AI Security Cheatsheets for Enterprise Security Teams

A practical 2026 reference collection for enterprise security teams, covering:

• AI Agent Guardrails - How to enforce hard controls across agent inputs, processing, and outputs to stop prompt injection, credential leakage, and data exfiltration

• Shadow AI - How to discover and govern unsanctioned AI tools, coding assistants, and agents before they become a compliance or security incident

• MCP Security - How to defend MCP servers against tool poisoning, prompt injection, rug pulls, and credential leaks across the full execution layer

• AI Security Posture Management (AI-SPM) - How to discover, assess, and remediate security risks across AI models, pipelines, agents, and supporting infrastructure

• AI Agent Red Teaming - How to simulate real-world attacks against agentic systems, map the attack surface, and build defense-in-depth across workflows

• AI Agent Identity - How to govern agent identities, scope permissions, enforce access boundaries, and prevent privilege escalation across multi-agent environments

• Agentic AI Security Terms — A quick-reference glossary of the most important concepts, threats, and controls in agentic AI security, from prompt injection and memory poisoning to agent identity and inter-agent trust.

• Essential Guardrails for Agentic AI Security — A practical guide to the input, execution, and output controls that protect AI agents from jailbreaks, sensitive data exposure, unsafe tool use, and policy violations.

• The 3-Layer Framework for Securing Production LLMs — A blueprint for implementing input, output, and system-level guardrails that reduce hallucinations, prevent data leaks, enforce governance, and secure production AI applications.