AI Automation Risk Audit
Identifies vulnerabilities, unauthorized data flows, model exposure, and logic failure modes across automated agent and workflow pipelines.
Why This Audit Exists
Organisations are deploying LLM agents, automated webhook workflows, and RAG search pipelines into internal operations without realizing that language models introduce an entirely new threat surface: prompt injection, unauthorized API execution, and sensitive data leakage.
This audit conducts a targeted 3–5 business day inspection of your prompts, tool permissions, and vector data flow to identify vulnerabilities before an agent triggers unauthorized mutations or exposes proprietary data.
What Is Inspected (Scope)
1. Prompt & System Instructions Architecture
Analysis of system prompts for direct and indirect prompt injection vulnerabilities, instruction hierarchy bypasses, and delimiter leakage.
2. Tool Permissions & Blast Radius
Review of API keys, webhooks, and database read/write permissions granted to the AI model. Ensuring destructive actions require deterministic human approval.
3. Data Privacy & Vector Flow
Verification that user PII or sensitive corporate IP is sanitized before ingestion into third-party LLM providers or unencrypted vector databases.
4. Output Validation & Guardrails
Inspection of downstream parsers to ensure malformed model output cannot trigger SQL injections, SSRF, or uncontrolled loops.
What Is Excluded
This audit focuses specifically on automated workflows and LLM agent boundaries. It does not include full network penetration testing, training deep neural networks from scratch, or re-writing your entire software codebase.
Deliverables Included:
- ✔ Executive Summary
- ✔ Technical Findings & Agent Logic Review
- ✔ Data Exfiltration & Exposure Risk Assessment
- ✔ Priority Hardening Recommendations
- ✔ 90-Day Action Plan
- ✔ 60-Minute Senior Engineer Debrief