AI Application Security Controls
AI Application Security Controls are defensive mechanisms designed to protect the infrastructure, APIs, and application layers surrounding AI models. Since modern AI systems are embedded into applications, agents, and APIs, they inherit risks from both traditional web security and AI-specific vulnerabilities such as prompt injection, model abuse, and data leakage. These controls enforce authentication, authorization, monitoring, and guardrails to ensure secure AI system deployment.
Mutual Service Authentication (M2M)
Use mutual TLS (mTLS) or OAuth2 scoped tokens for services communicating with AI models to prevent unauthorized service access and lateral movement.
Granular RBAC for Model Configuration
Restrict who can modify critical model parameters such as temperature, top_p, or system prompts.
Semantic Input Filtering
Use a dedicated moderation or classification model to analyze user prompts before sending them to the main LLM.
Structured Output Enforcement
Force the model to produce structured responses using strict schemas such as JSON to prevent arbitrary output execution.
Token Rate Limiting & Quotas
Limit the number of requests and token usage per user to prevent denial-of-service attacks and financial abuse.
Differential Error Handling
Return generic error messages to users while logging detailed errors internally to prevent information leakage.
AI-Aware WAF Rules
Configure Web Application Firewalls with rules specifically designed to detect AI prompt attacks.
Prompt Isolation & Delimiters
Separate user inputs from system instructions using delimiters or XML-style tags.
Agent Tool Permission Control
Restrict which tools an AI agent can access and define strict execution policies.
API Key Security
Secure API keys used by AI applications to prevent unauthorized access.
Context Window Protection
Prevent attackers from pushing system prompts out of the context window using extremely long prompts.
Output Content Filtering
Scan generated responses to prevent leakage of sensitive data or harmful content.
Hands-on Lab Environment
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Apply the concepts learned in the AI Application Security Controls course within our virtual terminal environment.
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