AI security audits vary wildly in scope and price. Here is what a real audit costs, what it should include, and how to measure the ROI.
Ask five vendors what an AI security audit costs and expect five different answers, spanning a range wide enough to make budgeting feel like guesswork. The honest answer is that cost depends entirely on scope, and most behavioral health CTOs are pricing the wrong scope for what they actually need.
A basic HIPAA risk assessment alone can run anywhere from a few thousand dollars to over 25,000 dollars depending on whether it is a template based tool or a full consultant engagement, and that figure does not even touch AI specific review work. Broader HIPAA compliance programs for small and mid sized healthcare software companies typically run in the 25,000 to 100,000 dollar range annually, depending on how much of that work is handled internally versus outsourced. None of those numbers were built with AI data flows, vendor BAAs, or model output risks in mind, which is exactly the gap an AI specific security audit needs to close. The confusion usually comes from comparing quotes that are not actually pricing the same thing. A vendor quoting a low number is often pricing a standard HIPAA risk assessment with AI mentioned as a checklist item. A vendor quoting a much higher number may be pricing a full technical review that includes code level testing, vendor verification across every AI service in use, and adversarial testing of the AI feature itself. Those are fundamentally different scopes wearing the same label, and comparing them on price alone misses the point entirely.
A quote that only covers a generic HIPAA checklist is not pricing an AI security audit, it is pricing a compliance audit that happens to mention AI in passing. A real AI security audit needs to price in a few specific components. It needs a full map of every AI data flow in the product, not just the flagship feature. It needs vendor verification covering BAAs, configuration, and subprocessor chains for every AI service in use. It needs adversarial testing of the AI layer itself, checking for cross patient data leakage, prompt injection, and output failures, not just a paperwork review. And it needs a documented, prioritized fix sequence at the end, not a spreadsheet of findings with no plan attached. Each of those components takes real time and real technical expertise to do properly, which is a large part of why price varies so widely between vendors. Mapping data flows across every AI touchpoint in a codebase, including retrieval systems and embeddings that rarely appear on an architecture diagram, is not a task that can be automated away with a template. Neither is adversarial testing, which requires someone who actually knows how to attempt cross patient contamination or prompt injection against a specific feature, not just read through its documentation.
Picture two quotes landing on the same CTO's desk in the same week. One vendor quotes 8,000 dollars for a HIPAA risk assessment covering policies, training, and a general technical safeguards checklist. Another quotes 45,000 dollars for a six week engagement covering AI data flow mapping, vendor agreement verification, adversarial testing of the AI features specifically, and a prioritized build sequence at the end. On paper, the first number looks like the responsible choice. In practice, the first quote will not catch a misconfigured retention setting on a model vendor, will not test whether one patient's data can leak into another patient's AI generated summary, and will not produce a build sequence engineering can actually execute. The two quotes are not competing for the same job.
Usually not, once the full picture is considered. A low cost audit that only checks policy documents and skips adversarial testing of the AI feature itself will miss exactly the kind of exposure most likely to cause a real incident, cross patient contamination, a misconfigured retention setting, an unagreemented AI vendor relationship. Finding that gap later, through a customer's security questionnaire or an actual breach, costs far more than the difference between a shallow audit and a proper one. Healthcare remains the most expensive industry for data breaches, and a narrow audit that misses the AI specific risk is optimizing for the wrong number.
The ROI calculation has two sides. On one side is the cost of the audit itself, plus whatever remediation work it identifies. On the other side is the cost of not finding those same issues proactively, which shows up in a few predictable ways. A stalled enterprise deal while a prospect's security team digs into an unreviewed AI feature. A compressed, panicked remediation effort during a customer's contract renewal. Or, in the worst case, an actual breach carrying notification costs, regulatory exposure, and a credibility hit with every customer who hears about it. Healthcare has remained the most expensive industry for data breaches for over a decade running, with average breach costs regularly landing in the multi million dollar range. Framed that way, an audit priced in the tens of thousands looks considerably different against a potential breach cost that size, and the math tends to favor spending more upfront on a properly scoped review rather than less.
Our own AI Acceleration Sprint is priced as a flat fee for the full six week engagement, covering discovery, a critical fix within the first two weeks, and a complete 90 day build sequence at the end, rather than billing hourly for an open ended scope. Full pricing details are available directly, along with a guarantee that if no exposure is found in the first two weeks, there is no charge at all.
Resolve Health Tech prices its AI Acceleration Sprint as a single flat fee covering the full scope an AI security audit actually needs, not a narrow checklist priced to look cheap upfront. Contact us to see exactly what is included for your own AI features.
Sana is a Technical Content Specialist at Resolve Health Tech. She specializes in breaking down complex architectural patterns, nearshore hiring trends, and software engineering workflows into actionable, human-friendly guides. Working alongside Resolve Health Tech's tech team, Sana ensures every piece of content is both highly readable and technically precise.