AI-Driven Pentest Platform
Autonomous, Logic-Aware Penetration Testing
AI agents that test an application knowing its roles and workflows, prove a finding by demonstrating it safely, and stop for human approval before anything changes state. The judgement stays with the expert.
About the Platform
A security assessment usually starts from a generic checklist. The first days go on rediscovering what was already discovered last year: which roles the application has, which workflows matter, where the trust boundaries sit, what was found the previous time. None of it was written down in a form a platform could hold, so it is done again, and you pay for it again.
VAaaS breaks that cycle. The platform keeps a profile for each application that belongs to the application rather than to the engagement; the scenario set for an assessment is built from a versioned methodology library plus that profile; every finding is recorded with its evidence and reproduction steps; and every exclusion carries its reason. What survives an assessment is more than a PDF.
The effect is simple: the first assessment builds the knowledge, later ones spend their hours on the difference, coverage can be audited, and every claimed fix is verified against the finding that produced it. This is the platform HafezSecure runs its own assessment work on.
Persistent
Profile per Application
Evidenced
Every Finding Reproducible
Auditable
Coverage and Exclusions
Two Ways
Our Service or Your Install
How the Work Is Divided
Machines run what machines do well, and the expert judges the rest
1
Every Test Case Declares Who Runs It
In the methodology, each case is marked expert-only, agent-assisted or agent-eligible. That judgement is made once, by people, and it is what keeps the machine on the work it is actually good at.
2
Agents Take the Mechanical Subset
The eligible cases run in parallel inside sandboxes, with the application profile telling the agent what the roles, workflows and entry points are — so it is working from knowledge of this application, not crawling it blind.
3
The Expert Keeps the Judgement
What an agent produces is a candidate with evidence. An expert decides whether it is real, what it means for your business, and how severe it is — and anything that would change state waits for a person to approve it first.
Key Features
What makes autonomous testing trustworthy
Works From an Application Profile
Roles, workflows, entry points and business rules are given to the agent before it starts. A tool that has to discover all that by clicking will spend its budget discovering instead of testing.
Logic-Aware, Not Pattern-Matching
Multi-step sequences across roles: place an order as one user and settle it as another, skip a step, repeat a step, arrive at a state the designers never drew. That is where the expensive findings live.
Findings Proved, Not Guessed
A candidate is confirmed by demonstrating it safely, and it arrives with the request, the response and the steps that reproduce it. Anything that cannot be demonstrated is reported as uncertain rather than dressed up as a finding.
Approval Before Anything Changes
Read-only exploration runs freely; a step that writes, deletes, pays or emails waits for a human. The person who requested the run is not the person who approves it.
Sandboxed, With a Leash
Agents run in isolated environments whose outbound traffic follows an allow-list, so a test cannot wander out of scope or reach a system nobody authorized.
Parallel, Within Your Limits
Many cases run at once, bounded by the request rate and concurrency you set for the target. Speed is useful; a test that takes an application down is not.
Replayable Record of What It Did
Every request, decision and tool call is recorded, so an engineer can follow exactly what the agent did and when — the answer to "what touched our staging environment at 3am?"
Your Data Does Not Feed a Cloud Model
Requests run through our governed AI platform, where sensitive values are masked before anything reaches an external model — and an installation can be restricted to models running inside your network.
Regression Testing for Security
Findings from the last engagement become cases that run again on the next release, so a fix that quietly comes undone is caught by the machine rather than by an attacker.
One Finding Lifecycle
What agents find lands in the same place as expert findings and imported results: correlated, prioritized, assigned and retested. No separate tool with its own list to reconcile.
Honest About What It Cannot Do
Cases marked expert-only stay with experts, and the report says which parts of the methodology the machine covered. An agent with no tailored scenario set is a scanner with extra steps, and we do not pretend otherwise.
Runs Where the Target Lives
Install it beside the systems under test, including in networks with no internet route, so traffic to your application never leaves your own infrastructure.
What It Tests
Anywhere there are logins, roles and workflows
Web Applications
APIs
Mobile Back Ends
Internal Services
Use Cases
Where expert hours are scarce and applications are many
The Eleven Months Between Pentests
An annual engagement leaves most of the year uncovered. Agent runs fill that gap on a schedule, and the expert engagement starts from what they found.
Release Gates
Run the cases that matter for the part of the application a release touches, before it reaches customers rather than after.
Large Application Portfolios
Hundreds of applications cannot all have an expert engagement every quarter. The machine covers breadth; people go deep where it matters.
Before the Experts Arrive
A sweep before a manual engagement clears the obvious ground, so the expert days are spent on the logic nobody automates.
Evidence for Auditors
Dated runs against named applications, each with what was covered and what was found, for the question about testing frequency that always comes up.
Verifying Old Fixes
Last year’s findings become this year’s regression suite, and a fix that quietly regressed shows up on the next run.
How It Works
From authorization to a reviewed report
1
Authorize and Bound
Agree the scope, the environment, the request rate and what the agent may never touch. Authorization is explicit and versioned, as it is for any other testing.
2
Hand Over the Profile
Roles, workflows, credentials for test accounts and the business rules that matter. The better the profile, the less budget goes on rediscovery.
3
Run, With Gates
Eligible cases execute in parallel inside sandboxes. Read-only work proceeds; anything that would change state stops for approval.
4
Expert Review, Then Report
An expert confirms the candidates, rates them and writes what they mean for you. The findings join the same lifecycle as every other finding, retests included.
Ways to Use It
With an engagement, installed with you, or on a schedule
With an Engagement
Our assessors run it as part of the work and you receive reviewed findings, not raw agent output.
Inside Your Network
Install it beside the systems under test so traffic never leaves, including on networks with no internet route.
On a Schedule
Run it per release or per month against a portfolio, with expert review only on what it finds.
Why This Platform
How it differs from an automated scanner
It Starts Knowing the Application
The difference between this and a scanner is the profile: roles, workflows and rules are handed over before the first request, so the machine tests behaviour rather than guessing at patterns.
Autonomy With a Handbrake
Sandboxes, an outbound allow-list, rate limits and human approval before any state change. Autonomous testing without those is how an automated test becomes an incident.
Fewer Findings, More True Ones
A candidate is only reported as a finding once it has been demonstrated, and an expert has agreed. Nobody needs another report full of maybes.
Nothing Sensitive Leaves
Sensitive values are masked before any request reaches an external model, and the whole thing can run on models inside your own network.
Frequently Asked Questions
What teams ask before letting an agent test anything
Does this replace penetration testers?
Can it run against production?
What stops it breaking something?
How do you avoid a report full of false positives?
What data leaves our environment?
How is it different from an automated scanner?
What do you need from us to start?
Who approves the risky steps?
How do you control the cost of the AI itself?
What do we receive at the end of a run?
Can it test something that needs a login and several roles?
How does it fit with VAaaS?
More Coverage, Without Giving Up Judgement
Contact our team to run it against one of your applications and see what comes back
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Related insights
What we have written about this service
Penetration testing
What a penetration test report must contain
The sections a useful penetration test report has, what each one is for, and the questions to ask before you accept a deliverable that is missing them.
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Vulnerability assessment vs penetration test
The two are bought for different reasons and priced differently. How they differ in method, depth and output, and a simple rule for which one to order this quarter.
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