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
Data flow diagram
Where prompts and retrieved docs travel; where outputs land.
Least privilege
Separate service accounts per integration; rotate keys.
Detailed breakdown
Red teaming prompts
Test jailbreaks specific to your templates and tools.
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
PrimeAxiom aligns automation designs to your security requirements—book a joint review with your IT team.