Resources
Agentic AI Security Guide
Agentic AI is not just another chatbot. Agents 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 creates an entirely new attack surface. You need to treat an agent as a non-human identity operating with real credentials inside your environment.
This guide is a single, opinionated reference for engineering and security teams building and deploying agentic AI systems. It is provider-neutral, pattern-driven, security-first, and aligned with established frameworks including NIST AI RMF, OWASP LLM Top 10, and standard SDLs.
Download PDFWhat's covered
12 sections, zero fluff
Each section is a standalone reference you can read on its own or work through in order. Start with the checklist if you want the short version.
1. Security Checklist
Quick-reference checklist covering all key security requirements for agentic AI systems.
2. Agentic Risk Landscape
Memory poisoning, tool misuse, privilege escalation, indirect prompt injection, and the new threats agents introduce.
3. Core Design Principles
Zero Trust assumptions, orchestration as policy brain, constrained agency, and defense-in-depth.
4. Secure Architecture and Patterns
Reference architecture, plan-verify-execute pattern, controlled breakpoints, and multi-agent consensus.
5. Identity and Access Control
Agent identity, RBAC/ABAC, just-in-time privilege, and credential handling.
6. Frontend and UX Security
Application security, XSS-resistant rendering, safe UX patterns, and prompt injection-aware UI design.
7. Orchestration and Tool Security
Policy enforcement, tool design, mediation layers, network controls, and MCP/plugin governance.
8. Data, RAG, and Memory Security
Data classification, RAG integrity, memory poisoning defenses, PII handling, and database mediation.
9. Guardrails and Responsible AI
Three-phase guardrail model, RAI harm categories, and domain-specific constraints.
10. Infrastructure and Sandboxing
Execution isolation, Kubernetes security, model gateways, supply chain management, and cloud provider recommendations.
11. Monitoring and Incident Response
Structured telemetry, behavioral monitoring, automated safeguards, and AI-specific incident response.
12. Secure SDLC and Testing
AI-aware development lifecycle, automated security testing, adversarial red teaming, and continuous evaluation.
Building with agentic AI? Let's talk security.
We test AI systems for a living. If you're deploying agents in production, we can help you find the gaps before someone else does.
Get in touch