An AI security audit's cost depends heavily on the scope of what is being tested, so there is no single fixed price, but the main cost drivers are the number of AI systems and models in scope, whether testing is automated only or includes manual adversarial red teaming, and how deeply the audit examines the surrounding infrastructure such as data pipelines and connected tools rather than the model alone. As of 2026, a narrow automated scan of a single chatbot, checking for common prompt injection and data leakage issues, can start in the lower five-figure range in US dollars, while a comprehensive engagement combining automated scanning with manual red teaming across multiple systems, plus a written remediation report, commonly runs from the mid five figures into six figures depending on scope. Ongoing testing, rather than a one-time audit, is often priced as a recurring retainer, which can be more cost-effective for a system that changes frequently. Buyers should ask exactly what is included, since a low quote for automated-only scanning is not comparable to one that includes manual testing and remediation support. Current pricing should always be verified directly with the vendor before budgeting. Nanobase AI scopes AI security audits to a client's actual system complexity rather than quoting a flat rate.
Why two audit quotes for "the same thing" can differ sharply
Two vendors quoting an AI security audit for the same chatbot can land far apart in price without either being wrong, because the phrase "security audit" covers a wide range of actual scope, and vendors rarely default to the same assumptions about what is included. The quote depends far more on scope decisions the buyer controls, such as how many systems are covered and whether testing includes manual red teaming, than on any fixed market rate for "an AI security audit." As of 2026, specific pricing should always be confirmed directly with a vendor rather than assumed from general figures.
Cost driver table
| Scope dimension | Lower cost | Higher cost |
|---|---|---|
| Number of systems in scope | Single chatbot or application | Multiple systems, agents, or a full AI portfolio |
| Testing depth | Automated scanning only | Automated scanning plus manual adversarial red teaming |
| Infrastructure coverage | Model and application layer only | Full stack including data pipelines, connected tools, and infrastructure |
| Engagement structure | One-time audit | Ongoing retainer with recurring testing |
| Deliverable depth | Findings list only | Findings plus detailed remediation guidance and re-test |
As of 2026, a narrow automated scan of a single chatbot, checking common prompt injection and data leakage issues, can start in the lower five-figure range in US dollars, while a comprehensive engagement combining automated scanning with manual red teaming across multiple systems, plus a written remediation report, commonly runs from the mid five figures into six figures depending on scope; these figures should be verified directly with a vendor before budgeting, since they move with market conditions and specific scope.
A scoping checklist before requesting quotes
Locking down these five decisions before contacting any vendor is what makes the resulting quotes comparable to each other.
- List every AI system that should be in scope, including any agents or connected tools, not just the primary customer-facing application.
- Decide whether the engagement needs manual red teaming or whether automated scanning meets the current risk tolerance, referencing the testing approach comparison for how these differ.
- Determine whether infrastructure such as data pipelines and connected MCP servers or tools should be included alongside the model and application layer.
- Decide between a one-time audit and an ongoing retainer, based on how frequently the system changes.
- Send the same scope definition to every vendor being compared, so the quotes received are actually comparable rather than reflecting different assumptions about what is included.
Ongoing testing versus one-time audit economics
A one-time audit captures a snapshot of the system's security posture at a single point in time, which ages quickly for a system under active development, since a new feature or prompt change introduced afterward is untested. A recurring retainer, priced as an ongoing arrangement rather than a single project fee, is often more cost-effective over a year for a system that changes frequently, because it avoids the overhead of re-scoping and re-negotiating a new one-time engagement every time a meaningful change ships.
Frequently asked questions
Why do audit quotes vary so much between vendors for what sounds like the same request?
Vendors default to different assumptions about scope, testing depth, and deliverable format unless the buyer specifies these explicitly, so quotes that appear to cover "the same thing" are often pricing genuinely different amounts of work.
Is a low quote a red flag?
Not automatically, but a quote significantly below others for the same stated scope is worth investigating specifically for what it excludes, such as manual testing or a written remediation report, rather than assuming it represents the same coverage at a better price.
Does audit cost scale with the size of the AI system itself, such as model parameter count?
Cost scales more with the complexity of the attack surface, the number of integrations and connected tools, and testing depth than with the underlying model's size, since a small chatbot with broad tool access can require more testing than a larger model used for a narrow, isolated task.
How Nanobase AI helps
Nanobase AI scopes AI security audits to a client's actual system complexity rather than quoting a flat rate, walking through the scoping checklist above before proposing a testing plan and selecting the tooling appropriate to the engagement.
Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.