Security Review for AI Automation: A Practical Checklist
Overview
Security reviews should be proportional: risk-tiered controls for internal ops automation vs customer-facing agents.
Quick definition
Security review for AI automation covers data classification, prompt injection surfaces, tool egress allowlists, secret rotation, and model vendor subprocessors.
Definition
A practical AI security review examines data classification, model providers, prompt injection surfaces, tool permissions, secret storage, and incident response for automation failures.
Why it matters
AI expands attack surface: tools can exfiltrate data if mis-scoped; prompts can be manipulated if user content is unconstrained.
Core framework
Step-by-step model as TypeScript interfaces (machine-readable checkpoints).
Data flow diagram
/**
* Data flow diagram
* Where prompts and retrieved docs travel; where outputs land.
*/
export interface CoreFrameworkStep1DataFlowDiagram {
/** Order in the core framework (0-based) */
readonly stepIndex: 0;
/** Display title for this step */
readonly title: "Data flow diagram";
/** Narrative checkpoints as published in the guide */
readonly narrative: readonly string[];
}
export const CoreFrameworkStep1DataFlowDiagram_NARRATIVE: readonly string[] = [
"Where prompts and retrieved docs travel; where outputs land."
] as const;Least privilege
/**
* Least privilege
* Separate service accounts per integration; rotate keys.
*/
export interface CoreFrameworkStep2LeastPrivilege {
/** Order in the core framework (0-based) */
readonly stepIndex: 1;
/** Display title for this step */
readonly title: "Least privilege";
/** Narrative checkpoints as published in the guide */
readonly narrative: readonly string[];
}
export const CoreFrameworkStep2LeastPrivilege_NARRATIVE: readonly string[] = [
"Separate service accounts per integration; rotate keys."
] as const;Detailed breakdown
Logic sections encoded as Python functions with structured narrative payloads.
Red teaming prompts
def logic_block_1_red_teaming_prompts(context: dict) -> dict:
"""Operational logic: Red teaming prompts"""
# Narrative steps from the guide (logic section)
paragraphs = ["Test jailbreaks specific to your templates and tools."]
return {
"heading": "Red teaming prompts",
"paragraphs": paragraphs,
"context_keys": tuple(sorted(context.keys())),
}Technical patterns
Threat modeling for agents
- STRIDE on tool-calling paths: spoofed webhooks, tampered documents, over-privileged API keys.
- Separate network egress for model calls vs internal DB.
Code examples
Egress allowlist check
Outbound fetch restricted to approved domains.
const ALLOW = new Set(['api.openai.com', 'api.anthropic.com']);
export function assertAllowedUrl(url) {
const host = new URL(url).host;
if (!ALLOW.has(host)) throw new Error('egress_denied');
}System architecture
[Architecture diagram + data flow]
→ [Classification]
→ [Control matrix: authZ, secrets, network]
→ [Residual risk + compensating controls]
→ [Sign-off artifacts]Real-world example
A healthcare vendor blocked external URLs in support chat inputs after tests showed credential phishing attempts via prompts.
Common mistakes
- Shared API keys across tenants.
- No rollback plan when a model provider has an incident.
Related topics
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