Most organizations that ask for an “AI security audit” are picturing a penetration test against a web app with a chatbot bolted on. That’s not what breaks in production. The real risk surface in an LLM-based system is different enough from classic application security that a generic security audit will miss most of it.
The pain point: your existing security program wasn’t built for this threat model
Application security teams are good at what they were trained for: injection attacks against structured inputs, authentication flaws, dependency vulnerabilities. LLM systems introduce a different category of risk entirely — one where the “input” is unstructured natural language the model was designed to obey, and the attacker doesn’t need credentials, just a cleverly worded document, email, or webpage the model happens to read.
This isn’t hypothetical. 77% of businesses reported an AI-related security incident, at an average cost of $4.88 million per breach — the highest average breach cost on record. Over 30% of AI-related breaches through 2026 involved prompt manipulation specifically, and 60% of AI-driven data-privacy incidents in the same period were tied to prompt-manipulation techniques. Real, disclosed vulnerabilities carry the point home: critical CVEs in Microsoft Copilot (CVSS 9.3), GitHub Copilot (9.6), and Cursor IDE (9.8), plus EchoLeak — the first documented zero-click prompt injection to cause real data exfiltration from a production AI system.
The threat model is not the same as a normal web app
- Prompt injection — untrusted input (a document, an email, a webpage the model reads) overriding the system’s intended instructions.
- RAG data leakage — a retrieval-augmented system surfacing documents or records a user should never have been able to see, because retrieval access controls don’t match the underlying data’s real permissions.
- Over-broad tool access — an AI agent wired to take real actions (send an email, query a database, call an API) with far more permission than the task in front of it actually requires.
- Silent model drift — behavior changing after a model or prompt update in ways nobody is monitoring for.
What a real audit produces
A proper LLM/AI security audit produces three concrete artifacts, not a generic risk score: a threat model specific to how your organization’s LLM and RAG systems are actually wired; a data-governance and access-control review that traces what each system can see and touch; and a written findings report with a prioritized remediation roadmap — ranked by real exploitability and business impact, not by how alarming each finding sounds.
Why this usually gets found late
Most organizations discover these gaps only after an incident, because the people building the AI feature and the people who understand its security exposure are rarely the same people, and the system shipped faster than the security review cycle around it. A focused audit closes that gap before it becomes an incident, typically in 2–4 weeks for a single system.
This is the scope of Systemsgrit’s AI / LLM Security Audit. For an organization further along — where the audit needs to feed directly into a governance framework and a shipped fix, not just a report — that’s the shape of the Transformation Program instead.
Frequently asked questions
What’s the difference between an LLM security audit and a regular penetration test?
A traditional pentest targets structured attack surfaces: authentication, injection, misconfigurations. An LLM security audit specifically models prompt injection, RAG data-leakage paths, over-broad agent tool permissions, and drift — risks that don’t exist in the same form in a conventional web application.
How long does an LLM/AI security audit take?
Typically 2–4 weeks for a single system, delivered as a fixed-fee engagement ($12K+ depending on scope) producing a threat model, an access-control review, and a prioritized remediation roadmap.
We already have a security team — why would we need this?
Most application security teams are highly capable but were not trained on LLM-specific threat classes like prompt injection or RAG leakage, simply because those risks didn’t exist in their prior stack. This audit is designed to complement an existing security function, not replace it.
What happens after the audit — do you fix the issues too?
The audit itself delivers findings and a roadmap. If you need the fixes actually implemented and folded into a broader governance framework, that’s the scope of the Transformation Program instead.