AI pentesting has emerged to help security teams deal with today’s scale challenges and allow them to do more testing with high-quality results. But as an emerging technology, there is no consensus yet on what “AI pentesting” actually entails. For instance, AI pentesting could refer to:
There are also a lot of vendor claims and promises about powerful AI capabilities and results. How do you make sense of it all and make smart decisions when there’s limited information and a fair amount of “AI washing”? To streamline your evaluation process, we share a few red flags to look out for and question early in your evaluation process below.
For more detailed evaluation guidance, download our new buyer’s guide: What to Look for In AI Pentesting. To get a quick look at the types of AI pentesting solutions and questions to ask vendors, see our new decision framework.
A vendor may claim their solution is “autonomous,” but how autonomous is it really? Many solutions require your team to be involved at some point, or several points, between the identification and reporting of a finding. Ask vendors about the level of human involvement, and request a demo of the system working from test kick-off to report generation.
“Zero false positives” is a bold claim. Ask for details on how they are reducing false positives. Are they validating findings with proof of exploits? Can you upload source code or SAST findings to improve the accuracy of results?
Can you trial the solution in your own system, or only in the vendor’s exclusive environment? Beware of restrictive trials that don’t take place in real-world scenarios.
If the vendor touts “continuous” testing, but also wants to schedule a monthly or weekly test, that’s a dubious claim. For teams deploying code to production frequently, a monthly or weekly test may not be sufficient. Ask the vendor to clarify “continuous,” who can trigger tests, if you can access the solution via API, if you can test incrementally, and the typical window between code deployment and testing.
Investigate any claims of covering huge numbers of vulnerabilities, like “thousands of vulnerability classes,” or a lack of details on coverage, like “the OWASP Top 10.” Ask whether it can unearth net-new vulns. Could it find a zero day, or is it just looking for existing patterns? Can it chain multiple findings to identify business logic flaws like IDOR?
How transparent are the findings? Ask to see a sample findings report from a real customer. Make sure you get enough detail that you could reproduce the findings.
Make sure the vendor has clear, solid answers about data governance. Ask about what data is retained (requests/responses, creds, tokens, findings), and how it’s retained. Also ask whether customer data used for training (opt-in/opt-out).
If there’s no talk about how the solution keeps AI agents from affecting production systems, dig deeper. Make sure they have detailed answers on guardrails and how the scope of testing is controlled.
Investigate the solution’s architecture. Tools built on a single-agent architecture have no or limited memory management and will struggle to operate with complex applications (50+ endpoints). A single agent could never conduct the volume of creative attacks needed to explore a system – both because of time constraints, and because, over time, it would accumulate and learn from all the wrong assumptions and misinterpreted responses and become ineffective.
Was this solution AI-based from the start? Or is it a long-existing product that now has AI tacked on? Ask about or investigate the history of the solution and the AI expertise of the company.
Penetration testing, or pentesting, is the gold standard for identifying and verifying risk in your applications and systems. Beyond just surfacing vulnerabilities, this powerful offensive security tactic unearths verified exploit pathways by exploring how an attacker would actually move through your environment. The problem is that the attack surface is changing, and traditional manual pentesting is fast becoming inadequate and ineffective due to limitations in velocity, coverage, economics, and quality.
“AI pentesting” doesn’t mean the same to every vendor. Most solutions fall into one of three categories. Ensure you understand the human/machine balance in the solution you are evaluating.
| Approach | Who drives the test | What AI does | Typical limitations |
|---|---|---|---|
| AI-assisted | Human | Helps with tasks | Still human-speed |
| Hybrid | Human orchestrates | AI runs phases | Context switching |
| Autonomous | AI agent | End-to-end attack exploration | Needs guardrails |
If you’re looking to augment and accelerate your offensive security with an AI-based pentesting solution, the best AI pentest tool depends on your needs and environment. In AI penetration testing, what features matter is dependent on your goals and a few basic safety and accuracy concerns. Here is a list of a few key things to add to your AI pentesting tools selection criteria.
To see what autonomous AI pentesting green flags look like end-to-end, from discovery to validated findings, request a demo of XBOW.
For more detailed evaluation guidance, download our new buyer’s guide: What to Look for In AI Pentesting. To get a quick look at the types of AI pentesting solutions and questions to ask vendors, see our new decision framework.
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