Research, best practices, and product updates from the AgentAIShield team.
8 sections covering everything from threat landscape to compliance frameworks and implementation. First 3 sections free — unlock all with your email.
Prompt injection is no longer a theoretical concern — it's the most actively exploited vulnerability class in production AI systems. We analyzed 2.3 million flagged requests across our platform to understand how attacks are evolving, what they're targeting, and what actually stops them.
Most developers treat their AI agents like they treat their API integrations: build it, deploy it, and check in if something breaks. The problem? AI agents are fundamentally different from deterministic code. Their behavior is emergent, context-dependent, and increasingly targeted by adversarial actors.
Today we're launching Agent Trust Score™ — a 0-100 reputation score for AI agents, computed continuously from observed behavior, data handling patterns, and injection resistance. Think of it as a FICO score, but for your AI agents.
We've seen it happen dozens of times: a customer's AI agent designed to answer support questions starts leaking SSNs from its context window, or an automated researcher agent exfiltrates API keys it was given for unrelated tasks. Here's how it happens and how to stop it.
Manual security testing doesn't scale for AI systems. AgentAIShield's automated red teaming framework runs thousands of adversarial attack scenarios daily, discovering vulnerabilities before attackers do.
The regulatory landscape for AI systems is converging in 2026. NIST AI RMF, EU AI Act, GDPR, and CCPA all impose requirements on how AI agents handle data, make decisions, and maintain transparency.