AI code security & audit firms compared (2026): which fits your situation

Last verified: October 2026· list

Lists like this usually hide paid placements or quietly rank the author first. This one does neither: nobody paid to be here, it is ordered by the kind of buyer each option suits, and because we sell this work too, we have left ourselves out of the comparison and disclosed our fit separately below.

How we picked these

We read each option’s public material — site, docs, case studies, talks, repositories — and record the specific signal we checked, with the month we checked it. Inclusion means an option publicly offers this work and has at least one checkable public signal. It is not proof of results; use the questions further down to test that yourself.

This is a buyer-fit comparison, not a ranking. Entries are grouped by buyer type — SAST products with AI-aware rules, AI-specific security products, then services firms — and the order reflects that grouping, not merit.
What we did: read each firm’s or product’s public material (website, docs, case studies, talks, repositories) and recorded the specific public signal we checked, with the month we checked it. We did not test any product or verify any audit.
Inclusion means the firm or product publicly offers security review or scanning aimed at code written or assisted by AI agents (Claude Code, Cursor, Copilot, etc.) or at agent systems, and has at least one checkable public signal. Pure legacy AppSec with no AI-specific offering is out of scope. Inclusion is not proof of results.
Agent Month offers AI code security work, so it is one of the options in this category. We therefore leave it out of the comparison and disclose our own fit separately.

What to weigh

Criterion	Weight	What to check
AI-specific expertise	high	Ask them to show you a review of AI-written code or an agent system they have done — redacted findings are fine — rather than generic AppSec with an AI label.
Controls that stay running	high	Check whether they ship policy and scanning into your CI, or deliver a one-off report.
Open-source footprint	medium	Look for open-source rules, scanners, or agent-boundary tooling you can inspect yourself.
Regulated context	medium	If you are in healthcare, finance, or an EU-residency setting, ask what work in that context they can show you.

The options, by buyer type

Grouped by the kind of buyer each option suits. The order is not a ranking, and no placement is paid.

Snyk
Vendor product
Teams that already have Snyk and want AI-aware scanning added on top.
- Existing Snyk customer
- Product-led AppSec team
What they do
- Snyk Code (SAST) with AI-aware rules
- Snyk DeepCode AI for AI-generated code
- License + dependency scanning
Strengths
- Mature product with broad language support
- Strong CI/CD integration
Limitations
- A product, not a services firm — you wire and operate it
- AI-specific rules are improving but not the entire company focus
Pricing
Per-seat / per-scan
Signal
Most-cited AI-aware SAST vendor (checked June 2026)
Visit Snyk
Semgrep
Vendor product
Engineering teams that want a customizable SAST engine with AI-specific rules.
- Custom rule writers
- Product-led AppSec team
What they do
- Semgrep Code with AI-aware rules
- Custom rule writing for AI-generated code patterns
- Supply chain scanning
Strengths
- Highly customizable; rules are open-source
- Strong fit when your team already uses Semgrep
Limitations
- Product, not services; you operate it
- AI-specific coverage is improving but not the whole product
Pricing
Per-seat
Signal
Frequently listed in AI code security "best of" (checked June 2026)
Visit Semgrep
HiddenLayer
Vendor product
Teams that want a model-and-MCP-aware security layer.
- AI/ML model security
- MCP-aware security layer
What they do
- AI / ML model security
- Adversarial-input detection
- MCP security
Strengths
- Specialized in AI/ML security, not generic AppSec
- MCP-aware
Limitations
- Narrower product surface than Snyk / Semgrep
- Less of a fit if you need a broad SAST replacement
Pricing
Enterprise
Signal
Cited as an emerging AI security vendor (checked June 2026)
Visit HiddenLayer
Astra Security
Engineering firm
Healthcare and finance CISOs that want a vendor-aware AI code security partner.
- Healthcare CISO
- Services-led audit
What they do
- AI code security audits
- Healthcare-specific compliance (HIPAA) reports
- AI supply chain risk
Strengths
- Strong healthcare vertical positioning
- Frequently cited in CISO content for AI supply chain risk
Limitations
- Vertical-specific (healthcare); less of a fit for finance or EU
- Less open-source footprint
Pricing
Per-engagement
Signal
Most-cited firm in the CISO/healthcare AI cyber SERP (checked June 2026)
Visit Astra Security

Disclosure

We’re one of the options: where Agent Month fits, and where it doesn’t

We sell this work, so we are not in the comparison above. Judge us on the same questions you would put to anyone else.

Fits

Security and engineering leaders who want an audit of AI-assisted code (vulnerabilities, licence issues, hallucinated packages, secrets) followed by AI-aware SAST and secret scanning shipped into CI, over about 3–5 weeks.
Teams running agents or MCP integrations that want least-privilege, read-only defaults, audit logging, and human approval on destructive actions.
Teams that want a prompt-data gateway that redacts PII and secrets and audit-logs prompts.

Doesn’t fit

You need a managed SOC or a large managed security team — our bench is small.
You want a managed AppSec product — the scanning we ship is yours to operate.
You need a compliance attestation or certification — that is a different product from a security audit, and we don’t issue one.
You need a firm with published, named audit case studies — we don’t have any yet.

Evidence you can check

agentvfs (github.com/neul-labs/agentvfs) — open-source workspace runtime and execution boundary for AI agents.
ormai (github.com/neul-labs/ormai) — open-source library for scoped, governed database access by AI agents.
Pricing published on our service page: $20–50k audit, $5–12k/mo monitoring (agentmonth.com/services/ai-code-security).
No named customer case studies published yet.

What separates a good vendor from a bad one

The comparison above is our read. This is how to form your own — the things that actually predict whether an engagement works.

Decide whether you are buying a code audit or an AI-system audit

These are separate disciplines and most firms are strong at one. A code audit reviews the software your team ships, including the parts an assistant wrote — the findings are conventional application security. An AI-system audit reviews the behaviour of the model-driven system itself: prompt injection through untrusted content, tool and agent permissions, what an agent can reach when it is manipulated. A firm with deep traditional appsec credentials may have little to say about the second, and a firm built around AI risk may be thin on the first.

Ask how they handle the volume problem

Scanners aimed at AI-generated code tend to produce a great many findings, and the value of the engagement is almost entirely in triage. Raw finding counts are the easiest number to inflate and the least useful to receive. What matters is whether they will tell you which findings are exploitable in your deployment, and whether they will stand behind that prioritisation.

Establish whether the output is a report or a remediation

A report tells you what is wrong and leaves the work with your team. A remediation engagement ships fixes. Both are legitimate, and they cost very different amounts. Many disappointing engagements come from buying the first while expecting the second, so confirm which one is priced before signing.

Check whether findings become permanent controls

A point-in-time audit ages immediately — the next sprint of AI-assisted code arrives with no protection. The durable version of this work leaves CI gates, secret scanning, and dependency policy behind, so the same class of issue is caught automatically from then on. Ask what remains running after the engagement ends.

Questions to ask on the first call

Each one is chosen because the answer is hard to fake and tells you something the website won't.

1
Do you test for prompt injection and agent permission escalation, or only for code defects?
It establishes immediately which of the two disciplines they practise. Both are valid to buy; buying one while believing you bought the other is the common and expensive mistake.
2
How do you decide which findings actually matter in our deployment?
Exploitability depends on context — network position, auth model, what data the component can reach. A firm that prioritises by generic severity rating alone has not looked at your architecture, and their ranking will not survive contact with your engineers.
3
Will you fix the issues, or hand us a list?
This is the single largest driver of both price and outcome. Make it explicit before scoping, because the two engagements are often described with very similar language.
4
What stays in place after you leave?
CI gates, scanning configuration, and policy are what stop the same problem recurring. An engagement that leaves nothing behind must be repurchased every time your codebase moves on, which for AI-assisted teams is continuous.
5
How do you handle our code and any data you touch during testing?
You are granting deep access to your source and possibly to production-like data. Retention, jurisdiction, and subcontracting should be documented before access is granted, not negotiated afterwards.

Red flags

None of these are disqualifying on their own. Two or more together usually are.

Findings quoted as a headline count.
Volume is trivially inflated by tuning a scanner to be noisy, and a large undifferentiated list transfers all the triage work back to your team — which is the part you were paying to have done.
No distinction drawn between AI-specific and conventional risks.
It usually means an existing appsec offering has been relabelled. The prompt-injection and agent-permission surface is genuinely different and will not be covered by a traditional review.
Unwillingness to discuss false positive rates.
Every tool has them. A firm that will not talk about theirs is either not measuring or is hoping you will not notice how much of the report you will need to discard.
Compliance certification offered as the outcome.
A security audit and a compliance attestation are different products with different scopes. Conflating them tends to mean the engagement is optimised for producing evidence rather than for finding problems.

How these engagements are priced

The pricing model shapes the incentives more than the rate does.

Model	Fits when	Watch for
Point-in-time audit	A specific release, a customer security review, or a due-diligence deadline.	It goes stale as soon as the next feature lands. Useful as evidence, weak as ongoing protection.
Audit plus remediation	You have findings you cannot resource fixing internally.	Confirm who verifies the fixes. Self-verified remediation by the firm that wrote the fix is worth less than an independent re-test.
Continuous scanning subscription	High rate of AI-assisted code entering the codebase.	Alert fatigue. Without tuning and ownership these become an ignored dashboard within a quarter.
Embedded engineer	You want the capability built into your team rather than delivered to it.	The value depends heavily on the individual. Interview the person who will actually do the work, not the practice lead who sells it.

What we didn't include

Legacy AppSec consultancies with no AI-specific offering
Cloud hyperscalers (AWS, GCP, Azure) — their security tooling is part of a wider platform rather than a dedicated AI-code offering
Generalist compliance consultancies

How to pick

Match the buy to the firm, not the other way around. A boutique engineering firm is not a substitute for an enterprise consultancy; a vendor product is not a substitute for either.

If you are…	Pick	Why
CISO / AppSec leader that needs an audit + scanning infrastructure shipped	Agent Month (us — see disclosure)	Fixed-scope audit with the scanning and gates shipped into your CI. No published case studies yet; the public evidence is our open-source agentvfs and ormai repos.
Team already on Snyk, want AI-aware rules added	Snyk	Mature product, broad language support.
Team that wants customizable SAST with AI rules	Semgrep	Open-source rules, very customizable.
Healthcare CISO	Astra Security	Healthcare vertical positioning, AI supply chain focus.

Frequently asked questions

What does an AI code security audit actually cover?

A focused audit covers: vulnerability classes unique to AI-generated code (hallucinated APIs, missing tests, subtle logic bugs, license violations), prompt-data leakage in the agent pipeline, MCP server hardening, and policy recommendations. It ends with a shipped scanning stack in CI, not a slide deck.

How is AI code security different from regular AppSec?

AI-generated code introduces new failure modes traditional AppSec tooling misses: hallucinated packages, prompt-injection surfaces in MCP, data leakage through prompts, and supply-chain risk in training-derived code. Generic SAST catches some of these, but you need AI-specific rules + a prompt-data gateway + MCP hardening to be fully covered.

How long does an AI code security audit take?

A focused 3–5 week engagement covers: a code audit, prompt-data gateway deployment, AI-aware SAST in CI, MCP hardening for your top 3 integrations, and a runbook for your security team.

How is AI code security priced?

Most engagements are fixed-scope for the audit ($20–50k) with a monthly monitoring retainer ($5–12k/mo). The retainer covers rule updates, supply-chain monitoring, and incident response.

Related guides

Hiring & partners

How to choose an MCP server development company

The MCP SDK is a weekend's work; the job you're actually hiring for is least-privilege auth, scoping, audit logging, and production hardening on servers that reach straight into your internal systems.

Agentic coding

Is your codebase ready for AI agents?

Agents don't fail randomly on a repo — they fail predictably on the same things that slow down human engineers: fuzzy boundaries, missing tests, undocumented conventions. Readiness is just those weaknesses paid down deliberately.