Security-First Architecture for AI Applications | Matthew Gribben
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Security-First Architecture for AI Applications
Building AI systems with security as a foundational concern, from input validation to output sanitization and model isolation.
November 12, 20254 min read
AI systems are attack surfaces. Every prompt is potential input injection. Every model response is potential data leakage. Every tool call is potential privilege escalation. If you're building AI applications without security as a foundational concern, you're building a breach waiting to happen.
Here's how to do it right.
The AI Security Threat Model
Traditional applications have well-understood threat models. AI applications introduce new attack vectors:
Attack Vector
Traditional App
AI Application
Input injection
SQL injection
Prompt injection
Data leakage
Database exposure
Training data extraction
Privilege escalation
Auth bypass
Tool permission abuse
Denial of service
Resource exhaustion
Infinite loops, token bombing
Layer 1: Input Sanitization
Every input to an AI system must be validated and sanitized:
Perfect security is impossible. Prompt injection, in particular, is an unsolved problem—there's no foolproof way to distinguish "data" from "instructions" when everything is natural language.
But "hard" doesn't mean "don't try." These layers dramatically reduce your attack surface and make successful attacks much harder and more detectable.
Build AI systems like you're building banking software. Because increasingly, you are.
MG
Matthew Gribben
Chief Technology Officer writing about AI systems, software architecture, cyber security, cryptography, and the practical realities of technology leadership.