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Patrowl Agentic AI connects your external attack surface, threat intelligence and previous evidence to guide the next action.
This AI-powered penetration testing approach keeps context before execution.
Context before action: connect assets, technologies, vulnerabilities and previous evidence
Certified expert escalation: OSCP / OSWE-certified pentesters take over complex or sensitive investigations
Controlled by design: AI guides the next action while offensive execution stays supervised
Traceable evidence: keep context, decisions, findings and retests connected
More than 100 clients, including CAC 40 companies
A solution built by pentesters and cybersecurity auditors
Context, adaptation and controlled escalation
Connect signals that belong to the same scenario.Agentic AI brings together asset data, technologies, vulnerability intelligence, exploit information and test history from your web application, APIs and other exposed assets.
Adapt when a fixed rule is not enough.If a known check fits the situation, the case can move directly to automated execution.
If a known check fits the situation, the case can move directly to automated execution.
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assets, vulnerabilities and threat intelligence in one investigation
new CVEs against the technologies you actually expose
whether to analyze, automate or escalate the next step
how evidence changes across a complex attack path
privilege escalation, business logic or sensitive data scenarios to human testers
AI context with web apps, CI CD pipelines, your existing security tool stack and red team workflows
1. Observe
Collect the context around the asset.
Patrowl brings the relevant security data into one investigation path.
exposed asset and detected technologies
current vulnerabilities and threat intelligence
previous findings and retest history
Result: the agent starts with context, not an isolated alert.
2. Correlate
Connect signals that belong together.
The agent looks for relationships between exposure, vulnerabilities and previous evidence.
link a CVE to the technology in use
connect several findings to the same asset
reuse earlier test evidence when it changes the scenario
Result: related signals become one security question.
3. Decide
Choose the next useful action.
The agent uses the available evidence to guide what should happen next.
request more context when evidence is incomplete
route a known scenario to automated testing
flag a complex case for expert review
Result: the next step follows the evidence instead of a fixed sequence.
4. Execute or Escalate
Send the action to the right execution path.
Patrowl separates analysis from offensive execution.
run repeatable controls through the testing engine
keep sensitive actions under expert control
move business logic or complex attack paths to a pentester
Result: automation handles repeatable work while experts keep control of high-judgment cases.
The agent uses these layers to guide the next action. It does not replace the testing engine, your security tool stack or human expertise.
| Comparison |
Rules-based
automation |
Agentic AI
Penetration Testing |
|---|---|---|
|
Best for
|
Known and repeatable checks
|
Context-dependent security decisions
|
|
Input
|
Defined rule or control
|
Assets, findings, threat data and test history
|
|
Decision path
|
Predefined decision path
|
Adapts to available evidence
|
|
Execution
|
Runs the known test
|
Guides automation or expert escalation
|
|
Human role
|
Experts handle exceptions
|
Experts handle complex or sensitive cases
|
|
Main value
|
Scale and consistency
|
Context and adaptive routing
|
Local File Inclusion on an Internet-Facing AssetPatrowl detects a High-severity Local File Inclusion requiring no authentication.
Agentic AI Penetration Testing: safe and controlled. Sensitive tests are expert-supervised and run via guarded capabilities (known IPs, rate-limited, no DoS/DDoS), grounded in a CERT/CTI context and recognized standards (PTES, OWASP, NIST, ANSSI).
Controlled offensive execution
The agent can recommend a next action, but offensive security tests still run through Patrowl's controlled capabilities.
Production safeguards
Patrowl uses known testing IPs, controlled request rates and configurable testing windows. It excludes load tests, DoS and DDoS.
Trusted security context
Patrowl's CERT / CTI team follows sources such as CERT-FR, ENISA / EUVD, CISA KEV and NVD, plus vendor advisories and security research. This context can run continuouslyas new threat data appears.
Recognized references
The wider testing process uses PTES, OWASP Top 10, OWASP API Security Top 10, SANS Top 25, NIST SP 800-70 and relevant ANSSI guidance.
I need to…
reduce noise and false positive signals between alerts and action
understand why the next test matters
keep AI-assisted decisions traceable
What Patrowl brings: context, evidence and controlled escalation that improve the security posture of security teams.
I need to…
connect threat intelligence with exposed technologies
understand which signal deserves action
keep investigation context when a case escalates
What Patrowl brings: one path from threat signal to validation or expert review.
I need to…
prioritize with more than a severity score
connect asset context with exploit data
route the right issue to remediation
What Patrowl brings: context-aware analysis before the next security action.
I need to…
spend less time on repetitive correlation
receive better context before manual investigation
keep control of sensitive exploitation
What Patrowl brings: AI-assisted preparation without removing human judgment.
I need to…
understand how a change affects an exposed app
send known checks to automation or CI CD pipelines
keep complex application behavior under expert review
What Patrowl brings: adaptive routing between analysis, automation and experts.
I need to…
keep a trace of how findings were analyzed
connect evidence to remediation and retesting
support audit reviews with documented decisions
What Patrowl brings: traceable context and technical evidence throughout the workflow.
Use AI for context, automation for repeatable execution and experts for deeper investigation.
| Title | Asset | Severity | Status | |
|---|---|---|---|---|
| Weak ciphersuite on port 443/tcp | moore.patrowl.io |
Medium | New | |
| Weak protocol on port 8443/tcp | howard-dennis.p… |
Medium | New | |
| Other dangerous service on port 80/tcp | lee-franklin.patr… |
Info | New | |
| Database on port 3306/tcp | elliott.henderson… |
Critical | New | |
| SEG implemented with None | dalton.patrowl.io |
Low | New |
It helps when the next action depends on context. The agent connects asset data, vulnerability information and previous evidence before guiding the next step.
No. Patrowl separates AI guidance from offensive execution. Unlike fully autonomous penetration testing, repeatable tests run through controlled engines, while complex or sensitive actions can move to human experts.
Standard automation works best when the test is already known. AI pentesting adds value when several signals or attack vectors must be connected, or when one result changes what should happen next.
No. Human testers remain important for business logic, complex attack paths, exploit adaptation and sensitive scenarios.
No. Traditional pentesting remains useful for deep human investigation. Agentic AI helps with context, routing and repeatable analysis around that work.
Where an AI-enabled web app exposes relevant attack surfaces, prompt injections can become part of a scoped security assessment. Complex cases should still stay under expert control.
Patrowl keeps the analysis connected to the affected asset, technical evidence, previous test results and the final validation path. The process validates findings before they become actions and keeps validated exploitation separate from AI-only analysis.
Yes. It can add context before automation, expert review, remediation or retesting without replacing the wider security workflow.
