Tag: AI security
Data Strategy for Generative AI: Build Quality, Control Access, and Secure Your Inputs
A strong data strategy for generative AI focuses on quality, access, and security. Without it, AI hallucinates, leaks data, and fails to deliver value. Learn what works-and what doesn't.
Incident Response Playbooks for LLM Security Breaches: What Works and What Doesn’t
LLM security breaches require specialized response plans. Learn how incident response playbooks for prompt injection, data leakage, and safety breaches work - and why traditional cybersecurity tools fail to stop them.
Databricks AI Red Team Findings: How AI-Generated Game and Parser Code Can Be Exploited
Databricks AI red team uncovered critical vulnerabilities in AI-generated game and parser code, showing how prompt injection, data leakage, and hallucinations can be exploited. These aren't theoretical risks-they're happening in real systems today.
Preventing RCE in AI-Generated Code: How to Stop Deserialization and Input Validation Attacks
AI-generated code often contains dangerous deserialization flaws that lead to remote code execution. Learn how to prevent RCE by replacing unsafe formats like pickle with JSON, validating inputs, and securing your AI prompts.
Red Teaming for Privacy: How to Test Large Language Models for Data Leakage
Learn how to test large language models for data leakage using red teaming techniques. Discover real-world risks, free tools like garak, legal requirements, and how companies are preventing privacy breaches.
