The Pipeline Security Model
An AI pipeline is a series of transformations from input to output, with an agent orchestrating the process. Security failures happen at the boundaries: where data enters the pipeline, where components hand off to each other, and where outputs leave the system.
Secure Input Handling
Every input to your AI pipeline should be treated as potentially hostile. This means:
- Sanitize and validate all inputs before they reach the LLM context
- Use structured data formats (JSON Schema) to constrain what users can provide
- Apply content classification to detect injection attempts early
- Log all inputs for audit and debugging purposes
Context Isolation
One of the most effective security patterns is strict context isolation. Trusted instructions from your system prompt should never be in the same context as untrusted user content — or at minimum, should be clearly delineated.
// UNSAFE: mixing trusted and untrusted
const prompt = systemPrompt + userInput + externalData;
// SAFER: structured separation
const prompt = {
system: systemPrompt, // trusted
user: sanitize(userInput), // validated
context: sandbox(externalData) // sandboxed
};Tool Call Validation
Before executing any tool call an agent proposes, validate it against a policy ruleset:
function validateToolCall(tool, params, context) {
// Check tool is in allowlist
if (!allowedTools.includes(tool)) throw new SecurityError();
// Check params match schema
validateSchema(tool, params);
// Check against current context permissions
if (!context.permissions.includes(tool)) throw new PermissionError();
// Rate limit check
if (rateLimiter.exceeded(tool)) throw new RateLimitError();
return true;
}Output Sanitization
Agent outputs that flow to users or other systems must be sanitized to prevent secondary attacks. An agent compromised by prompt injection might try to embed malicious content in its outputs.
Observability as Security
You can't secure what you can't see. Comprehensive observability is a security requirement, not just an operational one. Log every LLM call, every tool invocation, every context state change.