AI-assisted security testing
AI Powered Penetration Testing
Find security weaknesses across more of your environment with AI-assisted testing and experienced human testers. GDF validates the findings and gives your team practical steps to address the risks.
The engagement
Use AI to widen coverage, not make findings
AI can help a tester review a larger body of application behavior, generate useful test variations and correlate technical output. It can also invent explanations, repeat weak assumptions and produce results that are not safe to run without review.
GDF treats AI as an instrument inside a human-led test. The rules of engagement define which systems and models may be used, what data may be processed and which actions require direct approval. A qualified tester reviews every proposed action and reproduces every reported finding.
Scope
Reconnaissance triage
Use approved tools to organize exposed services, technology clues and candidate paths for human review.
Test-case variation
Generate and prioritize input, workflow and configuration variations without allowing a model to set its own scope.
Application and API analysis
Correlate routes, roles, schemas and observed behavior to identify test sequences that merit validation.
Evidence correlation
Group scanner, request, identity and configuration evidence so the tester can assess connected attack paths.
Human validation
Reproduce candidate findings, reject unsupported output and retain the technical record behind accepted results.
Targeted retesting
Use the original evidence and approved variations to confirm that the material path is closed.
What AI does in a GDF penetration test, and what it never does
An experienced tester facing an application with several hundred routes and half a dozen user roles has an organization problem before they have a security problem. AI is good at organization problems. It can sort the routes, cross-reference tool output and propose test cases the tester might not have reached on a five-day engagement. GDF uses AI powered penetration testing where it improves the work, and keeps scope, data handling, validation and reporting under human control at every step.
For you as the buyer, the practical value is better organized testing inside the agreed scope, and a report where every finding rests on behavior we demonstrated. If you ask how a finding was reached, you get a technical explanation from the examiner who reproduced it. A model's confident paragraph does not count as an answer.
Bounded tasks, reviewed by the examiner
Say a tester is working through account permission checks on a multi-tenant application. An approved model generates twenty variations of the test: different roles, different object types, different request methods, different sequences. The tester reads them and throws out the ones that touch anything outside scope. The rest get run against the application by hand. The ones that produce a real result become findings. The ones the model was sure about but that failed on the live system go in the bin.
The review matters because generated code, commands and explanations are wrong often enough that trusting them unread would be negligent. A test can also succeed for a reason unrelated to the weakness it was meant to find, and a model will happily report that as a hit. Reproduction by a human is part of the method, every time.
Your data does not leave the approved environment
The engagement specifies which tools are permitted, where processing happens and what client information may be used with them. Source code, credentials, personal data and detailed findings do not go to any service you have not approved. If you require that nothing touch a public model, that goes in the rules of engagement and we plan the work accordingly. The approved workflow also determines which prompts and supporting records get retained, so the account of how a finding was reached is available later.
A model is given no authority. It cannot expand the target list and it cannot act outside the agreed set. Scope decisions are made by people. When a useful test needs access we do not have, that becomes a conversation with you before anything else happens.
What you are actually buying
The report identifies the tested surface, the material findings, the reproduction evidence and the corrective priorities. Where AI contributed to the work, the account is clear enough that you can separate the assistance from the examiner's conclusion.
Be skeptical of anyone who tells you adding AI finds every vulnerability or makes a bounded assessment complete. It does neither. The question worth asking any vendor is whether the planned method fits your environment and leaves your team with validated issues it can fix. We will have that conversation with you before the work begins, and if conventional testing is the stronger choice for your situation, we will say so.
Methodology
How the test runs
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Constrain
Define targets, models, data handling, prohibited content and human approval points.
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Explore
Use AI-assisted analysis to expand candidate coverage within the authorized test plan.
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Reproduce
Require a tester to validate the condition and observed impact before it becomes a finding.
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Document
Record methods, evidence, limitations and the role AI played in each reported result.
Evidence commonly examined
Evidence reviewed
- AI and tool-use plan
- Approved model and data boundaries
- Prompts, commands and test records where relevant
- Human reproduction evidence
- Rejected and accepted candidate findings
- Remediation and retest record
What you can expect
What you receive
- AI-use and test-scope statement
- Human-validated technical findings
- Attack-path and evidence correlation
- Prioritized remediation and retest results
Frequently asked
Common questions
Does this test the security of our AI system?
Not by default. This service uses AI to assist penetration testing of your applications and infrastructure. Testing a model, an agent, a retrieval pipeline or an AI deployment is a separately scoped AI security engagement.
Can we prohibit public model processing?
Yes. Data and tool restrictions belong in scope. We will tell you whether the approved environment supports the AI assisted tasks we had in mind, and adjust the method if it does not.
Who is responsible for a reported finding?
The human test team. Every finding is reviewed and reproduced by an examiner before it appears in the report. Model output alone never counts as evidence that a weakness exists.
Does AI replace the penetration tester?
No. A human tester controls the engagement, reviews proposed actions and validates every reported finding.
Will proprietary data be sent to a public model?
Not by default. The scope identifies approved tools, processing locations and prohibited data before testing begins.
Does AI-powered testing guarantee broader coverage?
No finite test guarantees complete coverage. The report states the tested surface, methods, period and limitations.
Discuss a human directed test with GDF
Talk with GDF about whether AI powered penetration testing fits your environment. Share the systems in scope, your data restrictions and the decision you need to make. We can explain where AI support adds value, where conventional testing is stronger and how every finding will be validated. Start with a free initial consultation.
Discuss a human directed test with GDF
Related services and resources: application penetration testing, cybersecurity penetration testing, AI security consulting.
Talk with an examiner
Discuss your matter and next step
Tell us the systems, evidence and deadline. We can review relevant experience, potential conflicts and the scope before engagement.
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Talk with an examiner
Discuss the matter and the next step.
Tell us what happened and what you need to find out. Speak with a GDF expert about how we can help.
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