Maintained reading path from EnhanceLearning.AI — practitioner-grade articles for engineers, architects, and technology leaders building production AI-native systems.
Topic on the site: Security & Governance · Full library: enhancelearning.ai/articles
A curated reading path for Security & Governance. It is not a code SDK — it points to the foundation deep-dives on EnhanceLearning.AI so you can align on concepts, critique designs, and ship production systems that hold up.
These articles map AI security vs traditional AppSec, model-level vs system-level controls, prompt-injection basics, and the identity-policy-enforcement stack for AI-native products.
Security architects, platform leads, and product owners shipping tool-using AI.
- What AI Security Actually Covers Beyond Model Safety — AI security spans identity, permissions, data flows, and runtime policy — not just model alignment and output filters. A scope map for architects.
- The Difference Between AI Security and Traditional Application Security — What carries over from AppSec into AI systems — and what breaks when LLMs and agents join the request path. Avoid blind playbook reuse.
- The Difference Between Model-Level Safety and System-Level Security — Model safety filters harmful outputs; system security controls what your architecture can do. Why provider alignment is not your production posture.
- The AI-Native Security Stack: Identity, Policy, and Enforcement Layers — A three-layer security stack for AI systems: agent identity, policy definition, and runtime enforcement. A shared model for engineering and governance.
- Prompt Injection, Tool Abuse, and AI Security Basics — How prompt injection and tool abuse show up in production AI systems, and the controls that belong in code: isolation, allowlists, human gates, and monitoring.
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