headlines show what happens when these safeguards aren’t in […]
Building LLMs with sensitive data: A practical guide to privacy and security
Know your data: what “sensitive” means in practice Why this matters for LLMs: leakage is real Modern models can memorize and later regurgitate rare or sensitive strings from training corpora. Research has demonstrated the extraction of training data from production LLMs via carefully crafted prompts, and a growing body of work on membership-inference risks. The […]
Ensuring data privacy and security in data annotation
Protect sensitive data during the annotation process. Discover best practices for ensuring data privacy while maintaining data quality.