Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

The types of AI guardrails are input guardrails, output guardrails, security guardrails, ethical guardrails, and operational guardrails, each positioned at a different failure point across an inference pipeline. Gartner’s research found that 30% of generative AI projects don’t survive past the proof-of-concept stage, with weak risk controls cited as the leading reason. Most of those projects weren’t badly built. The models worked. The gaps were in what sat around them.

Generative AI and sophisticated social engineering have reshaped the cybersecurity landscape in 2026. Traditional "castle-and-moat" defenses centered on the Secure Email Gateway (SEG) are increasingly pressured by machine-scale attacks designed to bypass static filters. As organizations shift toward Integrated Cloud Email Security (ICES) models, a new technical and psychological barrier appears: the "black box" problem of defensive AI.

AI risk doesn’t live in the model. It lives in the APIs behind it. Every AI interaction triggers a chain of API calls across your environment. Many of those APIs aren’t documented or tracked. That’s your real exposure. Shadow API discovery gives you visibility into those hidden endpoints, so you can find them before attackers do. If you don’t know which APIs your AI relies on, you can’t secure the system.

Someone is scanning your LLM infrastructure right now. They are not waiting for you to finish your security review. Between October 2025 and January 2026, GreyNoise’s honeypot infrastructure captured 91,403 attack sessions targeting exposed LLM endpoints. These were two distinct campaigns systematically mapping the expanding attack surface of misconfigured AI deployments. Your team is moving fast on AI. LLM servers are going live, inference APIs are being connected, MCP endpoints are being spun up.