AI in penetration testing
AI accelerates the work.Humans prove the exploit.
AI runs recon, surface mapping, payload variation, and first-draft reporting. CREST-accredited researchers chain the exploit, validate business impact, and sign every finding.
Trusted by security teams across Fintech, SaaS & Education, Enterprise & Telecom, Security & Critical Infrastructure
Audit-grade guardrails
The same accreditations your auditor already accepts.
CREST, CERT-In, SOC 2 Type II, and ISO/IEC 27001 govern every report we ship, including the ones where AI drafted the first pass. Each accreditation acts as a guardrail against AI hallucination.
CRESTAccredited company & testers
CERT-InEmpanelled auditor
SOC 2 Type IIEngagement controls audited
ISO/IEC 27001Information Security Management
Where AI runs
Four jobs AI handles in our engagements.
AI is fast at the cataloguable and the repeatable. We put it where speed wins and judgment isn't required, and draw a hard line at the point where judgment starts.
Recon + attack-surface mapping
Subdomain enumeration, certificate-transparency mining, exposed-admin discovery, leaked-credential checks. AI agents parallelise the catalogue work that a human would otherwise burn a day on.
Payload variation + pattern matching
Known-bug-class fuzzing across input surfaces. SSRF wrappers, SQL variants, deserialization gadgets. AI generates and tries the variants; a human reviews which actually fire.
Draft report writing
First-pass write-up of each confirmed finding. title, reproduction steps, named bug class, severity rationale. A human pentester edits and signs.
Cross-finding correlation
Surface relationships between findings. e.g. this CVE on a public asset plus that AD path equals a candidate kill chain. A human decides if the chain is real.
Where a human signs
Four jobs a CREST-accredited human owns.
The work AI cannot do reliably. The seam between AI-only vendors and human-led AI penetration testing sits exactly here. We publish it instead of blurring it.
Hypothesis + business-logic chaining
A human reads your application like an attacker. invariants, roles, asset-of-value graph. AI doesn't know your business; this is where it's still flat-wrong.
Exploit validation + proof-of-exploit
A finding without a working PoC is a guess. The human runs the chained exploit, captures the request/response trail, and produces the tx hash or video your dev team can replay.
Customer narrative + impact rating
Severity isn't CVSS alone. it's CVSS scored against your invariants. The human maps each finding to the asset it threatens, in your language.
Final report + sign-off
Every report leaves the building with a CREST-accredited tester's name on it. No AI signature, no auto-published findings.
How we keep AI honest
Rabit0. The trust layer between AI and your data.
Rabit0 sanitizes client data before any model sees it. No customer code, secrets, or PII enters model context. High-severity candidates route through multiple models, and a finding only advances when independent runs agree. Guardrails block AI from auto-publishing or auto-emailing. Every AI-touched artefact records to an immutable audit log.
How the handoff runs
Six phases. One named owner per phase.
Every phase of an AI penetration testing engagement has a named owner: AI or human. No ambiguity. We publish the boundary so your auditor can read it.
- 01Scope + threat-model
- 02Recon + surface mapping
- 03Pattern-driven discovery
- 04Manual exploit + chaining
- 05Report drafting + review
- 06Fix-verify re-test
The seam
“AI-only vendors hand you a finding count and a confidence score. Vague hybrid vendors hand you the same thing wrapped in the word "expert." We hand you a working proof-of-exploit, a named bug class, the request trail a developer can replay, and the signature of the CREST-accredited human who ran it. The first two are guesses. The third is evidence.”
AI handoff runs on
Six engagement types. One handoff.
The AI / human boundary applies the same way across every pentest we ship. Pick the engagement that matches your scope; the methodology is identical.
Web application pentest
Auth bypass, IDOR, business-logic flaws, SSRF, deserialization. AI fuzzes payload variations across endpoints; humans chain the kill chain.
Mobile app pentest
iOS + Android, native + Flutter + React Native. AI maps the bundle and instrumentation surface; humans drive exploitation.
Cloud pentest. AWS · Azure · GCP
IMDSv1 SSRF, IAM role-chain abuse, Lambda over-privilege, AKS pod-identity. AI inventories the control plane; humans chain IAM.
Source code audit
AI surfaces pattern matches across the codebase, including AI-generated-code drift. Humans audit invariants and sign off on severity.
Red team assessment
AI handles open-source recon, lookalike domains, and pattern lookup. Humans drive stealth, detection bypass, and the objective chain.
Smart contract audit
AI mines invariant-violation patterns across Solidity, Rust, and Move. Humans write the working PoC on a forked mainnet.
Common procurement questions
What buyers askwhen they evaluate AI pentests.
Two ways SL7 runs AI in offensive security: this page covers the human-led, AI-augmented pentest engagement. For continuous, fully autonomous testing, see BugDazz Autonomous, a separate product line.
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Sample comparison report
AI-only vs. human-led, side by side.
A redactable PDF showing the same target run two ways: AI-only baseline vs. SL7 human-led AI-augmented engagement. Side-by-side findings, named bug classes, false-positive rates, time-to-PoC.