Agentic AI Security & Responsible Deployment Guide
Agentic AI is not "just another chatbot." Agents can plan multi-step workflows, call tools and APIs, persist and evolve memory, coordinate with other agents, and take real-world actions with little or no human oversight.
That autonomy makes agents powerful—and also creates an entirely new attack surface. You must treat an agent as a non-human identity (NHI) operating with real credentials inside your environment.
This guide consolidates best practices into a single, opinionated reference for engineering and security teams. It aims to be:
- Provider-neutral (fits OpenAI, Anthropic, local models, etc.)
- Pattern-driven (concrete architectural patterns you can reuse)
- Security-first (Zero Trust for agents: treat model + memory + tools as untrusted)
- Aligned with established frameworks (NIST AI RMF, OWASP LLM Top 10, standard SDLs)
Table of Contents
1. Summary Checklist
Quick-reference checklist covering all key security requirements.
2. Agentic Risk Landscape
Memory poisoning, tool misuse, privilege escalation, indirect prompt injection, and more.
3. Core Design Principles
Zero Trust assumptions, orchestration as policy brain, constrained agency, and defense-in-depth.
4. Secure Architecture & Patterns
Reference architecture, plan-verify-execute pattern, controlled breakpoints, and multi-agent consensus.
5. Identity & Access Control
Agent identity, RBAC/ABAC, just-in-time privilege, and credential handling.
6. Frontend & UX Security
Application security, XSS-resistant rendering, safe UX design, and prompt injection-aware UI.
7. Orchestration & Tool Security
Policy enforcement, tool design, mediation layers, and MCP/plugin governance.
8. Data, RAG & Memory Security
Data classification, RAG integrity, memory poisoning defenses, and PII handling.
9. Guardrails & Responsible AI
Three-phase guardrail model, RAI harm categories, and domain-specific constraints.
10. Infrastructure & Sandboxing
Execution isolation, Kubernetes security, model gateways, and cloud provider recommendations.
11. Monitoring & Incident Response
Structured telemetry, behavioral monitoring, automated safeguards, and AI-specific IR.
12. Secure SDLC & Testing
AI-aware development lifecycle, automated security testing, and adversarial red teaming.
Need Help Securing Your Agentic AI Systems?
Casaba Security has extensive experience testing and securing AI/LLM systems for the world's leading technology companies. Contact us to learn how we can help secure your agentic AI deployments.
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