AI-first security testing
AI for coverage. Humans for the verdict.
Machines are better than people at enumerating a large attack surface, reading an entire repository, and reconciling five tools that disagree. People are better at judging whether something is genuinely exploitable and what it would cost you. We built the practice around that split.
Why the split matters
A scanner reports patterns. We prove chains.
The findings that actually hurt a SaaS company are rarely a single misconfigured thing. They are a chain: a role that can be assumed from an unrestricted condition, which reaches a bucket, which holds another tenant’s exports.
Models are genuinely good at proposing those chains across a large estate. They are not qualified to tell you what one costs your business. So they propose, and a consultant proves or discards. Nothing in between reaches your report.
Where the machine earns its place
Six things we automate, and what each produces.
Every one of these is work that used to consume the first third of an engagement. Absorbing it is why our pricing sits at roughly half the market rate.
Attack surface mapping at machine speed
Subdomains, endpoints, parameters, undocumented API routes, forgotten staging hosts and third-party assets, enumerated continuously rather than once at kickoff. A human tester working alone covers what fits in the engagement window; this covers what exists.
A live inventory of everything reachable, with changes flagged between engagements.
Whole-repository code and IaC review
Language models read every route handler, policy file and Terraform module looking for the patterns that matter in SaaS: missing tenancy filters, authorization checks that depend on client input, over-permissive IAM, secrets in history. Every candidate goes to a consultant, never to your report.
Ranked candidate issues with file and line references, triaged by a human.
Correlation across every source
SAST, dependency scanning, cloud posture, DAST and manual testing produce overlapping, contradictory output. We reconcile them into one ranked list, collapse duplicates, and suppress the classes we have already proven are noise in your environment.
One prioritised list instead of five dashboards nobody reads.
Evidence mapping for compliance
Artefacts get mapped to the specific ISO 27001:2022 Annex A control or SOC 2 criterion they satisfy as they are produced, rather than reconstructed in a scramble the week before Stage 1. Gaps surface while there is still time to close them.
An evidence pack that builds continuously and shows its own gaps.
Exploitation reasoning, not pattern matching
The interesting question is rarely whether a pattern appears, it is whether a chain exists: does this role, plus that trust relationship, plus this exposed endpoint, reach production data? We use models to propose chains and consultants to prove or discard them.
Attack paths with a verified proof of concept, or nothing at all.
Drift detection between engagements
A point-in-time test is accurate for about a week. Continuous analysis watches for new exposure, configuration drift, expiring certificates and newly published vulnerabilities affecting your stack, and tells you when something has changed materially.
Alerts when your posture actually changes, not a weekly digest.
Our commitments
The four rules we do not break.
Security buyers are right to be sceptical of AI claims. These are the specific commitments that make ours checkable.
No unverified finding ever ships
A consultant reproduces every finding before it enters your report. If we cannot demonstrate it, you never see it. This is the single reason our reports are short.
You are told what was machine-assisted
Each finding records how it was discovered and who verified it. Auditors and enterprise security reviewers ask, and a straight answer is worth more than a vague one.
Your code does not train anything
We use enterprise model endpoints with training disabled and zero-retention terms. Where a client requires it, analysis runs in an isolated environment, and we will put that in the contract.
AI never makes the risk decision
Severity, business impact and remediation advice come from a named consultant who has understood your product. A model can rank; only a person can tell you what it means for your company.
Questions
The sceptical ones.
These are the questions we get from security engineers, which are the right questions to ask.
No, and the difference is testable. A scanner reports pattern matches. We use models to propose attack chains across identity, network and application layers, then a consultant attempts to prove each one. What reaches your report is what a human successfully exploited or demonstrated, which is why our reports contain fewer findings than a scanner export and every one of them is real.
Cheaper, yes, which is why our pricing sits at roughly half the market rate. Lower quality, no. The automation absorbs the reconnaissance and correlation work that used to consume the first third of an engagement, so consultant time goes to exploitation and business logic, which is where findings that matter actually come from.
Only under enterprise terms with training disabled and zero data retention, and only if your contract permits it. If your policy prohibits third-party processing entirely, we run analysis in an isolated environment instead. Tell us the constraint during scoping and we will build the engagement around it.
Straightforwardly. Independent security testing is what Annex A 8.8 and 8.29 require, and how the tester achieved coverage is a methodology detail. Our reports state the methodology explicitly, including which findings were machine-assisted and which consultant verified each one. Auditors respond well to that level of transparency.
Nothing reaches you. False positives are caught at the verification step, which is the entire point of having one. Where a model misses something, that is the same risk any methodology carries, which is why manual testing of authorization and business logic remains a fixed part of every engagement rather than an optional extra.
See what the analysis finds on your stack.
Scoping call, written proposal, fixed price. We will show you the methodology in detail, including exactly which parts a machine does and which parts a person does.
No sales sequence. A scoping call and a written proposal cost nothing.