AI Security Audit Cost: 2026 Red-Teaming Price Guide

Published AI security audit prices in 2026 run from $4,500 at Startrise for a scoped single-system test with fixes included, through $10,000 to $40,000 at specialist firms, to the low six figures for multi-week manual engagements. Schellman publishes a $16,000 floor for one engagement. Scope sets the price, and so does whether remediation and a retest are in the quote.

Disclosure: Startrise sells this. The Startrise AI Security Audit is the $4,500 row in the table below. So every number here is labelled with who published it, plus a link and a date. Ours too. Use it to sanity-check any quote, from us or anyone else.

Key Takeaways

• Published prices run from $4,500 (Startrise) through B.D. Emerson’s $10,000-$40,000 AI/LLM range (Schellman’s floor is $16,000) and up to low six figures for multi-week manual work
• Five things set scope: what the system can do, what data it reaches, whose model it runs, where it’s exposed, and what evidence you need
• Retest is often missing from the quote or sold separately
• For most startups, the enterprise security questionnaire is the real deadline, not the EU AI Act

What do AI security audits cost? Published prices, side by side

Published AI security audit prices range from $4,500 (Startrise, fixed, one system) to the low six figures (Mindgard’s range for two-to-six-week manual engagements), and most specialist prices sit between $10,000 and $40,000. Every row below links to a page where the vendor published the number itself. Prices checked 2026-10-21; vendor pages change, so re-check before you budget.

Most of the published single-system prices above sit between $10,000 and $40,000. Below that, you’re buying either an automated scan or a deliberately narrow scope (ours is one system in 1-2 weeks). Above it, you’re buying agents with real access, regulated data or multi-week manual work. B.D. Emerson puts the gap plainly: “a chatbot wrapper is days of work, while an agent holding tools, credentials, and retrieval is weeks.”

We left out ranges we couldn’t trace to a named source. Repello’s pricing guide, for example, is useful on line items, but most of its ranges aren’t attributed to a named firm, so they’re not in the table.

What’s actually in the price?

An AI security audit quote should cover five line items plus one deliverable: scoping and threat model, adversarial testing days, a findings report, remediation and a retest, plus an evidence pack. Two quotes with the same headline number can buy different outcomes, depending on which lines are in.

• Scoping and threat model. Which flows, which data, which tools. Weak scoping is how two weeks of testing end up on the marketing chatbot instead of the agent with database access.
• Adversarial testing days. The bulk of the bill. As Mindgard puts it, “Labor is the main cost driver.”
• Findings report with reproduction steps. Severity-ranked, and every finding reproducible by your own engineers.
• Remediation. Who writes the fixes? Many quotes end at the report.
• Retest. Does anyone confirm the fix worked? Repello lists retest fees among the “hidden cost categories your CFO will ask about.” DSE sells the retest as an add-on to its AI Security X-Ray (its first-step assessment) and includes a 30-day retest in its Red Team Sprint. Schellman’s page doesn’t mention retesting.
• Evidence pack. The document your buyer’s security team or auditor actually reads.

Our $4,500 includes remediation PRs, a retest and the evidence pack. When findings point at missing runtime oversight, that work continues as AI monitoring and guardrails, from $2,500 in 1-3 weeks.

When you compare quotes, price the outcome “fixed and verified,” not “found.” A cheaper report with no fixes and no retest isn’t cheaper once your engineers spend a sprint on remediation and you pay again for someone to verify it.

What sets your scope (and your price)? The five drivers

Five factors set the scope and price of an AI security audit: what the system can do, what data it reaches, whose model it runs, where it’s exposed, and what evidence you need afterwards. Price follows test days, and test days follow attack surface. Map your architecture to the OWASP Top 10 for LLM Applications (2025) and you know which categories a competent tester has to cover. Each driver below names the categories it pulls into scope.

What can the system do?

A text-only chatbot puts three categories in scope: LLM01 Prompt Injection, LLM07 System Prompt Leakage and LLM09 Misinformation. Once it calls tools or takes actions, add LLM06 Excessive Agency and LLM05 Improper Output Handling, because model output now becomes a query, an email or an API call. This is the biggest single jump in test days. Our risk-tiered approval workflow guide covers the defensive half: which agent actions should never run without a human.

What data can it reach?

Retrieval adds LLM08 Vector and Embedding Weaknesses. Personal or health data makes LLM02 Sensitive Information Disclosure the priority.

Here’s what that looks like in practice. On a document-intelligence platform we built for a client under NDA, the redaction review step shows “30 detections across a handwritten clinical note, each one approvable before output (synthetic demo record),” alongside audit logging, de-identification and HIPAA-aligned handling. That was a platform build, not a red-team engagement. The point: every one of those controls is something a tester has to try to get around.

Whose model and supply chain?

A hosted API model keeps this driver small. Fine-tunes, third-party models and plugins add LLM03 Supply Chain and LLM04 Data and Model Poisoning: where did the weights, datasets and plugins come from, and who can tamper with them?

Where does it run, and what does it cost to run?

Testing production rather than staging needs tighter rules of engagement and coordination with whoever is on call. Any public endpoint also brings LLM10 Unbounded Consumption into scope. A denial-of-wallet attack that runs up your inference bill is worth testing before someone else does.

What evidence do you need afterwards?

A customer questionnaire, SOC 2 or ISO 42001 control mapping, or EU high-risk documentation. Each adds reporting time on top of testing time.

Notice what’s missing: headcount. DSE doesn’t hedge: “Company size is not a pricing factor, surface area is.” A ten-person company with a tool-using agent on customer data needs more test days than a large one with an internal FAQ bot.

AI security audit vs pentest vs SOC 2: which one are you pricing?

An AI security audit, a web app penetration test and a SOC 2 Type 2 audit are three different purchases: the first tests the AI feature against adversarial use, the second tests the application layer, and the third certifies that security controls operated over time. If you searched “security audit cost,” you may be pricing the wrong one:

A clean web pentest says nothing about prompt injection. A SOC 2 report certifies controls; it isn’t adversarial testing. If you sell an AI feature to enterprise buyers, they may ask for more than one, so price each for what it proves.

One more fork. If your app was AI-built rather than AI-powered, the risk lives in the generated code, not in a model. That’s a code audit: our vibe code cleanup starts at $2,500, and the cleanup specialist hiring guide explains what you’re buying.

Do you need one for compliance? The 2026 answer

Usually not directly: under the EU AI Act, the explicit adversarial-testing duty falls on providers of general-purpose AI models with systemic risk, not on most startups building apps on top of them. The facts:

• Article 55(1)(a) of the EU AI Act requires “conducting and documenting adversarial testing of the model with a view to identifying and mitigating systemic risks” (Art. 55). It binds providers of general-purpose AI models with systemic risk, not most teams building apps on top of them.
• Article 15(5) requires high-risk AI systems to be “resilient against attempts by unauthorised third parties to alter their use, outputs or performance,” naming data poisoning, model poisoning and adversarial examples (Art. 15).
• The Digital Omnibus was published in the Official Journal on 24 July 2026 (FPF) and is now in force. Annex III high-risk obligations “will now take effect on 2 December 2027 instead of 2 August 2026,” and Annex I obligations follow on 2 August 2028 (Orrick).

So any pitch built on an “August 2026 deadline” is out of date. For most startups the real deadline is the AI security questionnaire an enterprise buyer sends during procurement, and test findings plus fixes are what answer its AI security questions. For the paperwork side (inventory, risk classification, controls), see AI compliance readiness.

This is not legal advice. We’re engineers, not lawyers; confirm your classification with counsel.

Why is skipping it the expensive option?

Skipping the test is usually the expensive option: in IBM’s Cost of a Data Breach 2025 study, 97% of organizations that reported a breach of an AI model or application had no AI access controls in place, and the global average breach cost $4.44 million.

The study covered breaches at 600 organizations. Of those, 13% reported breaches of AI models or applications, and “of those compromised, 97% report not having AI access controls in place” (IBM, 2025). Read that carefully: 97% of the breached organizations, not of all firms.

Organizations with high shadow-AI use paid $670,000 more per breach than those with little or none, and 63% of breached organizations either had no AI governance policy or were still writing one.

We won’t turn those figures into an ROI multiple. The comparison is simple enough: a single-system audit costs a small fraction of one average breach.

Can you red team your own LLM app?

Partly: open-source tools like Promptfoo, Garak and PyRIT automate known attacks against an LLM app, but they cost tokens to run, miss chained business-logic attacks and don’t write the fixes. They’re free to license, not free to run. Netguru ran Promptfoo against Omega, its own “production-grade AI sales agent,” and estimated the token bill:

• Full OWASP GenAI Red Team config: ~$1,239 (24,780 probes)
• Full OWASP LLM Top 10 config: ~$630
• Simplified OWASP LLM Top 10 config: ~$127

Their basis: “~5,000 tokens per probe and $10 per 1M tokens.” These are a vendor’s own estimates, not invoices.

Automation catches known patterns cheaply. A human finds the chained, business-logic attacks that only work in your real flows: the injected document that steers the agent into calling the refund tool. And no scanner writes the fixes.

Where DIY is enough: an internal tool with no tools, no actions and no sensitive data. Run the scan and fix what it finds. But anything customer-facing that takes actions or touches personal data needs a human-led test.

The short version

The cost of an AI security audit is set by scope, not company size. Get remediation and a retest written into the quote, and match the test to the evidence your buyer needs. We hold ourselves to the same standard. We audited the AI judges in our own LLM frontend benchmark and published the results, bugs included.

Most quotes price finding problems. Ours prices fixing them: the Startrise AI Security Audit is fixed from $4,500, one system, 1-2 weeks, with remediation PRs, a retest and an evidence pack your buyer will accept. For public-facing AI that ships often, Continuous AI Security runs $2.5k/mo with retests on every release. Send us your architecture and we’ll scope it, or tell you a scan is enough.